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--- title: Identification of Candidate Genes and Functional Pathways Associated with Body Size Traits in Chinese Holstein Cattle Based on GWAS Analysis authors: - Ismail Mohamed Abdalla - Jiang Hui - Mudasir Nazar - Abdelaziz Adam Idriss Arbab - Tianle Xu - Shaima Mohamed Nasr Abdu - Yongjiang Mao - Zhangping Yang - Xubin Lu journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044097 doi: 10.3390/ani13060992 license: CC BY 4.0 --- # Identification of Candidate Genes and Functional Pathways Associated with Body Size Traits in Chinese Holstein Cattle Based on GWAS Analysis ## Abstract ### Simple Summary Body size is a significant economic trait for dairy cows due to its significant influence on the production, health, selection, and environmental adaptation of dairy cattle. However, measurements of body size are widely used to predict body weight. Managers of dairy farms also use them to assess the development and growth of the animals during the rearing process. Therefore, understanding the genetic basis of inter-individual variation of body size might accelerate future efforts aimed at dairy cattle improvement. Our genome-wide association study identified several genes and functional pathways associated with body size traits in Chinese Holstein cattle. Results of this study might serve as a foundation for genetic improvement programs for dairy cattle that are based on molecular genetics. ### Abstract Body size is one of the most economically important traits of dairy cattle, as it is significantly associated with cow longevity, production, health, fertility, and environmental adaptation. The identification and application of genetic variants using a novel genetic approach, such as genome-wide association studies (GWASs), may give more insights into the genetic architecture of complex traits. The identification of genes, single nucleotide polymorphisms (SNPs), and pathways associated with the body size traits may offer a contribution to genomic selection and long-term planning for selection in dairy cows. In this study, we performed GWAS analysis to identify the genetic markers and genes associated with four body size traits (body height, body depth, chest width, and angularity) in 1000 Chinese Holstein cows. We performed SNPs genotyping in 1000 individuals, based on the GeneSeek Genomic Profiler Bovine 100 K. In total, we identified 11 significant SNPs in association with body size traits at the threshold of Bonferroni correction (5.90 × 10−7) using the fixed and random model circulating probability unification (FarmCPU) model. Several genes within 200 kb distances (upstream or downstream) of the significant SNPs were identified as candidate genes, including MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L. Moreover, genes within 200 kb of the identified SNPs were significantly enriched (p ≤ 0.05) in 25 Gene Ontology terms and five Kyoto Encyclopedia of Genes and Genomes pathways. We anticipate that these results provide a foundation for understanding the genetic architecture of body size traits. They will also contribute to breeding programs and genomic selection work on Chinese Holstein cattle. ## 1. Introduction Body size in humans, cattle, and other domestic animals has been widely investigated [1,2,3,4]. Body size traits were commonly used as the primary breeding selection criterion to monitor cattle growth and to evaluate the selection response [5,6,7]. The profitability of dairy production is largely determined by cows’ ability to produce a large amount of milk. This is in addition to other factors, such as health, fertility, and feed efficiency, as well as management practices. Body size measurements have been used as predictors of body weight in dairy cattle [8]. Hip height and heart girth are the essential traits widely accepted as the most satisfactory method to predict body weight because they are more easily obtained than body weight [9]. Over the last years, heart girth and hip height measurements have been routinely collected from birth to first calving in Chinese Holstein cattle. Heritability estimates for these traits are moderate to high (0.33–0.40) [5]. There have been reports of positive genetic correlations of body weight with milk and protein yield [10]. Furthermore, body weight has an impact on dairy cow health and fertility. For example, calving ease and calf survival are moderately correlated with cows’ body weight and calves’ birth weight [11]. The interval between calving and first service was shorter in heavier cows, but conception rates decreased as body weight increased [12]. In contrast, the improved non-return rates after 56 and 90 days was associated with increasing the heifers’ body weight; body depth was genetically correlated with many other economic traits, such as calving interval (0.35), the days from calving to first insemination (0.79), and gestation period (0.34) [10]. Similarly, gestation period has been shown to have a genetic correlation with body height (0.49) [13]. Interestingly, numerous studies have demonstrated that cows with higher body height have shorter longevity [8,14]. Furthermore, good depth of heart girth in cattle indicates good forage convertibility and feet and leg conformation [15]. On the other hand, studying the genetic basis of body size variation among individuals might help understand the mechanism of environmental adaptation of cattle [7]. For over a decade, GWAS has become an effective approach for detecting genetic markers that are associated with multiple economic traits in animal production. Some GWASs on body size have been carried out across different populations in order to understand the genetic mechanisms of growth traits, such as Chinese Holstein cattle [5], Simmental beef cattle in China [6], and Brahman and Yunling cattle [7]. In addition, various SNPs, genes, and QTLs were related to body measurement, body weight, and conformation traits in different breeds [16,17,18,19]. Therefore, our study aims to identify SNPs associated with body size traits (body height, body depth, chest width, and angularity) using the GWAS approach. Furthermore, we conducted gene ontology and KEGG pathway analysis for a better understanding of the biological functions of the genes within the significant SNPs. The newly identified genetic markers and candidate genes may contribute to genomic selection and the genetic improvement of body size in Chinese Holstein dairy cows. ## 2.1. Ethical Statement The entire procedures involving animal care, collecting samples of hair follicles, and measurement of phenotypic traits were performed in strict compliance with the guidelines provided by the China Council on Animal Care and the Agricultural Ministry, China. This research was also accepted by the Institutional Animal Care and Use Committee of School of the Yangzhou University Animal Experiments Ethics Committee (License Number: SYXK (Su) IACUC 2020-0910), Yangzhou University. During the collection of samples and data, no animals were uncomfortable or malnourished. ## 2.2. Phenotypic Data and DNA Samples Collection Four farms located in the province Jiangsu, China, were used to select 1000 Chinese Holstein cows for the experiment. Body height (BH), body depth (BD), chest width (CW), and angularity (ANG) were measured by three trained technicians. They were recorded on a point scale from 1 to 9 in accordance with the China National Standard Code of the practice of type classification in Chinese Holstein (GB/T 35568-2017). In addition, 50 hair follicle samples were obtained from each cow for genotypic analysis. ## 2.3. Phenotypic and Genetic Parameters The computer-based software IBM-SPSS, (Version 25.0. Armonk, NY, USA: IBM Corp.), was used to estimate the pairwise Pearson correlation coefficients and to determine the descriptive statistic of phenotypic traits (BH, BD, CW, and ANG). Genetic correlations and heritability among the four traits of body size were estimated using the DMU software (derivative-free approach to multivariate analysis) [20] with the animal model, as below. Yijklm=u+HerdI+Yearj+Seasonk+Parityl+am+eijklm where Yijklm is the phenotype in the jth year, kth season, and lth parity of the mth individual from ith herd; u is overall mean of the population, Herdi is the herd effect according to a cow’s origin from one of the four herds; Yearj is the jth year effect, Seasonk is the kth season effect, and Parityl is the effect of lth parity; a is the additive effect of mth individual, and e is the residual in the jth year, kth season, and lth parity of the mth individual from ith herd. All effects were treated as fixed except the additive effect. There are at least three generations of pedigree data (age) available for the cows (2009–2020), their parities range from 1 to 4, and four seasons during the measurement time were June–August, September–November, December–February, and March–May [21,22]. ## 2.4. Genotyping and Quality Control Hair follicle samples have been used to extract genomic DNA. DNA extraction and genotyping was carried out using the GeneSeek Genomic Profiler Bovine 100 K SNP Chip at Neogen Biotechnology (Shanghai, China) Co., Ltd. ((http://www.neogenchina.com.cn), accessed on 28 June 2020), based on ARC-UCD1.2/bosTau9 as the genome reference. Quality control was performed using Plink 1.90 software [23] to exclude SNPs: [1] the SNPs with a call rate less than $90\%$; [2] all SNPs with a MAF (minor allele frequency) less than 0.05; [3] those that violated the HWE (Hardy Weinberg equilibrium) value ($p \leq 1.0$ × 10−6). A total of 984 cows and 84,406 SNPs were kept for subsequent analyses following quality control. ## 2.5. Population Stratification GWAS can be confounded by population stratification. Therefore, if not properly corrected population stratification can produce spurious associations. Our previous work [22] revealed that the first two principal components (PCAs) had been fitted as covariate variables in the association analysis to eliminate the influence of population stratification. ## 2.6. Genome-Wide Association Studies The GWAS was performed using the fixed and random model circulation probability unification (Farm-CPU) model [24]. Iteratively, The FarmCPU method uses a fixed effect model (FEM) and a random effect model (REM). Using Plink software, v1.90 [23], the SNP genotypes coded for the association analyses were converted to 0, 1, and 2. The SNPs that passed the significant threshold in the FEM were detected as pseudo quantitative trait nucleotides (QTNs). The pseudo QTNs were subsequently verified using the REM, where the kinships were built using alternative sets of pseudo QTNs. Iterative calculations were carried out through the FEM and the REM until no updated pseudo QTNs exceeded the significance threshold. False positive correlations are mainly caused by population stratification [25]. Therefore, the FEM tests SNPs one at a time. Two of the highest PCAs, accounting for $21\%$ of the population stratification in the FEM, were considered as covariates in order to account for the other genetic variations, except for the pseudo QTNs [26]; the FEM can be given as:[1]y= Xbx+Mtbt + Sjdi+e where y is the vector of the adjusted phenotypic values for BH, BD, CW, or ANG traits; bX is the corresponding effect of the first two PCAs, and X is the corresponding coefficient matrix; bt is the fixed effect of the tth pseudo QTN, which was detected by the FEM and optimized by the REM in each cycle, and Mt is the corresponding genotype matrix; Sj is the genotype of the jth marker, which was converted to 0, 1, or 2, and dj is the effect of the jth marker; and e is the random residual of the model. Markers have their own p-value after substitution. The p-values and the associated marker map are used to update the selection of pseudo QTNs using the SUPER algorithm (Settlement of MLM Under Progressively Exclusive Relationship) [27] in a REM, as follows:[2]y= u+e where y is the vector of the adjusted phenotypic values of BH, BD, CW, or ANG; u is the vector of total genetic effects of individuals and is assumed to satisfy $u = 0$, Kσu2, in which K is the kinship matrix constructed by the QTNs obtained from the FEM, and σu2 is the unknown genetic variance; and e is the random residual of the model. The false positive associations (type 1 error) rate was set at $5\%$. The p-value (5.9 × 10−7) of the significance for SNPs was determined based on the Bonferroni correction method (0.05/N) [28], where N is total SNPs number left after quality control. ## 2.7. Gene Identification and Functional Analysis The UCSC genome browser via an Asian server for cow assembly (April 2018) (UCSC Genome Browser Gateway) (accessed on 29 May 2022) and the full National Center for Biotechnology Information gene (NCBI) database (National Center for Biotechnology Information (nih.gov) (accessed on 29 May 2022) were used to identify genomic regions and candidate genes. The linkage disequilibrium (LD) analysis performed in [22] identified genes within the 200 kb region of the significant SNPs as candidate genes. We submitted the GWAS candidate genes to the Database for Annotation, Visualization, and Integrated Discovery (DAVID) [29] for the Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analysis. For functional and pathway analysis, a significant p value was set at p ≤ 0.05. The online Search Tool for the Retrieval of Interacting Genes (STRING) database, v11.0 [30], was used to investigate protein–protein interactions (PPI) between genes, and the resulting PPI network was visualized using Cytoscape software (v3.8.2). ## 3.1. Descriptive Statistics and Genetic Parameters Estimation of Body Size Traits The descriptive statistics of phenotypic measurements of body size traits (BH, BD, CW, ANG), including mean, maximum, and minimum values, are shown in Table 1, where the mean of measurements of these traits ranges between 6.18 and 8.2 scale scores. Genetic and phenotypic correlations between body size traits are given in Table 2. The genetic correlations between these traits revealed highly positive correlations between BH, BD, and CW. At the same time, ANG had a moderate positive correlation with BH and a low positive correlation with other traits. In contrast, the pairwise estimation of phenotypic correlation was high between BD and CW, while BH had low phenotypic correlations with BD and CW. Heritability, as well as genetic and phenotypic correlations between body size traits, are presented in Table 2. The heritability estimation results for BH, BD, CW, and ANG were 0.48, 0.1, 0.17, and 0.19, respectively, as shown in Table 2. ## 3.2. Candidate Genes Association The GWAS analyses were performed based on the Farm-CPU model. Quantile–quantile plots suggested no inflation or clear systematic bias in the study (Figure 1). Because the model used was well controlled for the population stratification, indicating that the inflation factor (λ) at each trait is near 1 (0.9 < 1.01), a slight deviation in the upper right tail from the diagonal line indicates the significant SNPs associated with the target traits. Manhattan plots (Figure 2) are used to illustrate the significance level of SNPs of GWAS results based on chromosome location. Here, each dot corresponds to a single SNP. The x-axis represents genomic position, while the y-axis displays the negative of the log p value. The results of the GWAS analyses identified 11 significant SNPs related to body size conformation traits in Chinese Holstein cattle, which statistically passed the threshold (s 5.9 × 10−7) after Bonferroni correction, as shown in Table 3. Five SNPs were associated with BH trait, including the SNP rs134484400 on Chr. 11, the SNP rs110462304 on Chr.1, which is positioned within myosin heavy chain 15 gene (MYH15), the SNP rs109930583 on Chr. 6 is located within LOC112447047 gene and chromosome 6 C4orf17 homolog gene (C6H4orf17), the SNP rs109824125 on Chr. 14 is close (100 kb) to KH RNA binding domain containing gene (KHDRBS3), and the SNP rs42188649 on Chr. 29 is located within aryl hydrocarbon receptor interacting protein gene (AIP). Two SNPs were associated with BD trait; the SNP rs133735152 on Chr. 24 is located near (100 kb) to the DCC netrin 1 receptor gene (DCC), and the SNP rs43286429 on Chr. 1 is located close (100 kb) to the LOC112447004 gene. Moreover, three SNPs were associated with CW; the SNP rs110355602 on Chr. 10 is positioned close (200 kb) to sulfide quinone oxidoreductase gene (SQOR), the SNP rs43615333 on Chr. 10 is located within ubiquitin associated protein 1 like gene (UBAP1L), and SNP rs42095998 on Chr. 26. is positioned within vesicle transport through interaction with t-SNAREs 1A gene (VTI1A). In addition, one SNP was related to the ANG trait; the SNP rs135918869 on Chr. 5 is located close (100 kb) to the coiled-coil domain, containing 59 gene (CCDC59) (Table 3). ## 3.3. Gene-Set Enrichment and Analysis To gain a deeper understanding of the biological functions shared by the trait-associated genes, we analyzed 105 genes (Table S1) within the region of 200 kb (up/downstream) of the significant SNPs for the four traits of body size. We then conducted KEGG and GO enrichment analysis. Gene ontology enrichment analysis (Table S2 and Figure 3) revealed six biological process terms (GO:0004745:retinol dehydrogenase activity, GO:0016491:oxidoreductase activity, GO:0004024:alcohol dehydrogenase activity, zinc-dependent, GO:0004028:3-chloroallyl aldehyde dehydrogenase activity, GO:0051015:actin filament binding, and GO:0016620:oxidoreductase activity, acting on the aldehyde or oxo group of donors, NAD, or NADP as acceptor), two cellular component terms (GO:0005829:cytosol and GO:0005770~late endosome), and 12 molecular function terms (GO:0006069:ethanol oxidation, GO:0042573:retinoic acid metabolic process, GO:0042572:retinol metabolic process, GO:0006068:ethanol catabolic process, GO:0006081:cellular aldehyde metabolic process, GO:0031529:ruffle organization, GO:0045471:response to ethanol, GO:0034622:cellular macromolecular complex assembly, GO:0006886:intracellular protein transport, GO:0007015:actin filament organization, GO:0042104:positive regulation of activated T cell proliferation, and GO:0006120:mitochondrial electron transport, NADH to ubiquinone). Furthermore, the KEGG pathways (Table 4), which were significantly over-represented (p ≤ 0.05) by the set of genes, may have been associated with body size involving 10 pathways (bta00980:Metabolism of xenobiotics by cytochrome P450, bta05204:Chemical carcinogenesis, bta00982:Drug metabolism-cytochrome P450, bta00350:Tyrosine metabolism, bta00010:Glycolysis/Gluconeogenesis, bta00071:Fatty acid degradation, bta00340:Histidine metabolism, bta00410:beta-Alanine metabolism, bta01100:Metabolic pathways, and bta00830:Retinol metabolism). A PPI analysis was conducted using the STRING database for all the genes previously analyzed as part of the functional analysis. Figure 4 shows a number of significant interactions between genes (77 nodes are connected by 217 edges). ## 4. Discussion Body size plays a crucial role in dairy cattle’s production, health, selection, and environmental adaptation. However, body size measurements are widely used to predict body weight. In addition, they are commonly used by dairy farmers to monitor growth and track the development of their animals’ developmental progress during the rearing period. Therefore, mapping and detection of significant SNPs and candidate genes influencing body size traits have high economic benefits for dairy cattle breeding. In the current work, we identified 11 SNPs and their associated candidate genes (Table 3), which significantly correlated with the four body size traits (BH, BD, CW, and ANG); five of these significant SNPs were related to the BH trait. BH is one of the quantitative traits that has long fascinated geneticists and is usually highly heritable; understanding the molecular basis of inter-individual variation in this trait might provide novel insights into the mechanisms controlling individual growth [31]. Among these SNPs, the rs110462304 SNP on Chr. 1 has been previously reported in a QTL region associated with carcass trait performance [32]. In our study, this SNP was located within the MYH15 gene, which belongs to the myosin heavy chain gene family. Myosins are superfamily genes of eukaryotic motor proteins that bind actin and use ATP hydrolysis energy to contribute significantly to a wide range of biological processes, such as muscle contraction, cytokinesis, cell motility and contractility, and intracellular trafficking [33]. A previous meta-analysis of GWAS revealed that the MYH15 gene is listed as a plausible candidate gene for detecting pleiotropic polymorphisms for body height, reproduction, and fatness in beef cattle [34], as well as pleiotropic effects on body composition in sheep [35]. Over the last few years, the MYH15 gene has been reported in several studies in chickens, such as RNA sequencing results, which validated that the MYH15 gene was associated with muscle structures [36], as well as comparative transcriptome analysis reporting that this gene is one of the genes involved in breast muscle growth, regulation, and development in three breeds of chicken [37]. The MYH15 gene was also related to skeletal muscle myoblast proliferation and differentiation among layer and broiler chickens [38]. The SNP rs109824125 is on Chr. 14 is significantly associated with BH, which has been previously found in some QTLs related to fat milk percentage and milk solids percentage for Thai dairy cattle [39]. This SNP is located near to KHDRBS3 gene, which is a member of the signal transduction and the activator of RNA (STAR) family proteins. It has been reported that this gene is associated with the 305-day milk yield trait in two cattle breeds: Girolando crossbreed cattle [40] and primiparous Holstein cows [41]. Gene network interactions analysis, derived from GWAS-based results, revealed this gene is related to meat quality and growth traits of Brazilian Nelore beef cattle [42], as well as carcass weight trait in Hanwoo cattle [43]. Deng et al. [ 44] reported that this gene is one of the suggested candidate genes that might play a role in milk production traits in buffaloes. The rs42188649 was another significant SNP related to BH trait, which is located within AIP gene; this gene has been found to be evolutionarily conserved among species and is widely expressed throughout the organism [45,46]. Recent studies provided insight into the body size variation in cetaceans, indicating that this gene was related to tall stature and overgrowth [47], and a genome-wide scan for selection signatures revealed that this gene was related to cardiac structure and function in Atlantic killifish [48]. Another multi-breed GWASs on dual-purpose bovine behavior traits identified this gene as related to rumination [49]. In this study, SNP rs133735152 was significantly associated with the BD trait, which is close to the DCC gene. Several studies, including that of Sunirmal Sheet et al. [ 50], identified this gene as a candidate gene associated with obesity-related traits in canines based on GWAS analysis. In addition, genome-wide analysis of copy number variations identified the DCC gene to be probably associated with morphological, milk, meat, healthy, and reproductive traits in three indigenous Iranian river buffaloes [51]. The most significantly associated SNP in the present analysis ($$p \leq 9.45$$ × 10−11), rs110355602, was located on Chr. 10, which is associated with BD trait; the rs110355602 SNP is located close to the SQOR gene. The SQOR is a mitochondrial inner membrane-coding gene in humans that plays an essential role in catalyzing and metabolizing hydrogen sulfide (H2S.) [52]. Similarly, the SQOR gene has been also associated with the growth and muscle development traits in Chinese Simmental beef cattle [53]. On the same chromosome (Chr. 10), we found another SNP (rs43615333), which is located within the UBAP1L gene; this gene was also significantly associated with BD trait in our results. Various metabolic activities occur during an animal’s body growth and development, so it is reasonable that the metabolic pathways are one of the significantly enriched KEGG pathways in this study. In the present work, many GO terms (Table S2 and Figure 3) and KEGG pathways (Table 4) were significantly enriched (p ≤ 0.05). Among them, five pathways (metabolic pathways, fatty acid degradation, drug metabolism-cytochrome P450, metabolism of xenobiotics by cytochrome P450, and chemical carcinogenesis) were reported to be associated with residual feed intake in Australian Angus cattle [54]. Moreover, retinol metabolism was significantly over-represented in biological pathways involved in carcass trait performance in Holstein-Friesian cattle [32]. In contrast, histidine metabolism was significantly enriched in pathways related to mammary teat-shape conformation traits in Chinese Holstein cattle [55]. In addition, tyrosine metabolism and beta-alanine metabolism were identified as significant KEGG pathways of the differentially expressed genes in the Large White pigs [56]. Besides the ALDH3B1 gene, three members of ADHs genes (ADH1C, ADH7, and ADH6 gene) were displayed in the top six significantly enriched GO terms (zinc-dependent, alcohol dehydrogenase activity, ethanol oxidation, retinol dehydrogenase activity, retinoic acid metabolic process, retinol metabolic process, and ethanol catabolic process). Moreover, these genes are also revealed in most KEGG pathways in the current study. Zhao et al. [ 57] reported that ADH6 in three enriched KEEG pathways (bta00830:Retinol metabolism, bta00982:Drug metabolism-cytochrome P450, and bta00980:Metabolism of xenobiotics by cytochrome P450) of the functional analysis of differentially expressed genes in the down and up group are based on transcriptome analysis of ruminal epithelia related to the gradual high fermentable dietary transition in beef cattle. The ADH1C gene has an interactive effect with vitamin A supplementation level on intramuscular fat content in beef steers [58], while Ghafouri et al. [ 59] reported the ADH1C gene to be downregulated in the lipolysis process in poultry based on the integration of RNA-Seq and microarray data approach. The ADH1C gene also has been reported in another study using text mining technique to identify genes associated with meat quality and carcass traits in Hanwoo cattle [60]. In their study, Wang et al. [ 61] found that this gene was linked with hot carcass weight, carcass marbling, lean meat yield, and rib eye area in beef cattle. The authors of this study showed that a direct association and biological function of this gene are related to small molecule biochemistry and lipid metabolism. Interestingly, this gene also has been involved in lipid metabolism, and small molecule biochemistry biological functions for daily dry matter intake in a study integrated plasma metabolites and imputed whole genome sequence variants in beef cattle [62]. This gene was also differentially expressed in beef steers’ livers for average daily gain and daily dry matter intake in the Charolais breed [63]. Rafael Medeiros de Oliveira et al. also identified the ALDH3B1 gene in a GWAS work for backfat thickness in Nellore cattle [64]. There is some interest in the fact that the most significant genes in this study were related to growth and body development traits of beef cattle in several previous studies, which might be attributed to the importance of body size as one of the main breeding selection criteria in beef cattle breeding. ## 5. Conclusions In summary, we identified 11 significant SNPs associated with four body size traits (BH, BD, CW, and ANG) using FarmCPU-based GWAS in Chinese Holstein cattle. This study revealed the six most promising candidate genes (MYH15, KHDRBS3, AIP, DCC, SQOR, and UBAP1L); in addition, five KEGG pathways and 25 GO terms were significantly enriched. 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--- title: 'Medication errors in relation to direct-acting oral anticoagulants: a qualitative study of pharmacists’ views and experiences' authors: - Abdulrhman Al Rowily - Nouf Aloudah - Zahraa Jalal - Mohammed Abutaleb - Mohamed Baraka - Vibhu Paudyal journal: International Journal of Clinical Pharmacy year: 2023 pmcid: PMC10044102 doi: 10.1007/s11096-023-01555-3 license: CC BY 4.0 --- # Medication errors in relation to direct-acting oral anticoagulants: a qualitative study of pharmacists’ views and experiences ## Abstract ### Background Despite their effectiveness and ease of use, medication errors have been reported to be highly prevalent with direct-acting oral anticoagulants (DOAC). ### Aim The aim of this study was to explore views and experiences of pharmacists on contributory factors and mitigation strategies around medication errors in relation to DOAC. ### Method This study used a qualitative design. Semi-structured interviews were conducted with hospital pharmacists in Saudi Arabia. The interview topic guide was developed based on previous literature and Reason's Accident Causation Model. All interviews were transcribed verbatim and MAXQDA Analytics Pro 2020 was used to thematically analyse the data (VERBI Software). ### Results Twenty-three participants representing a range of experiences participated. The analysis recognised three major themes: (a) enablers and barriers faced by pharmacists in promoting safe utilisation of DOAC, such as opportunities to conduct risk assessments and offer patient counselling (b) factors related to other healthcare professionals and patients, such as opportunities for effective collaborations and patient health literacy; and (c) effective strategies to promote DOAC safety such as empowering the role of pharmacists, patient education, opportunities for risk assessments, multidisciplinary working and enforcement of clinical guidelines and enhanced roles of pharmacists. ### Conclusion Pharmacists believed that enhanced education of healthcare professionals and patients, development and implementation of clinical guidelines, improvement of incident reporting systems, and multidisciplinary team working could be effective strategies to reduce DOAC-related errors. In addition, future research should utilise multifaceted interventions to reduce error prevalence. ### Supplementary Information The online version contains supplementary material available at 10.1007/s11096-023-01555-3. ## Impact statements Numerous factors identified contribute to DOAC safety, such as lack of healthcare professionals' knowledge and education, lack of monitoring, unavailability of antidotes, and underreporting of errors. Empowering pharmacists and patients are key enablers that could reduce DOAC-related medication errors. Gaps in education, training, collaboration and implementation of multidisciplinary approach when dealing with DOAC need to be addressed. ## Introduction Direct-acting oral anticoagulants (DOAC) are effective treatment options in nonvalvular atrial fibrillation (NVAF) and the prevention and treatment of thromboembolic conditions [1]. They offer advantages over vitamin K antagonists such as warfarin in several aspects, including the need for less intense monitoring and reduced probability of drug-drug and drug-food interactions [2, 3]. Despite their effectiveness and ease of use, medication errors with DOAC are common [4]. A recent systematic review and meta-analysis demonstrated that approximately $20\%$ of DOAC prescriptions have at least one error [5]. DOAC are classified as high-alert medication as per the institute for safe medication practices (ISMP), with the potential to cause severe bleeding episodes or even death when overdosed [6]. Pharmacists' roles in preventing and mitigating medication errors have been previously emphasised [5–9]. Understanding contributory factors associated with medication errors is essential to promote the safe utilisation of DOAC in clinical practice [5, 6, 8]. Reason's Accident Causation model offers a theoretical framework to investigate contributory factors and identify strategies for their mitigation [10]. Through awareness of contributory factors, areas of improvement for patient safety can be identified [11]. Reason's Accident Causation model distinguishes active failures, such as human mistakes and violations, from latent failures associated with system-related factors, such as lack of resources [10]. A previous study identified that lack of adequate knowledge and training of physicians and nurses about DOAC, lack of confidence in prescribing and administration and lack of access to clinical guidelines as key factors related to medication errors with DOAC [12]. However, pharmacists' perspectives on DOAC related errors and effective strategies to mitigate the contributory factors have not yet been investigated. ## Aim The aim of this study was to explore views and experiences of pharmacists on contributory factors and mitigation strategies around medication errors in relation to DOAC. ## Ethics approval Ethical approval was obtained from the University of Birmingham Research Ethics Committee (ERN_20-0551). In addition, ethical approvals from each of the participating hospitals were also obtained ($\frac{164}{2020}$; SP$\frac{20}{212}$/R; AFHER-IRB-2020-015). Informed consent was obtained prior to the study enrolment. ## Study design This study used a phenomenological qualitative study design. In-depth interviews were conducted with pharmacists for data collection. The findings were reported using the consolidated criteria for reporting qualitative research (COREQ) [14]. The COREQ checklist includes thirty-two items that enable researchers to appropriately report essential aspects of qualitative research teams, methodology, setting and context, results, analysis and interpretation [14]. ## Participants and setting Using a purposive sampling technique, pharmacist participants were recruited from tertiary hospitals in three different cities in Saudi Arabia: Riyadh, Jazan and Dhahran. For the hospitals to be eligible as recruitment sites, they needed to have cardiology and internal medicine departments with at least two consultant cardiologists (i.e. larger centres). Pharmacists who had experiences in anticoagulation pharmacy stewardship programs or experience in DOAC ordering, reviewing and dispensing were included. Pharmacists with two different rankings per the Saudi commission for health specialities pharmacist classification system were included. These included: senior level consultant pharmacists who had graduated with a PhD or postgraduate-year 2 (PGY2) specialised residency program with three years of practice experience after graduation; and junior-level clinical pharmacists who had an accredited degree and experience in clinical pharmacy (e.g. Master, residency or PhD) but were yet not able to meet the eligibility criteria to be consultants. ## Data collection Hospitals received an invitation e-mail to recruit research participants. A local collaborator invited all pharmacists in each hospital and outlined inclusion criteria. During pharmacy team meetings, collaborators also conveyed research objectives to potential participants. Those who replied to the invitations were given a participants' information sheet (PIS) and requested to sign consent forms. A topic guide was developed based on previous literature and Reason's Accident Causation Model [8, 11, 13, 14]. The majority of the interview questions [supplementary electronic material 1] focused on eliciting participants' knowledge and experience with DOAC concerns around DOAC-related errors, availability of clinical guidelines and other sources of information in their institutions, their views about healthcare professionals' roles in improving DOAC safety, and their views around contributory factors to errors and mitigation strategies. The questions for the interview schedule were drafted by the first author (AA) and further refined by the research team based on advice from research collaborators at study locations. In addition, seven other pharmacists examined the topic guide to improve its clarity. The interviews were conducted in English (since English is commonly used in hospitals in Saudi Arabia and because many of the pharmacists were English speakers), recorded, and transcribed verbatim by an expert transcriber. Prior to the interviews, the study researcher described the purpose of the study and offered an opportunity for the participants to ask any questions. Due to COVID-19 pandemic restrictions, semi-structured interviews were undertaken via Zoom videoconferencing platform. The study was conducted during September and November 2021. The primary researcher (AA) was supported by a second member of the research team (NA) as a note-taker during all interviews. ## Data analysis Thematic analysis of interviews was performed using MAXQDA Analytics Pro 2020. ( VERBI Software) [15]. Two authors (AA, NA) separately analysed each transcript, while a third author (VP) examined both versions for discrepancies. A discussion was had in the case of any disagreements to reach a consensus. As the semi-structured interviews progressed, transcripts and notes from each session were examined to establish initial codes and to detect emergent information. To increase rigour and credibility, each interview was concluded with a verbal summary developed by the interviewer. This summary was discussed, modified when necessary, and finally confirmed with the interviewees to ensure their agreement with the recorded interview content. The two researchers (AA and NA) met after each interview and discussed the interview data. During the interviews, memos (such as noting an interviewee's facial expressions or reluctance to answer specific questions) and journaling were recorded utilising MAXQDA memos. Interviews were conducted until data saturation was reached. Data saturation was achieved when redundant responses to interview questions were observed without any new information. ## Results Saturation of the data collection was achieved after 20 interviews, and three further interviews (total $$n = 23$$) were conducted for assurance. The median duration for the interviews was 32 min, ranging from 19 to 44 min. Characteristics of the participants are presented in Table 1. Reasons for non-response to the invitation were not recorded. Table 1Participants’ characteristics ($$n = 23$$)Participants' characteristicsPharmacists ($$n = 23$$)GenderFemale9Male14Training background years of experience < 525–101511–15516–201 > 200Years of experience with DOAC orders (in years)* < 5145–10211–15416–202 > 201Specialty or departmentCardiology2Internal Medicine14Unspecialised6Others1Current job titleConsultant pharmacists 12Clinical Pharmacists 11Others include any specialist for pharmacist: drug informationYears of experience with DOAC orders (in years): Any experience with dispensing, checking, order handling or monitoring DOAC Three main themes were identified from the data analysis: [1] enablers and barriers faced by pharmacists in promoting the safe utilisation of DOAC; [2] factors related to other healthcare professionals (HCPs) and the patients; [3] strategies to promote DOAC safety. Figure 1 represents an overview of the themes and subthemes. Fig. 1An overview of themes and subthemes HCPs: Healthcare professionals, DOAC: Direct oral anticoagulants ## Theme one: Enablers and barriers faced by pharmacists in promoting the safe utilisation of DOAC Pharmacists as a facilitator for DOAC safety Participants described pharmacists acting as a source of medication information. They highlighted providing recommendations to assist other HCPs in decision-making (switching from warfarin to DOACs, other elements of prescribing, and administration of DOAC), participating in bleeding risk assessment, and leading anticoagulation clinics amongst many diverse ranges of roles described (Fig. 1). In addition, clinical and drug information pharmacists support formulary decisions that impact the use of DOAC and their safety. “Mainly drug information pharmacist, so I dealt with most questions related to DOAC and was involved in formulary decisions related to DOAC. Those are my main two encounters, so questions received in the drug information centre and, during discussion for formulary addition” Participant 8, pharmacist (Riyadh) Participants further illustrated activities such as conducting appropriate risk assessments to help improve safe DOAC prescribing. “Again, you must conduct a reasonable risk assessment based on the patient's scenario, bleeding versus thrombosis risk assessment. I think you can't prescribe DOAC safely without this assessment, which is absent in many cases. I've seen they never do it.” Participant 2, pharmacist (Dammam) Many participants described the assessment of renal function among the roles of clinical pharmacists to support decisions around the continuation, adjustment, or discontinuation of DOAC.“One of our roles as clinical pharmacists is to usually get questions like renal dose adjustments, when can we minimise, the dose based on creatinine clearance and when to hold and when to resume and how to dose based on indication” Participant 8, pharmacist (Riyadh) Participants also described patient counselling as one of the crucial roles of pharmacists, especially during patients' discharge. In addition, they describe pharmacists' role in providing information on the proper use of DOAC and how to deal with missing doses.2.Barriers faced by pharmacists in promoting the safe utilisation of DOAC Pharmacists identified a lack of knowledge of dosing guidelines and other prescribing information related to DOAC as key barriers that might lead to errors while using these high-alert medications. “Honestly, I'm unfamiliar with all DOAC dosing (information), unfortunately, but, in some cases, I need to be more careful, and DOAC dosing in some cases are not mentioned in the guidelines” Participant 9, pharmacist (Riyadh) Many participants reported that effective medication error reporting, investigation, and follow-up systems were lacking. From some 'participants' point of view, factors such as lack of time, workload, being bored, or just careless attitude were common reasons for underreporting of DOAC-related safety issues. “Regarding documenting incidents through the occurrence variance reporting system in our hospital, to be honest, I don't do it. Why didn't I do that? because I don't have time, or sometimes I feel lazy to do this, to be honest.” Participant 4, pharmacist (Riyadh) Some pharmacists believed that, unlike warfarin, DOAC lack monitoring tools or criteria for dosing. Moreover, the availability of antidotes was another problem that hindered the safe use of DOAC.“I am feeling high responsibilities, but it is not like warfarin. Why? Because there are no monitoring criteria for the test, unlike “INR” for warfarin” Participant 1, pharmacist (Jazan) Lastly, participants discussed the impact of patient education on adherence to DOAC and medication errors. ## Theme two: Factors related to other healthcare professionals (HCPs) and related to the patients Factors associated with other HCPs While many described positive experiences of effective multidisciplinary working, some participants described that physicians often do not accept pharmacists' recommendations after medication order review.“… other things, the resistance from the physician side; when we contact him, he refuses to keep this medication as discontinued, so I feel under pressure if I will review that order, maybe the nurse will give it to the patient, or if I do not review it, maybe also the patient get harm if this medication not administered to him” Participant 3, pharmacist (Riyadh) Participants further identified that knowledge of local clinical protocols and guidelines was sometimes lacking, particularly among physicians. “Yeah, some of the physicians do not collaborate with pharmacists, especially upon catching dosing error …They should follow the local hospital protocol and guidelines, and not any other reference” Participant 7, pharmacist (Riyadh) Participants emphasised the importance of nurses’ roles in promoting medication safety. Education and training about identifying adverse events of these drugs were deemed necessary as nurses closely monitor the patients on DOAC.2.Factors associated with the patients Pharmacists discussed that DOAC-related errors could be serious or fatal if patients do not understand dosing information. In addition, there was a risk that patients continued administering two anticoagulants when switched from one to the other. “Sometimes the patient, when we shift him from one anticoagulant to another, he continues to use both.” Participant 2, pharmacist (Riyadh) Pharmacists identified that medical history should be clear to avoid prescribing DOAC to those who have comorbidities or contraindications to these drugs. “Wrong patient criteria; for example, the patient has advanced liver disease and was prescribed DOAC.” Participant 2, pharmacist (Riyadh) Many noted that patients often gain knowledge through their own experiences of use. “Yeah, of course, if the patient were more experienced with the medication, this would minimise the risk of error. if the patient has used DOAC for a long time and he/she knows what that medication used for is and what side effects may happen.” Participant 2, pharmacist (Riyadh) Pharmacists discussed the importance of effective communication with patients in minimising errors. However, they reported that it was difficult in some situations where pharmacists were not able to communicate effectively with patients. These included situations including patients’ mental health disorders, presence of cognition-impairing diseases in addition, to illiteracy and language barriers. Some also noted that there was often reluctance from some patients to discuss medication related issues with a pharmacist. ## Theme 3: Strategies to promote DOAC safety Study participants reported that reviewing medications by clinical pharmacists, dose adjustment according to indication and renal function, careful assessment after taking patient history, risk assessment for bleeding tendencies, providing patient education and continuous monitoring of patients could be effective strategies to promote DOAC safety. Furthermore, pharmacists also believed that nurses could play a major role in the prevention of errors by independently double-checking DOAC orders. They emphasised that the five rights of medication administration (the right patient, the right drug, the right dose, the right route, and the right time) should also be evaluated for DOAC users before administration. “The nurse should perform independent double checking; it is very important, especially for this high alert medication,” Participant 2, pharmacist (Riyadh) Participants believed that HCPs should work collaboratively in multidisciplinary teams to decide about 'patients' suitability to receive DOAC therapy. “I think forcing the implementation of the multidisciplinary approach is the number one goal to minimise the prescribing errors.” Participant 2, pharmacist (Riyadh) Pharmacists described that research is important to generate evidence regarding DOAC prescribing in COVID-19.“Sometimes physicians don't have any strong evidence (to support their practices with regard to DOAC, e.g., the doctors prescribe DOAC for post-COVID patients. But they don't have any strong evidence regarding this thing.” Participant 2, pharmacist (Jazan) Participants discussed that allowing easy identification of patients on DOAC by developing specific safety cards could allow delivery of appropriate anticoagulation care and minimise medication errors. Participants also described that medication reconciliation is another high-priority strategy that pharmacists should use to promote safety. “I believe the reconciliation is a very high priority when it comes to catching these kinds of errors. So, if we have proper reconciliation upon 'patients' admission and, discharge, you make sure that the patient has been… (looked after to avoid errors).” Participant 8, pharmacist (Riyadh) Interviewees identified that implementing clinical decision support systems could be an effective strategy to promote DOAC safety, not only for prescribing error reduction but also for dispensing and administration errors. Pharmacists raised the importance of improving the working environment and decreasing their workload to allow adequate time to promote patient safety. Busy shifts were deemed to negatively affect patient safety. In addition, participants discussed the importance of encouraging HCPs to report errors associated with DOAC. They emphasised that blame free reporting culture should be promoted, and the reporting system should be simplified to save time and facilitate reporting. Furthermore, interviewees identified that improving awareness and enforcing strict audits for institutional guidelines implementation would help HCPs during the whole medication cycle, especially if such audits could be supported by a hospital information system (HIS).“Well, I believe that, first, there should be strict protocols and guidelines, and this should be disseminated to all care providers, so they would know what the policy and educational activities is should also be done to them so they will know what's the policy.” Participant 7, pharmacist (Riyadh) Participants indicated that empowering clinical pharmacists to perform their daily tasks in dealing with these high-alert medications was very critical. They exemplified that pharmacists can promote safety by running anticoagulation clinics, communicating their recommendations to other healthcare providers in the team, following up patients and renal adjustment of doses based on patients' kidney function. ## Statement of key findings This study investigated pharmacists' views and experiences about contributory factors and mitigation strategies relevant to DOAC medication errors. Pharmacists believed that they could play a vital role in improving DOAC safety when they were provided with appropriate training and reliable information sources. These results are in line with the findings of previous studies which have highlighted the positive impact of pharmacists' interventions in the reduction of DOAC related medication errors [8]. Pharmacists could assist in clinical decision-making, perform bleeding risk assessment, help with dose adjustment, participate in formulary decisions, and provide patient counselling in addition to running specialised anticoagulation clinics [16]. The data gathered suggests that barriers to DOAC safety included poor communication with patients, lack of knowledge and training of HCPs, underreporting of errors, and lack of follow-up of patients receiving DOAC. The study further highlighted several patient-related factors such as misunderstanding of indications and proper doses, having multiple comorbidities, lack of prior experience with DOAC increased likelihood of errors. Pharmacists suggested that careful assessment of cases during history taking, better monitoring, double-checking of orders by pharmacists, multidisciplinary management, research, improving healthcare providers' awareness regarding guidelines and enforcing multidisciplinary teamwork could improve DOAC safety. Moreover, pharmacists recommended appropriate education and counselling of patients, availability of patient cards indicating the use of DOAC, and improving clinical decision support systems. They emphasised on the need to reduce the workload for healthcare providers, encourage error reporting by HCPs and updating of clinical guidelines in order to prevent medication errors. ## Interpretation A recent study conducted in Qatar corroborated our findings in supporting the positive role of well-trained, experienced pharmacists in promoting DOAC safety. The study authors reported that clinically experienced pharmacists (i.e. those who had Board certification or clinical involvement in DOAC prescribing or dispensing) had better awareness and experience in DOAC prescriptions management, compared to other pharmacists [17]. Our study findings are concordant with the results of previously published studies that highlighted the importance of pharmacist-led clinics or services in reducing DOAC errors [5, 18]. Moreover, our study participants revealed that the clinical pharmacist role was vital to improving patient care, especially for patients with chronic disease, comorbidities and receiving polypharmacy. Pharmacists' involvement in care with other HCPs was also emphasised by physicians and nurses in our previous study [12]. Physicians' views highlighted the pivotal role of multidisciplinary teamwork in preventing medication errors [19]. Similar to the findings in this study, other literature also suggested pharmacists face similar challenges while reporting incidents of medication errors [20]. Time limitations, heavy workload, and difficulty in dealing with the reporting system were identified by pharmacists as key barriers to error reporting in our current study and the published literature [20]. Pharmacists in our study reported that improving awareness and education for healthcare providers is vital for the identification and prevention of medication errors. Implementation of education and training programs while focusing on reflective learning might ultimately reduce medication errors [20]. ## Strengths and weaknesses This study provides an in-depth understanding of contributory factors in relation to medication errors associated with DOAC from pharmacists' perspectives. Although our findings may not be generalisable due to the data generated from a qualitative study, the involvement of participants from different regions of Saudi Arabia enhanced the transferability of the findings. The participants represented healthcare institutions with diverse staffing levels, patient populations, and variable healthcare practices. Data were collected using a topic guide designed based on the literature, researchers’ previous studies, expert review and discussion amongst the research team which enhanced the face and content validity of the data collection tool. ## Further research Our study findings provide experience-driven insights for HCPs and policymakers regarding pharmacist views about factors that could hinder DOAC safety. Effective and theory-based interventions to improve the knowledge of healthcare professionals, and patients are needed. Multifaced interventions that combine education and technology are required. Implementation and evaluation of pharmacist prescribing, pharmacist run anticoagulation clinics and development of anticoagulation stewardship programs are likely to promote safety. Future research should investigate the efficacy of proposed DOAC risk mitigation strategies. Future research findings could help prioritise these strategies based on their measured impact on promoting DOAC safety. ## Conclusion Pharmacist participants of this study believed that DOAC safety could be improved through the education of patients and healthcare professionals, development and implementation of clinical guidelines, and improvement of error reporting systems. Furthermore, they highlighted the need to promote multidisciplinary team working. Future research should utilise multifaceted interventions to reduce error prevalence and assess the impact of such interventions. ## Supplementary Information Below is the link to the electronic supplementary material. Supplementary file1 (DOCX 18 kb) ## References 1. Hellenbart EL, Faulkenberg KD. Evaluation of bleeding in patients receiving direct oral anticoagulants. Vasc Health Risk Manag.* (2017.0) 13 325-342. DOI: 10.2147/VHRM.S121661 2. 2.Capiau A, Mehuys E, De Bolle L, et al. Drug–drug interactions with direct oral anticoagulants: development of a consensus list for ambulatory care. Int J Clin Pharm. 2023. 10.1007/s11096-022-01511-7 3. Mekaj YH, Mekaj AY, Duci SB. New oral anticoagulants: their advantages and disadvantages compared with vitamin K antagonists in the prevention and treatment of patients with thromboembolic events. Ther Clin Risk Manag (2015.0) 11 967-977. DOI: 10.2147/TCRM.S84210 4. Ferrat E, Fabre J, Galletout P. Inappropriate direct oral anticoagulant prescriptions in patients with non-valvular atrial fibrillation: cross-sectional analysis of the French CACAO cohort study in primary care. Br J Gen Pract (2021.0) 71 e134-e139. DOI: 10.3399/bjgp20X714005 5. 5.Al Rowily A, Jalal Z, Price MJ, et al. Prevalence, contributory factors and severity of medication errors associated with direct-acting oral anticoagulants in adult patients: a systematic review and meta-analysis. Eur J Clin Pharmacol. 2021;1–23. 6. Alrowily A, Jalal Z, Abutaleb MH. Medication errors associated with direct-acting oral anticoagulants: analysis of data from national pharmacovigilance and local incidents reporting databases. J Pharm Policy Pract (2021.0) 14 81. DOI: 10.1186/s40545-021-00369-w 7. Bakey KH, Kristen H, Nguyen CTN. Impact of a pharmacist intervention in the emergency department on the appropriateness of direct oral anticoagulants prescribed in venous thromboembolism patients. J Pharm Pract (2022.0) 35 599-605. DOI: 10.1177/08971900211000704 8. Haque H, Alrowily A, Jalal Z. Direct oral anticoagulant-related medication incidents and ‘pharmacists’ interventions in hospital in-patients: evaluation using’ ‘reason’s accident causation theory. Int J Clin Pharm (2021.0) 43 1693-1704. DOI: 10.1007/s11096-021-01302-6 9. Willeford A, Leiman V, Noel ZR. Impact of a pharmacist-to-dose direct oral anticoagulant protocol on medication errors at an academic medical center. J Am Coll Clin Pharm (2021.0) 4 1392-1400. 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Consolidated criteria for reporting qualitative research (COREQ): a 32-item checklist for interviews and focus groups. Int J Qual Health Care (2007.0) 19 349-357. DOI: 10.1093/intqhc/mzm042 15. 15.Woolf NH, Silver C. Qualitative analysis using MAXQDA: The five-level QDATM method. 1st ed. New Yor: Behavioral Sciences; 2017; ISBN 9781315268569. 16. Burnett AE, Barnes GD. A call to action for anticoagulation stewardship. Res Pract Thromb Haemost. (2022.0) 6 e12757. DOI: 10.1002/rth2.12757 17. El-Bardissy A, Elewa H, Khalil A. Assessing pharmacists knowledge and attitude toward the direct oral anticoagulants in Qatar. Clin Appl Thromb Hemost (2020.0) 26 1076029620933946. DOI: 10.1177/1076029620933946 18. Ashjian E, Kurtz B, Renner E. Evaluation of a pharmacist-led outpatient direct oral anticoagulant service. Am J Health Syst Pharm (2017.0) 74 483-489. DOI: 10.2146/ajhp151026 19. Breuker C, Abraham O, di Trapanie L. 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--- title: Evaluation of a stakeholder advisory board for an adolescent mental health randomized clinical trial authors: - Alicia M. Hoke - Perri Rosen - Francesca Pileggi - Alissa Molinari - Deepa L. Sekhar journal: Research Involvement and Engagement year: 2023 pmcid: PMC10044104 doi: 10.1186/s40900-023-00425-6 license: CC BY 4.0 --- # Evaluation of a stakeholder advisory board for an adolescent mental health randomized clinical trial ## Abstract We conducted a study (Screening in High Schools to Identify, Evaluate, and Lower Depression) to understand if an adolescent major depressive disorder screening tool delivered in the school setting aided in the identification of symptoms and treatment. We planned and conducted this study with the guidance of a stakeholders, including adolescents. At the end of each study year, we sent an evaluation survey to stakeholders to understand their experience, such as how appropriately the study included stakeholders and their perspectives. We also surveyed the team leading the study to understand their perspectives about stakeholder involvement. In general, both stakeholders and the study team reported feeling positive about stakeholder involvement; However, some stakeholders felt less involved as the study moved forward, and for some activities stakeholders and study team did not agree on how much the stakeholders were involved in study activities. Additionally, adolescent stakeholders reported low involvement in the study when completing the final evaluation, which, unfortunately, was not captured in the evaluations conducted in earlier study years. By evaluating the experiences of stakeholders, along with gathering perspectives of the study team, we were able to understand how well we involved stakeholders. However, additional questions remain unanswered, such as how best to involve adolescents as stakeholders, and how involving stakeholders impacted the results of our study. Evaluation tools to best understand these impacts are needed across the field of community-engaged research to answer these questions for future studies. ### Introduction Community engagement in research is widely accepted as best practice, despite gaps in existing frameworks to evaluate its process, context, and impact on research. The Screening in High Schools to Identify, Evaluate, and Lower Depression (SHIELD) study evaluated the use of a school-based major depressive disorder screening tool in the identification of symptoms and treatment initiation among adolescents, and was developed, implemented, and disseminated in partnership with a Stakeholder Advisory Board (SAB). We summarize outcomes of the evaluation strategy applied through our partnership with the SAB and explore gaps in the available engagement evaluation tools for mixed stakeholder populations including youth. ### Methods SHIELD study SAB members ($$n = 13$$; adolescents, parents, mental health and primary care providers, and professionals from education and mental health organizations) advised on study design, implementation, and dissemination over a three-year period. Both SAB members and study team members (i.e., clinician researchers, project managers) were invited to quantitatively and qualitatively evaluate stakeholder engagement after each project year. At the conclusion of the study, SAB members and study team members were asked to evaluate the application of engagement principles in overall stakeholder engagement across the study period, using portions of the Research Engagement Survey Tool (REST). ### Results SAB members and study team members responded similarly when evaluating engagement process (i.e., valued on team, voice represented); means ranged from 3.9 to 4.8 out of 5 points across all three project years. Reported engagement within study-specific engagement activities (i.e., meetings, study newsletter) varied from year to year, with some discrepancy between SAB member and study team evaluations. Using REST, SAB members reported the alignment of their experience with key engagement principles the same or higher than study team members. Qualitative feedback at the conclusion of the study generally matched quantitative measures; adolescent SAB members, however, reported disengagement from stakeholder activities that was not accurately or effectively captured in evaluation strategies employed across the study period. ### Conclusions Challenges exist in effectively engaging stakeholders and evaluating their engagement, particularly among heterogenous groups that include youth. Evaluation gaps should be addressed through the development of validated instruments that quantify the process, context, and impact of stakeholder engagement on study outcomes. Consideration should be given to collecting parallel feedback from stakeholders and study team members to fully understand the application and execution of engagement strategy. ## Introduction Community engagement in research has flourished over the last two decades, creating opportunities to develop, implement, and disseminate research impacting communities and the health of their citizens. National funding agencies such as the Patient-Centered Outcomes Research Institute (PCORI), Centers for Disease Control and Prevention, and National Institutes of Health have laid the groundwork for health researchers to go beyond the traditional models of research “for” the community, migrating toward expectations that research be created “with” the community of interest. Despite leadership among national funders to prioritize the inclusion of stakeholders in the research process, the research community has lagged in the development of standard, agreed-upon community and patient-engagement frameworks and nomenclature. Key et al. [ 1] described a continuum of community-engaged research (CEnR), highlighting levels of involvement for community members and academic research partners at each level. This framework serves as a grounding point for CEnR teams to establish the most appropriate strategies to meet the community engagement goals of a study. Majid and Gagliardi [2] also conducted a review of engagement literature to understand varying terms used to describe levels of “meaningful” engagement. Their review also presented strategies expected of health researchers at each level. Several articles have also described key principles of stakeholder engagement in research [3, 4]. For example, Goodman and colleagues [3] described a consensus-building study that included perspectives from national experts in stakeholder engagement and community stakeholders to identify the primary principles that should underpin stakeholder-engagement activities and methodologies. Harrison and colleagues [4] conducted a review of patient engagement in research. This review identified many similarities to the work described by Goodman and colleagues [3], and also highlighted potential other emerging engagement practices in the field. While there is overlap in the suggested strategy and methodology reported in the described literature, there is still discrepancy and a lack of standardization to guide CEnR teams. The field of CEnR is also in need of a systematic way to evaluate the accepted best practice of engaging stakeholders and patients in research. Esmail et al. [ 5] conducted a review of literature describing benefits of CEnR, proposing measurable components for process, context, and impact of engagement. More recently, Luger et al. [ 6] conducted a mapping review of studies that included CEnR evaluation, sorting evaluation strategies into (a) context measures, which evaluate capacity within the community to engage with research (i.e., training, experiences), (b) process measures, focused on group dynamics and general experiences, and (c) outcome, or impact, measures, which can examine both the impact of the engagement on the community partners and the impact of community engagement on the research, itself. Context and process measures were identified most frequently, where outcome measures were less common. These reviews, and others described by Harrison and colleagues [4] also highlight the lack of standardized evaluation tools and the implications these gaps can have on understanding the impact of stakeholder involvement in research. The Screening in High Schools to Identify, Evaluate, and Lower Depression (SHIELD) study evaluated the use of a school-based major depressive disorder (MDD) screening tool in the identification of MDD symptoms and treatment initiation among adolescents [7]. This randomized clinical trial (RCT) was developed, implemented, and disseminated in partnership with a Stakeholder Advisory Board (SAB). The engagement strategy for this study was governed by PCORI engagement principles. However, gaps in standardized evaluation methodology presented challenges in understanding the impact our SAB had on study activities. This manuscript describes the evaluation strategy (along with challenges) utilized with SAB members in the SHIELD study, summarizes the outcomes of the evaluation strategy, and explores gaps in the available engagement evaluation tools for mixed stakeholder populations including youth. ## Participants and setting As outlined in Hoke et al. [ 8], the SHIELD SAB included adolescents [2], a parent [1], mental health and primary care providers [2], and professionals from education [3] and mental health organizations [5]. The initial SAB was comprised of 11 stakeholders, with 2 additional stakeholders joining for years 2 and 3 ($$n = 13$$). The adolescent SAB individuals were members of a high school mental health club. Due to this arrangement, a small, but fluid number of club members fulfilled the adolescent role for the SAB. The SAB met quarterly in a virtual format for 3 years (spring 2019 to fall 2021) and conducted one in-person meeting during the first year. In-person meetings were discontinued with the onset of the COVID-19 pandemic during the second year. SAB members engaged in a variety of activities (Table 1) across the three engagement years. Some activities spanned the project (i.e., SAB meetings, study newsletters), while others were activated and discontinued based on the milestones of the overarching SHIELD study. For example, engagement year 1 occurred during the launch of the RCT, resulting in stakeholder activities that aligned with recruitment and RCT launch (Table 1), where year 3 focused on results dissemination. Table 1Stakeholder advisory board member activities during SHIELD study 2019–2021Year 1Year 2Year 3Overall SHIELD Study Focus1. RCT recruitment & launch2. Planning for qualitative study components1. RCT implementation2. Qualitative study implementation3. Planning for dissemination1. Data analysis (RCT and Qualitative)2. Result disseminationActivities (example actions)SAB meetings (pre-reading, attend meetings)X Quarterly; 3 virtual, 1 in personX Quarterly, all virtualX Quarterly, all virtualStudy newsletter for participating schools (content development; editing/proofing)X Two per yearX Two per yearX Two per yearSupport recruitment (leverage school contacts)XQualitative study interview guides (question development; editing)XXMDD awareness video (development of storyline/script; proofing)XX DisseminationResult dissemination (lay language guidance)X QualitativeX Qualitative & RCTPublications and presentations (co-authorship)XXSAB Stakeholder Advisory Board, RCT randomized clinical trial, MDD major depressive disorder Of note, SAB members for this study did not have a direct role in the foundational development of the RCT, however stakeholder feedback was solicited in its development. This is further described in Hoke et al. [ 8]. ## Procedures and instrumentation SAB members were invited to evaluate their level of engagement at the conclusion of each program year. Surveys were distributed electronically. The annual SAB member evaluation, influenced by similar surveys utilized in Kraschnewski et al. ’s [9] studies, included core process evaluation questions asked across each program year regarding (a) SAB member experience (i.e., ability to contribute ideas, meetings were valuable use of time) and (b) perceived impact of their contributions on the research (i.e., voice represented, ability to leverage expertise, valued on the team). Survey items utilized a 5-point Likert scale with increasing numbers indicating more favorable response. In years 2 and 3, SAB members also reflected on their level of engagement (context evaluation) in stakeholder activities (outlined in Table 1; i.e., study newsletter, presentations and publications), using a 4-point Likert scale where increasing numbers indicate higher levels of engagement. In addition to evaluating SAB member perspectives, study team members (i.e., clinician researchers, project managers) annually evaluated their perceptions of stakeholder engagement. Study team members reflected on opportunities for SAB members to contribute, and the presence of the stakeholder voice in study progress (process evaluation; 5-point scale), along with SAB member engagement in stakeholder activities (outlined in Table 1). Each annual survey for SAB members and study team members also included open text fields for more specific feedback. All surveys were completed anonymously. At the conclusion of the study, in addition to the annual evaluation elements previously described, SAB members and study team members were asked to evaluate overall stakeholder engagement (process evaluation) across the 3-year engagement period using portions of the Research Engagement Survey Tool (REST), developed by the Goodman Lab. [ 3, 10] REST was designed to evaluate application of PCORI’s engagement principles in stakeholder engagement activities-both how well the engagement principles are executed (quality) and how often (quantity) they are exhibited. Segments of the REST tool were selected for inclusion based on the engagement principals applied through engagement activities with our study. We selected the following four principles, and thereby the associated questions, for inclusion: (a) Partner input is vital, (b) Foster co-learning, capacity building, and co-benefit for all partners, (c) Build on strengths and resources within the community or patient population, and (d) Involve all partners in the dissemination process. Questions utilized 5-point scales where higher numbers in each scale represented greater quality or quantity, as applicable. The SAB member evaluation also included open-ended questions to collect reflections on their entire experience and provide recommendations for future engagement. All surveys were completed anonymously. Data was managed in REDCap (Research Electronic Data Capture) survey. REDCap is a secure, web‐based platform used for data collection purposes by researchers, hosted at Penn State Health Milton S. Hershey Medical Center and Penn State College of Medicine [11, 12]. ## Data analysis All data were summarized (means and standard deviations) using Microsoft Excel. Each data point was averaged within groups (i.e., SAB, Study team) for each annual evaluation to observe change over time. REST questions were analyzed according to guidance provided by the tool developers [13]. Questions in each section of the REST tool utilized (i.e., engagement principles 2, 4, 5, and 7) were averaged across respondents to develop both a quality and quantity score for each engagement principle measured. Informal comparisons between responses from study team versus SAB member respondents were generated. ## Results A total of $\frac{6}{11}$, $\frac{9}{13}$, $\frac{8}{13}$ SAB members and $\frac{8}{11}$, $\frac{10}{11}$, $\frac{8}{11}$ study team responses were received in response to the annual evaluation in years 1, 2 and 3, respectively. ## Annual SAB member and study team member process evaluation Overall, SAB members responded favorably about their experience and perceived impact, with a mean score of 4.1 points or higher for each annual process evaluation item (Fig. 1). Study team members responded similarly in their assessment of stakeholder opportunities to contribute ideas and the representation of the stakeholder voice in study progress, reporting a mean of 3.9 points or higher for each process evaluation item (Fig. 1). The inclusion of the stakeholder voice was reported lowest in year two by both SAB members and study team members. The study team evaluation also reflected fewer opportunities to engage SAB members in year two, as compared to years one and three. In addition, SAB members reported an annual decline in the ability of the study team to leverage their expertise, though they perceived an increasing ability to contribute their ideas across the three study years. Fig. 1Results from annual engagement process evaluation, as self-evaluated by SAB members and assessed by study team ## Annual SAB member and study team member context evaluation SAB members and study team members responded similarly when evaluating levels of stakeholder engagement in stakeholder activities in years 2 and 3, including quarterly SAB meetings, development of a MDD awareness video, development of qualitative study elements (i.e., interview guides), development of the biannual study newsletter (i.e., contributions to format and content), and the development of publications and/or presentations related to the study (Fig. 2). SAB members and study team members reported stability or minor deviations in engagement from year two to three. The most notable discrepancy was engagement with the MDD awareness video, for which SAB members reported a decrease in engagement from year 2 to 3 and study team members reported a perceived increase in stakeholder engagement. Both groups perceived highest levels of stakeholder engagement with quarterly SAB meetings, compared to other activities. Though, SAB members reported slightly higher levels of engagement with quarterly SAB meetings in both years 2 and 3, when compared to study team members. Meeting attendance remained stable across each year (data not shown).Fig. 2Results from annual assessment of engagement in stakeholder activities, as self-evaluated by SAB members and assessed by study team ## Overall SAB member and study team member process evaluation-REST tool Using REST, study team members ($$n = 7$$) and SAB members ($$n = 7$$) evaluated overall engagement strategies across the three years and their alignment with specific PCORI engagement principles (Fig. 3). SAB members evaluated both quality (how well) and quantity (how much) of the principles the same or higher than study team members. SAB members and study team members responded most similarly in their experience of engagement principle 2, “partner input is vital.” For engagement principle 4, SAB members rated the quality of engagement efforts that “Foster co-learning, capacity building, and co-benefits for all partners” higher (4.4) than study team members (4.0). The largest discrepancy between groups was noted in quality and quantity of engagement principle 7, “Involve all partners in the dissemination process”, where SAB members evaluated both quality (4.3) and quantity (4.2) higher than the study team, who averaged quality and quantity scores at 3.8 and 3.6, respectively. Fig. 3Overall engagement evaluation results, as reported by SAB members and study team members using the Research Engagement Survey Tool (REST); EP-Engagement principle ## Qualitative feedback from final evaluation SAB members were asked to reflect on whether they met their personal goals for serving on the SAB. The majority responded favorably, one member sharing that they were able to “assist with meaningful work that will benefit schools and students” another stating they “learned a lot about research and the rolling out of a research study of this magnitude.” Of those who responded ($$n = 7$$), all indicated they would accept an opportunity to serve as a stakeholder on another project. However, feedback from participating students and supporting student advisors indicated that the experience may not have been equally as rewarding or beneficial. One student (as identified in the open text of the anonymous survey) reflected on meeting their goals as “I don’t know. I understood almost none of what anyone was saying, ever.” We also received feedback suggesting the “students felt rather clueless at many meetings…it’s important they feel engaged from the start.” The feedback was surprising, as these concerns were not raised in any previous evaluation timepoints and, therefore were unfortunately unable to be addressed in a timely manner. ## Discussion Engagement evaluation strategies for the SHIELD study spanned across both context and process domains. The summarized quantitative evaluation data suggest a high degree of stakeholder engagement in designated engagement activities, though challenges emerged in engaging all members of our heterogenous SAB equally. The inclusion of study team member perspectives on stakeholder engagement, in addition to the self-evaluation of SAB members, both corroborates the results, and introduces nuances to understand success and impact of stakeholder engagement that may be missed by a single perspective. SAB members self-reported engagement with quarterly meetings higher than any other engagement activity. This is understandable considering it was the most accessible way for SAB members to be involved in the study. Engagement in the development of the biannual study newsletter remained high across both years 2 and 3. This is likely because two newsletters were developed each year and we solicited support directly from individual SAB members to serve in both writing and reviewing roles, rotating our requests across SAB members for each edition. Activities that utilized a “request for volunteers” strategy (i.e., qualitative study development) garnered lower rates of reported participation, and thereby, reported engagement. We also recognize that activities that are more typically aligned with academic activities (i.e., publications/presentations) were of less interest to our SAB member group, unless the stakeholder had a personal or professional motivation for partnering on the activity. These observations suggests a need for alternate approaches to describing the activities and additional training may be needed to reduce barriers to participation. One area for overall engagement improvement was sustainability of engagement over time. Our ability to leverage stakeholder expertise throughout the study period and ensure meetings were worth the time of our stakeholders waned as the study approached completion, despite the fact that meeting participation remained stable and SAB members felt an increasing ability to contribute ideas. This may be a result of stakeholder activities changing across the study period (Table 1) to align with progress of the SHIELD study. As the study moved from development and launch, into implementation, and then toward data analysis and dissemination, the focus of quarterly meetings shifted toward delivering updates and involvement of individual SAB members in some specific engagement elements (i.e., publications), rather than collaboratively developing study products. This is not unique to our project, as others reported similar challenges in sustaining high levels of engagement throughout the course of a study [14–16]. Another interesting finding was the discrepancy between SAB members and study team members in their perceptions of how well and how much the study exhibited alignment with engagement principal 7, which describes engagement of all partners in the dissemination process (Fig. 3). SAB members perceived more and better alignment, suggesting an imbalance in the expectations of the study team and the SAB members, and the need to collaboratively develop expectations at the outset, and revisit them throughout the length of the partnership. While our study’s engagement approach did utilize strategies recommended by previous stakeholder-engaged studies, such as establishing shared expectations and sustaining engagement through frequent study meetings, utilization of study newsletters, and ongoing training and education opportunities [9], it is clear that not all members of our heterogenous SAB were equitably engaged. Our experience suggests that stakeholders may benefit from clearer understanding of the parameters and expectations for each activity, and how the activity ties to the study, along with ongoing and repeated grounding discussions about the status and purpose of the study activities. Additional exploration is warranted to understand the application of these, and other, engagement strategies in varying stakeholder engagement structures and group compositions. Engaging a heterogenous group of stakeholders is encouraged [17, 18] to ensure the research question is relevant, project implementation is feasible, and dissemination is robust and well received by the impacted community. This is especially true with health research involving youth populations, as literature describes the critical role youth engagement plays in adoption of/participation in health research and outcomes [14, 15, 19]. However, for our adolescent members, end of program evaluation elicited feedback about less than ideal experiences, though these concerns were not well captured through annual evaluation metrics. We hypothesize several explanations for limitations in adolescent engagement. First, quarterly stakeholder meetings were only conducted virtually after year one. While second nature now, the concept of engaging virtually was not the norm when introduced as the only form of stakeholder meeting, necessitated by the COVID-19 pandemic. Thus, adolescents could have joined the meetings but not be fully engaged due to a lack of understanding and/or feeling reluctant to speak up and seek clarification due to the heterogenous backgrounds of participating stakeholders. Second, communication with the adolescents between meetings was indirect as a result of needing to work through a club advisor. This indirect engagement may have impacted the connection or feeling of engagement of adolescent members. Lastly, there was inconsistency among which of the adolescent SAB members attended the quarterly meetings. Although the engagement strategy was designed this way to increase the presence of the adolescent voice in stakeholder activities (i.e., avoiding consistent schedule conflicts with more than one adolescent stakeholder available), this model potentially introduced challenges among adolescent SAB members to fully understand the historical SAB meeting information and stay connected. As such, adolescent SAB members may have lower confidence to engage. Future opportunities to engage adolescents as stakeholders may benefit from a youth leadership role to improve communication with the study team and improve equity among SAB members. The challenges we experienced in including adolescent stakeholders have been described by others, along with possible solutions for best engaging youth [14, 19–21]. Special consideration should be given to the ethical inclusion of youth, particularly in heterogenous stakeholder board scenarios [22]. Evaluation of stakeholder experience in heterogenous stakeholder populations presented unanticipated challenges and opportunities for improvement in the field. Namely, the needs of all stakeholders in a heterogenous stakeholder group may not be synonymous, resulting in the need for diverse engagement evaluation strategies that take into consideration, among other things, varying ages and education. Much of the evaluation literature [23, 24] focuses on utilizing qualitative strategies, which limits comparison within projects and generalizability across projects, but provides a rich understanding of individual stakeholder experiences. Martinez et al. [ 25] describe a stakeholder-centric instrumentation process where evaluation tools are customized to each project and accompanying stakeholder body. One challenge with the development of project-specific tools is the generalizability of stakeholder engagement, experience, and impact across the field. However, strategies Martinez and colleagues proposed may be helpful in the development of standardized tools that are representative of the interests and values of specific stakeholder groups, such as youth. The results of our study also present an opportunity to consider standardized strategies and best practices in evaluating engagement and its impact by both the stakeholder group and members of the study team. Development of validated quantitative evaluation tools designed for stakeholder use should have parallel tools to measure study team perspectives on the same topics, thus creating a better understanding of context, process and impact evaluation data. A first step in this process is the development of accepted engagement terminology across study teams and research stakeholders. Key and colleagues, along with Majid and Gagliardi describe formative work in this space. Sanders Thomson et al. [ 26] describes, more specifically, discrepancies in the way academicians and community members understand and interpret language used in engaged research. For example, the research term “stakeholders” and it’s community member alternate definition, “people with relevant lived experience.” These gaps must be bridged before standardized, inclusive, and meaningful evaluation can occur. Our experience in engaging stakeholders in the development, implementation, and dissemination of a randomized clinical trial with direct community impact exposed opportunities for improvement in evaluating the process and context of engagement with diverse stakeholder partners, in addition to the value of collecting parallel feedback from study team members. Learnings from our experience should also be considered as the field addresses another evaluation gap-the availability of validated instruments that quantify the impact of stakeholder engagement on study outcomes [23, 27]. Encouragingly, there is movement to bridge this gap. PCORI convened a workshop in 2016 to consider strategies for envisioning impact of stakeholder engagement and its evaluation [28], and Maurer [27] and colleagues conducted a qualitative study with researchers and stakeholders involved in 58 studies funded by PCORI to understand stakeholder engagement impacts on phases of the research process. Most recently, PCORI released a Science of Engagement request for proposals [29] “seeking to fund studies that build an evidence base on engagement in research, including measures to capture structure/context, process, and outcomes of engagement in research.” We look forward to the evaluation opportunities created through this funding mechanism. ## Limitations Evaluation data presented in this manuscript were self-reported by study team members and SAB members engaged in our study. We acknowledge limitations presented by our sample size and response rates, along with challenges presented by a lack of demographic information about our stakeholders. Additionally, due to the heterogenous nature of our SAB, and the anonymous format of our evaluations, we cannot confirm if the same SAB members participated in evaluation from year to year. We acknowledge that the evaluation data described represents stakeholder perspectives engaged with only one study, and may not be generalizable to other community-engaged research. Within our engagement evaluation strategy, we elected to utilize segments of an existing, albeit imperfectly aligned, evaluation tool, rather than engage our SAB members in the development of new, study-specific tools. Future efforts in engagement evaluation should prioritize the involvement of stakeholder populations in development and testing of instrumentation. These limitations further support the need to collectively move toward accepted terminology and standardized evaluation strategies to improve generalizability in engagement evaluation. ## Conclusions Engaging community members with varied perspectives and lived experience is an increasingly accepted research practice, however the mechanisms for effectively and consistently evaluating the process, context, and outcomes of those engagement strategies lags behind the practice. Challenges still exist in effectively engaging stakeholders, particularly heterogenous groups that include youth. Further exploration is needed to develop evaluation strategies that include broad (i.e., both qualitative and quantitative) understanding of engagement within a study, in addition to standardized metrics that can be used to understand the impact of engagement across community-engaged research. ## References 1. Key KD, Furr-Holden D, Lewis EY. 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--- title: Integration of the Microbiome, Metabolome and Transcriptome Reveals Escherichia coli F17 Susceptibility of Sheep authors: - Weihao Chen - Xiaoyang Lv - Xiukai Cao - Zehu Yuan - Shanhe Wang - Tesfaye Getachew - Joram M. Mwacharo - Aynalem Haile - Kai Quan - Yutao Li - Wei Sun journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044122 doi: 10.3390/ani13061050 license: CC BY 4.0 --- # Integration of the Microbiome, Metabolome and Transcriptome Reveals Escherichia coli F17 Susceptibility of Sheep ## Abstract ### Simple Summary Escherichia coli (E. coli) F17 is one of the major pathogenic bacteria responsible for diarrhea in farm animals; however, little is known about the biological mechanism underlying E. coli F17 infection. The aim of our study was to reveal the interplay between intestinal genes, metabolites and bacteria in E. coli F17 infected sheep. Our results confirm that the intestinal differ significantly in sheep with different E. coli F17 susceptibility, and integrated omics analyses reveal subsets of potential biomarkers for E. coli F17 infection (i.e., GlcADG 18:0-18:2, ethylmalonic acid and FBLIM1). Our results can help in the development of new insight for the treatment of farm animals infected by E. coli F17. ### Abstract Escherichia coli (E. coli) F17 is one of the most common pathogens causing diarrhea in farm livestock. In the previous study, we accessed the transcriptomic and microbiomic profile of E. coli F17-antagonism (AN) and -sensitive (SE) lambs; however, the biological mechanism underlying E. coli F17 infection has not been fully elucidated. Therefore, the present study first analyzed the metabolite data obtained with UHPLC-MS/MS. A total of 1957 metabolites were profiled in the present study, and 11 differential metabolites were identified between E. coli F17 AN and SE lambs (i.e., FAHFAs and propionylcarnitine). Functional enrichment analyses showed that most of the identified metabolites were related to the lipid metabolism. Then, we presented a machine-learning approach (Random Forest) to integrate the microbiome, metabolome and transcriptome data, which identified subsets of potential biomarkers for E. coli F17 infection (i.e., GlcADG 18:0-18:2, ethylmalonic acid and FBLIM1); furthermore, the PCCs were calculated and the interaction network was constructed to gain insight into the crosstalk between the genes, metabolites and bacteria in E. coli F17 AN/SE lambs. By combing classic statistical approaches and a machine-learning approach, our results revealed subsets of metabolites, genes and bacteria that could be potentially developed as candidate biomarkers for E. coli F17 infection in lambs. ## 1. Introduction Escherichia coli (E. coli) F17, a major subtype of enterotoxigenic E. coli, is one of the most common pathogens causing diarrhea in farm livestock, which causes huge economic loss and produces serious ecological concerns globally [1,2,3]. The E. coli F17 infection can disrupt intestinal tight junctions, increase permeability and resulting in severe vomiting, diarrhea and even death [4]. For the treatment of E. coli F17 infection, antibiotics are widely used in the farm industry; however, considering the potential of antimicrobial resistance [5], it is necessary to find alternative solutions, such as improving the susceptibility of animals to E. coli F17. To date, multiple approaches have been performed to understand the molecular signatures underlying pathogenic E. coli susceptibility of different species, such as RNA-seq [6], LC-MS [7] and GC-MS [8]. Moreover, various studies have shown that the integrated omics approach can identify the potential biomarkers that could be responsible for the E. coli infection. Fukuda et al. [ 9] integrated microbiome and metabolome analyses to elucidate the E. coli O157:H7 susceptibility of mice. In another study, integrated comparative genomics and immune-informatics approaches were applied to reveal the vaccine candidates against enterotoxigenic E. coli (ETEC) by Kusum et al. [ 10]. Recently, emerging evidence has indicated the promising power of machine-learning approaches in integrated omics studies for Alzheimer’s disease [11], cancer [12], diabetes [13], etc. Within multiple machine-learning methods, Random Forest has shown high accuracy and a low overfitting risk for multi-omics datasets (features ≫ samples). Recently, several Random Forest analyses on multi-omics data have identified diverse biomarkers across multiple biological processes, such as colorectal cancer [14], quality traits of potato [15] and clear cell renal cell carcinoma [16]. Collectively, these mentioned studies highlighted the reliability of Random Forest approach in identifying potential biomarkers using different types of multi-omics dataset. However, only very few studies focused on the crosstalk between the different obtained datasets, especially on E. coli F17 infection. In our previous study, lambs with different E. coli F17 susceptibility (E. coli F17-antagonism and -sensitive, AN and SE) were identified in a challenge experiment, and we analyzed the jejunal microbiota diversity [17] and transcriptomic profiles [18] using RNA-seq and 16s rRNA-seq approaches, respectively. The present study consists of two parts, first, the jejunal metabolite composition E. coli F17 AN and SE lambs were profiled using UHPLC-MS/MS approach. Then, we integrated the omics data to investigate the association between the microbiome, metabolome and transcriptome using a tree-based machine-learning approach: Random Forest. The aim of our study was to reveal the interplay between jejunal genes, metabolites and bacteria in E. coli F17 infection and could also help us understand the accuracy of this machine-learning method in integrated omics research. ## 2.1. Sample Collection Experimental lambs with different E. coli F17 (DN1401, fimbrial structural subunit: F17b, fimbrial adhesin subunit: Subfamily II adhesins, originally isolated from diarrheic calves) susceptibility were obtained from a challenge experiment—the details of the challenge experiment can be seen in our previous report [17]. In brief, 50 newborn Hu sheep lambs were randomly chosen and reared on lamb milk replacer free of probiotics and antimicrobial additives. The challenge experiment was conducted at 3 days after birth. The experimental lambs were divided into high-dose and low-dose challenge groups, which were orally gavaged with 50.0 and 1.0 mL of culture of E. coli F17 (1 × 109 CFU/mL) for 4 days, respectively. Subsequently, the E. coli F17 susceptibility of experimental lambs was evaluated via stool consistency scoring, histopathological examination on intestinal tissue and bacteria plate counting of the intestinal contents. Accordingly, six diarrhea lambs with severe intestinal pathology in the low-dose challenge group and six healthy lambs in high-dose challenge group were identified as E. coli F17-sensitive lambs (SE) and E. coli F17-antagonistic lambs (AN), respectively. Proximal jejunum tissue and jejunum contents were collected and stored in liquid nitrogen until use. ## 2.2. UHPLC-MS/MS Analysis and Data Processing The collected jejunum contents were individually resuspended with prechilled $80\%$ methanol. The resuspended tissues were incubated on ice for 5 min and then were centrifuged at 15,000× g, 4 °C for 20 min. Subsequently, the supernatant was diluted to a final concentration containing $53\%$ methanol by LC-MS grade water and then were centrifuged at 15,000× g at 4 °C for 20 min. Finally, the extracted jejunal metabolites were then accessed by the ultra-high-performance liquid chromatography–tandem mass spectrometry (UHPLC-MS/MS) analyses using a Vanquish UHPLC system (Thermo Fisher, Bremen, Germany) coupled with an Orbitrap Q Exactive™ HF-X mass spectrometer (Thermo Fisher, Bremen, Germany) [19]. Both positive polarity mode and negative polarity mode were operated to obtain the maximal coverage for jejunal metabolites. The raw data generated by the UHPLC-MS/MS were processed using Compound Discoverer (Thermo Scientific, Waltham, MA, USA, version 3.1) to perform peak alignment, peak picking and quantitation for each metabolite [20]. Then, the peak intensities were normalized to the total spectral intensity. The normalized data were used to predict the molecular formula based on additive ions, molecular ion peaks and fragment ions. Subsequently, the peaks were matched with the mzCloud, mzVault and Masslist databases to obtain the accurate qualitative and relative quantitative results. ## 2.3. Identification of Differential Metabolites These metabolites were annotated using the Kyoto Encyclopedia of Genes and Genomes (KEGG, https://www.genome.jp/kegg/pathway.html, accessed on 3 November 2021), Human Metabolome Database (HMDB, https://hmdb.ca/metabolites, accessed on 3 November 2021) and LIPID MAPS database (http://www.lipidmaps.org/, accessed on 3 November 2021). Principal components analysis (PCA) and partial least squares discriminant analysis (PLS-DA) were used to classify different samples with MetaboAnalystR R library ver 3.0 [21]. A t-test was used to identified subsets of differential metabolites, a metabolite was declared as differential metabolite if the difference in expression values between AN and SE lambs resulted in a Variable Importance in the Projection (VIP) > 1 and p-value < 0.05. The Pearson correlation coefficient (PCC) and p-value were also calculated to estimate the potential connection between differential metabolites, and the correlation was considered as statistically significant with a threshold of p-value < 0.05. ## 2.4. Acquisition of Microbiomic Dataset The jejunal microbiota dataset used in this study was obtained from our previous research [17]; the raw data are available on: https://www.ncbi.nlm.nih.gov/, PRJNA827002, accessed on 10 October 2022. In brief, total genome DNA was extracted from the jejunum contents of E. coli F17 SE/AN lambs, and the PCR amplifications of the 16S V3-V4 regions of the bacterial 16S rDNA gene were amplified using universal Primer 341F and Primer 806R. Sequencing libraries were prepared using TruSeq® DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, CA, USA) and sequenced on the Illumina NovaSeq platform after purification and quantification. After quality inspection, effective reads were assigned to the same operational taxonomic units (OTUs) by UPARSE software. The taxonomy of OTUs was aligned against SILVA reference database (SILVA SSU 138) based on the Mothur algorithm. A total of 1115 OTUs were clustered and then assigned to 16 phyla, 31 classes, 79 orders, 127 families, 241 genuses and 163 species. The detailed information of and the results of differential abundance analysis can be found in Supplementary Table S1. ## 2.5. Acquisition of Transcriptomic Dataset The mRNA expression dataset used in this study was obtained from our previous research [18]; the raw data are available on: https://www.ncbi.nlm.nih.gov/, PRJNA759095, accessed on 10 October 2022. In brief, RNA was extracted from the jejunum tissue of E. coli F17 SE/AN lambs, and the mRNA libraries were prepared using a NEB Next® Ultra™ RNA Library Prep Kit (Ipswich, MA, USA). All prepared libraries were sequenced on the Illumina HiSeq™ 2500 platform, and the raw data were obtained. After quality control, the clean reads were mapped to the Ovis aries reference genome (Oar_v4.0), and the mRNA candidates were distinguished using StringTie. A total of 20,601 mRNAs were screened. Differentially expressed (DE) mRNA were identified between AN and SE groups using edgeR, and 1465 DE mRNAs were identified between the AN and SE lambs. The detailed information can be found in Supplementary Table S2. ## 2.6. Integrated Analyses of the Multi-Omics Data Using Random Forest Tree-based machine methods have shown promising results in identifying variable importance and interaction effects when applied to multi-omics data. In the present study, a tree-based machine-learning method (Random Forest) was conducted to investigate the interaction within multi-omics data; the detailed strategy for RF was described in our previous research [22]. In the present study, two integrated analyses were performed, namely Microbiome–Metabolome and Microbiome–Metabolome–Transcriptome. According to the omics data size, two key parameters (Ntree and mtry) were systematically examined. The out-of-bag (OOB) error rate was calculated to determine the minimum hyperparameter values required for subsequent analyses. For interaction effect investigation, first, Random Forest was applied to select the subset of variables, and these variables were ranked by their values of variable important measures (VIM), the higher the “VIM” value, the more important the variable is in generate strong interactions with other selected variables [23]. Hence, the Pearson correlation coefficients (PCCs) between the top high “VIM” variables (the top $5\%$ variables for Microbiome–Metabolome analysis and the top $2\%$ variables for Microbiome–Metabolome–Transcriptome analysis) were calculated, and variable–target pairs with \|PCC\| > 0.8 and $p \leq 0.05$ were finally selected to establish the correlation network between different omics data. The RF machine-learning analyses were conducted using the randomForest R package [24]. The networks were established using cytoscape software ver 3.9.1 [25]. ## 3.1. Profiles of Jejunal Metabolites After filtering the internal standards and pseudo-positive peaks, a total of 1957 metabolites (1110 in positive polarity mode and 847 in negative polarity mode) were detected in the AN and SE samples. The details of the identified metabolites can be found in Supplementary Table S3. To investigate the biological relevance of the identified metabolites, functional annotation was performed using the KEGG, HMDB and LIPID MAPS databases. Figure 1 present the annotation results of the identified metabolites, the top three enriched pathways from the KEGG enrichment analyses were Global and overview maps (Metabolism category), Digestive system (Organismal Systems category) and Lipid Metabolism (Metabolism category). In HMDB annotation analyses, the top three enriched terms were Lipids and lipid-like molecules, Organic acids and derivatives and Organoheterocyclic compounds. In LIPID MAPS annotation analyses, the top three enriched terms were Fatty Acids and Conjugates, Glycerophosphocholines and Glycerophosphoethanolamines. The detailed annotation results of the identified metabolites can be found in Supplementary Table S4. Subsequently, PCA and PLS-DA were utilized to classify different samples, the PCA scores plot (Figure 2A,B) showed that the difference between the metabolite profiles of AN and SE groups was not obvious; however, the PLS-DA scores plot (Figure 2C,D) showed that the AN and SE samples were divided into two groups. Collectively, our results indicated relatively different metabolite profiles of AN and SE groups. ## 3.2. Identification of Differential Metabolites Combining the t-test results and the VIP value calculated based on PLS-DA, five differential metabolites were identified between the AN and SE groups in positive polarity mode, within which three metabolites were upregulated, and two metabolites were downregulated. Six differential metabolites were identified between the AN and SE groups in negative polarity mode, within which three metabolites were upregulated, and three metabolites were downregulated (Figure 3). The detailed differential analyses results can be found in Supplementary Table S5. The PCC and p-value were also calculated to estimate the potential correlation between the differential metabolites identified in positive polarity mode (Figure 4A) and negative polarity mode (Figure 4B), a strong positive correlation (PCC = 0.989) was observed between 2-[3-(4-pyridyl)-1H-1,2,4-triazol-5-yl] pyridine—PC (18:4e/17:2), and negative correlations (PCC = −0.596) were also observed between N-benzyl-3-(4-chlorophenyl)-4,5-dihydro-5-isoxazolecarboxamide and LPG 18:2. ## 3.3. Integrated Analysis of the Microbiome and Metabolome For integrated analysis of microbiomic data and metabolomic data, all variables were first accessed by RF; the parameter training results and detailed RF results are provided in Supplementary Table S6. The “VIM” values were calculated (supplementary table), and the top $5\%$ variables (13 bacteria species and 93 metabolites) were selected for subsequent analysis. The top three variables with the highest “VIM” values were GlcADG 18:0-18:2 (15.85), 2-(3,4-dihydroxyphenyl)-7-hydroxy-3,4-dihydro-2H-1-benzopyran-4-one (13.41) and oxytetracycline (13.34). Subsequently, the PCC between the selected variables were calculated (Supplementary Table S7). The correlation map (Figure 5) indicates strong correlations between the selected variables, and 316 interaction pairs with \|PCC\| > 0.8 were finally obtained for interaction network construction (Figure 6). For a better understanding of the interaction network, we calculated the edge betweenness centrality of each node in the network (Supplementary Table S8), the top three variables with the highest betweenness value were Adenosine (0.092), Lysopc 18:2 (0.085) and Guvacoline (0.083). The top bacteria species with the highest betweenness value was Ruminococcus flavefaciens (0.008), and the node with higher betweenness value indicated the node with stronger control power in the interaction network. ## 3.4. Integrated Analysis of the Microbiome, Metabolome and Transcriptome The parameters training results for integrated analysis of the microbiome, metabolome and transcriptome are provided in Supplementary Table S6. All variables were first accessed by RF, the “VIM” values were calculated (Supplementary Table S6), and the top $2\%$ variables (307 genes, 145 metabolites and 2 bacteria species) were selected for subsequent analysis. The top three variables with the highest “VIM” value were Ethylmalonic acid (6.31), FBLIM1 (5.89) and RNF213 (5.73). Subsequently, the PCCs between the selected variables were calculated (Supplementary Table S7). The correlation map (Figure 7) also indicated strong correlations between the selected variables, and 7779 interaction pairs with \|PCC\| > 0.8 were finally obtained for interaction network construction (Figure 8). Regarding calculating the edge betweenness centrality of each node in the network (Supplementary Table S8), the top three variables with the highest betweenness value were L-leucyl-L-alanine hydrate (0.22), 2-(3,4-dihydroxyphenyl)-7-hydroxy-3,4-dihydro-2H-1-benzopyran-4-one (0.15) and MRPL19 (0.13). ## 4. Discussion Recent advances in ETEC reveal the metabolic [26], microbial [27] and genetic mechanisms [28] underlying ETEC infection in different species; however, little is known about the E. coli F17 susceptibility of animals. In the present study, the jejunal metabolite profiles of E. coli F17 AN and SE lambs were first investigated using UHPLC-MS/MS approach. In addition, we presented a multi-omics study using RF to gain insights into the complex interactions between the microbiome, metabolome and transcriptome in E. coli F17 challenged lambs. ## 4.1. Jejunal Metabolic Profile of E. coli F17 AN and SE Lambs In the present study, a total of 1957 metabolites were profiled in AN and SE lambs, and a PCA plot and PLS-DA plot were constructed to classify different samples. The PLS-DA plot showed that a clear separation existed between the AN and SE lambs; however, clear group separation was not observed in the PCA plot. As a supervised dimension reduction method, PLS-DA can better yield group separation between similar groups compared with an unsupervised method (PCA) [29]. Considering the similar metabolic profile between E. coli F17 AN and SE lambs (all experimental lambs were challenged with E. coli F17), the different strategy underlying PCA and PLS-DA may be the reason for the different data grouping results. Then, functional enrichment was performed on the identified metabolites. Pathways related to the lipid metabolism were found as mostly enriched. In the small intestine, lipids function to maintain the cellular integrity of the IECs [30]. Therefore, it is possible that E. coli F17 can affect intestinal metabolic homeostasis via regulating the lipid metabolism. Subsequently, DE analysis was conducted to identify the differential metabolites, and 11 metabolites were identified differentially expressed in AN and SE lambs, within which, the most up-regulated metabolite was fatty acid esters of hydroxy fatty acids (FAHFA). FAHFAs are a kind of unique lipid messenger, which involves many immune-metabolic processes. In the gut, FAHFAs was previously proven to regulate GLP-1 (glucagon-like peptide-1) secretion and β-cell maturation [31]. Additionally, Rodriguez et al. reported that FAHFAs can inhibit apoptosis in colon carcinoma cells [32]. In the present study, the expression of FAHFA was remarkably higher in AN lambs than in SE lambs, which implied that the syntheses of FAHFA may also have similar inflammatory effects in sheep during E. coli F17 infection. The most down-regulated metabolite was propionylcarnitine. In humans, propionylcarnitine can serve as an immune marker to distinguish hepatocellular carcinoma from chronic hepatitis and cirrhosis [33]. In cattle, the upregulation of propionylcarnitine confirmed to confer good benchmarking for primary vaccine formulations [34]. Even though the biological effects of propionylcarnitine have so far not been fully characterized, the high expression of propionylcarnitine in SE lambs indicated that the assessment of propionylcarnitine may provide a feasible way to identify E. coli F17 AN individuals in lambs. It is worth noting that only 11 metabolites were identified in the present study. Similar results were also obtained by He et al. [ 8] and Kim et al. [ 35], who found that only small subsets of metabolites were found to be differentially expressed between E. coli F18 challenged pigs and non-challenged pigs (serum, ileal mucosa and colon digest). These findings indicated that the relatively stable metabolic profile may be a global characteristic for ETEC infection, which presents the demand for a more efficient approach to discover metabolic biomarkers rather than classic statistical methods, such as the machine-learning approach. ## 4.2. Integrated Analysis of the Microbiome, Metabolome and Transcriptome As a hallmark of ETEC, E. coli F17 infection can lead to the disrupted intestinal microbiota homeostasis, metabolic disorder and gene expression change [36]. In this context, we performed an integration of the microbiome, metabolome and transcriptome using RF, to investigate the crosstalk between genes, metabolites or bacteria species involved in the E. coli F17 infection. Random Forest, as the leading class of machine-learning algorithms, has shown high accuracy and low overfitting risk in diverse biological analyses, especially for the high-dimensionality datasets, such as multi-omics data [37,38,39]. In RF analysis, a VIM value is generated for each variable accessed by RF, the higher the ‘VIM’ value is, the more important the variable is for generation a prediction in the decision trees [23]. Hence, selecting the variables with high VIM value can be an effective method for identifying important biomarkers in multi-omics study. In the previous reported studies [39], $5\%$ were usually set as the cutoff threshold for variables selecting in RF, considering the size of different omics datasets (163 bacterial species, 1957 metabolites and 20,601 genes), and $5\%$ and $2\%$ were set as the threshold for variables selecting in Microbiome–Metabolome analysis and Microbiome–Metabolome–Transcriptome analysis, respectively. As mentioned above, the decision-tree-based strategy underlying RF indicated that certain interaction exists between the selected high-VIM variables and other variables [40]; hence, PCCs were calculated between selected variables. As expected, the correlation map of two multi-omics analyses showed that the selected variables are strong interacted with each other, which further prove the ability of RF in capturing the biological interaction between different omics datasets. In the integrated analysis of the microbiome and metabolome, the top three variables with the highest “VIM” value were GlcADG 18:0-18:2, 2-(3,4-dihydroxyphenyl)-7-hydroxy-3,4-dihydro-2H-1-benzopyran-4-one and oxytetracycline. Oxytetracycline is a well-studied and widely used antimicrobial for treatment of various bacterial infections [41,42]; furthermore, Sarmiento et al. reported that oxytetracycline can decrease the adhesion of E. coli K88 to intestinal epithelial cells (IECs) [43]. Collectively, the present results showed that oxytetracycline may function similarly in lambs during E. coli F17 infection. Little is known about the roles of GlcADG 18:0-18:2 and 2-(3,4-dihydroxyphenyl)-7-hydroxy-3,4-dihydro-2H-1-benzopyran-4-one in the intestinal immune response during E. coli F17 infection; however, the high VIM value showed that these two metabolites may serve as potential biomarkers in E. coli F17 infection. In the interaction network analysis, the variable with the highest betweenness centrality was adenosine. Adenosine is a key immunomodulator with complex biological roles in diverse immune responses [44,45]. Gross et al. reported that adenosine can protect mice against E. coli-induced acute lung injury [46]. However, Sun et al. reported that adenosine can also enhance the resistance of E. coli to acidic stress [47]. Despite the unexamined role of adenosine in E. coli F17 infection, the high betweenness centrality of adenosine implies that it may serve as a key regulator in the E. coli F17-induced diarrhea—of course, in-depth research needs to be performed to confirm our idea. Regarding the integrated analysis of the microbiome, metabolome and transcriptome, the top three variables with the highest “VIM” value were ethylmalonic acid and FBLIM1. Interestingly, both of the top two variables have been proven to function important in intestinal homeostasis. Ethylmalonic acid is a metabolic organic acid and is highly correlated with intestinal permeability [48], while FBLIM1 is a vital modulator in the epidermal growth factor receptor pathway [49], which can contribute to the proliferation and migration of IECs for preserving intestinal homeostasis [50]. The interaction analysis showed that the selected metabolites and genes were clearly separated into two groups, which were linked by L-leucyl-L-alanine hydrate, a commonly used substrate for fluorometric determination [51]. The gene with the highest betweenness centrality was MRPL19—directly linked to L-leucyl-L-alanine hydrate and 21 genes, which also has a high control power over the network. MRPL19 is one of the house-keeping genes, with expression stability across different tissues [52]. At odds with their expression stability, in the present study, the expressions of L-leucyl-L-alanine hydrate and MRPL19 in SE lambs were higher than that in AN lambs. Although the specific biological roles of L-leucyl-L-alanine hydrate and MRPL19 remain incompletely understood, our results suggested that L-leucyl-L-alanine hydrate and MRPL19 may function as vital regulators in intestinal immune response to E. coli F17 infection. Regardless of the Microbiome–Metabolome analysis or Microbiome–Metabolome–Transcriptome analysis, only small subsets of bacterial species were picked in the analyses, the small size of the microbiome data (only 163 bacterial species) and similar intestinal microbiota between AN and SE lambs (all experimental lambs were challenged with E. coli F17), and this could be the reason for the results. Additionally, the variables with the highest VIM value did not outperform other variables in interaction network analysis (betweenness centrality) as expected, one potential explanation for the results is that only the variables with the highest VIM were selected for the interaction network analysis, while massive interactions may exist between the top variables and the other unselected variables. Another explanation is that the top variables with the highest betweenness centrality might be considered as leaders of connections to the E. coli F17 susceptibility, while the top variables with the highest VIM were directly involved in that. ## 5. Conclusions In summary, our study provides the metabolomic profile of E. coli F17 AN and SE lambs. A total of 1957 metabolites were profiled in the present study, and 11 differential metabolites were identified. 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--- title: Individualized Housing Modifies the Immune–Endocrine System in CD1 Adult Male Mice authors: - Iván Ortega-Saez - Alina Díez-Solinska - Roger Grífols - Cristina Martí - Carolina Zamora - Maider Muñoz-Culla - Oscar Vegas - Garikoitz Azkona journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044133 doi: 10.3390/ani13061026 license: CC BY 4.0 --- # Individualized Housing Modifies the Immune–Endocrine System in CD1 Adult Male Mice ## Abstract ### Simple Summary In recent years, awareness of laboratory animals’ wellbeing and the refinement of their house conditions have increased considerably. Mice (Mus musculus) are the most widely used animal species in research in the European Union and are sociable and hierarchical creatures. It is important to determine whether experimental conditions may affect research results and whether housing conditions (isolated or grouped) may be one such condition. The aim of this study was, therefore, to determine whether 4 weeks of social isolation (usual practice in our animal facility and some laboratory procedures) could induce changes in different physiological parameters (body weight, number of blood cells, and stress hormones) in adult mice. Although we did not observe changes in body weight, red blood cells, and platelets, mice that were socially isolated for 4 weeks did have a decreased count of some white blood cells. Moreover, levels of the main stress hormone were higher in single-housed mice after 1 week, although they decreased after 4 weeks to the same levels as those recorded for grouped mice. We can, therefore, conclude that social isolation affects some physiological parameters, and that this should be taken into account in the interpretation of research data. ### Abstract In the last years, different research groups have made considerable efforts to improve the care and use of animals in research. Mice (Mus musculus) are the most widely used animal species in research in the European Union and are sociable and hierarchical creatures. During experiments, researchers tend to individualize males, but no consideration is given to whether this social isolation causes them stress. The aim of this study was, therefore, to explore whether 4 weeks of social isolation could induce changes in different physiological parameters in adult Crl:CD1(ICR) (CD1) males, which may interfere with experimental results. Body weight, blood cells, and fecal corticosterone metabolites levels were the analyzed parameters. Blood and fecal samples were collected at weeks 1 and 4 of the experimental procedure. Four weeks of single housing produced a significant time-dependent decrease in monocytes and granulocytes. Fecal corticosterone metabolite levels were higher in single-housed mice after 1 week and then normalized after 4 weeks of isolation. Body weight, red blood cells, and platelets remained unchanged in both groups during this period. We can, therefore, conclude that social isolation affects some immune and endocrine parameters, and that this should be taken into account in the interpretation of research data. ## 1. Introduction People working with laboratory animals display a high level of awareness of and sensitivity to their wellbeing [1]. Indeed, perceived animal stress/pain has been found to negatively affect their professional quality of life [2]. In the last few years, different research groups have made considerable efforts to improve the care and use of animals in research, regardless of receiving specific funding for that purpose [3]. In the near future, this new scientific knowledge will provide new evidence to improve the welfare and housing conditions of animals used in scientific procedures. Current European legislation on the protection of animals used for scientific purposes (Directive $\frac{210}{63}$/EU) establishes suitable environmental conditions and minimum enclosure measures by age and animal species. It likewise indicates that social laboratory animals must be socially housed in stable groups of compatible individuals. Moreover, procedures in which social animals (e.g., dogs and monkeys) are completely isolated for prolonged periods are classified as “severe” [4]. However, the legislation does not specify what exactly is considered to be a “prolonged period”, and it does not mention other social species. Despite the current debate about their predictive value in basic and regulatory studies [5,6,7,8,9,10], mice (Mus musculus) continue to be the most widely used animal species in research in the European Union [11]. Mice are sociable and hierarchical animals that, in nature, live in small groups. These groups are usually composed of a dominant male, along with various females with their offspring, both young and juvenile. The size of the territory occupied by a mouse family varies according to different factors. These include the availability of different resources such as water and food, as well as the density of the group. Occasionally, depending on the aggressiveness of the dominant male and the density of the group, young males are found in the aforementioned family groups. Generally, however, males are usually rejected from the group when they reach sexual maturity and can be found in the wild alone or in groups of young males. As for the females, they usually become part of the family group once they reach sexual maturity [12]. Unfortunately, in animal facilities, mice are not housed as in their natural environment, thus interfering with their natural ethogram. Standard laboratory protocols stipulate that mice’s weaning and maternal separation should occur 21 days after birth. Thereafter, it is recommended that animals should be housed separately by sex and strain in stable groups of 2–5 members, a step that fosters the formation of affiliate relationships between individuals in the same group [13] and reduces aggression between males [14]. The main reason for housing male mice individually is aggression between cage mates [15,16]. Recently, a series of recommendations were published to minimize aggression between males [17]. Keeping newly weaned animals in the company of other animals is important for the correct development of their brains. It has been shown that post-weaning social deprivation by isolating mice induces neurochemical and morphological alterations, which have a behavioral impact in adulthood [13,18,19,20,21,22,23]. Indeed, the lack of social experiences before adulthood has been used in mice as a model to study some impaired behavioral phenotypes, such as depression and anxiety-like behavior types [21,22,23], as well as social and cognitive deficits [19,22]. In light of the above, in our animal facility, we implemented two different strategies in order to minimize the number of single-housed newly weaned male mice [24,25]. There is still an ongoing debate about whether adult male mice should be housed individually [15,26]. Years ago, “isolation syndrome” was described, with authors arguing that the inability to interact socially is likely to have a harmful effect on the animal’s emotional state [27]. Indeed, it has been proven that adult male mice prefer the proximity of another male over individual housing [28], which is considered a stressor. The gold standard to measure the immediate physiological responses to stress is the activation of the hypothalamic–pituitary–adrenal (HPA) axis, which induces the secretion of corticosterone from the adrenal gland [29]. The effect of solitary versus social housing on corticosterone levels has been explored with varying results. Some studies observed that single-housed male mice had increased corticosterone levels after 14 days [30] and 15 months [31], whereas others found that corticosterone levels remained stable up to 42 days of individual housing [32,33,34,35,36], and two studies reported that single housing caused less stress for mice than group housing [37,38]. Other indications of stress include changes in body weight and a decrease in circulating leukocytes. A meta-analysis of the effects of individual housing on body weight found considerable heterogeneity in different mice strains, with higher, unchanged, or lower body weights being reported after social isolation [39]. Although it is well documented that chronic stress results in immunosuppression [40], differences in the total number of white blood cells have also been observed [36,41]. Among other factors, these discrepancies may be due to differing isolation periods. In our animal facility, researchers tend to individualize males during experiments for a maximum period of 4 weeks, mainly for reasons of convenience and habit. However, no consideration is given to whether individually housing animals may cause them stress. The aim of the present study was, therefore, to determine if 4 weeks of social isolation could induce changes in body weight, blood cells, or fecal corticosterone metabolite levels in adult Crl:CD1(ICR) (CD1) males, which may interfere with experimental results. ## 2.1. Animals Mice born in our specific pathogen-free (SPF) breeding zone were housed in pressurized and individually ventilated 1145T (403 × 165 × 174 mm; 435 cm2 floor area; Tecniplast) (PIV) cages (70 air changes/h). We used black poplar/aspen shavings (Lignocel Selectfine; Rettenmaier Ibérica S.L.) as litter bedding, two sheets of tissue (Tork®; Essity Spain S.L) irradiated by Ionisos Iberica as nesting material, and an in-house autoclaved cardboard cylinder (12.5 × 9 × 0.5 cm; Sodispan Research S.L.) as enrichment. Once a week, socially housed mice (four mice per cage), together with their nesting material, were transferred to clean cages by picking them up at the base of their tails. This same procedure was carried out with individually housed mice every other week. New irradiated tissue was added if the nest was dirty or did not have enough material. Similarly, if the cardboard was broken, a new cylinder was provided. Mice had ad libitum access to water and diet (irradiated Special Diet Services RM1). Rooms were maintained under standard environmental conditions (humidity: 55 ± $10\%$; temperature: 20–24 °C) with a 12 h light/dark cycle (lights on at 8:00 a.m.). Animals were monitored every day. The animal care and use program was accredited by AAALAC International. The Catalan Government and the PRBB Ethics Committees approved the experimental protocol (DAAM 10576). ## 2.2. General Procedure Eight-week-old CD1 mice were randomly assigned to two groups (grouped or single; $$n = 8$$ per group, 16 in total) and housed in the same room in which they were born. We selected CD1 adult male mice because they are outbred, are the most commonly used strain in toxicology studies [42], and have a high propensity to fight, resulting in suggestions that they may benefit from individual housing [15]. This does not apply to females, since chronic social isolation is used to model separation-induced depression [43]. Animals were weighed on the same day of the week for 5 weeks (weeks 0–4; 9:00–11:00 a.m.). Sampling was carried out in a laboratory adjacent to the room where they were housed, and the animals were transferred there 1 h before sampling, around 8:00 a.m., because the technician started their working day at this time. Sampling was carried out at two different time points to minimize the influence of handling as much as possible. Thus, on weeks 1 and 4 (9:00–11:00 a.m.), whole blood and fecal samples were obtained from each animal (Figure 1). No signs of fighting were observed during the experimental period. None of the animals had adverse events, and all completed the procedure. Animals became part of our colony once the experiment was completed. ## 2.3. Hematological Parameters Blood samples were obtained by facial vein puncture with a 21 G sterile hypodermic needle. We collected blood from the facial vein because this procedure has been found to have the least adverse effects on welfare parameters in mice [44,45]. Samples (15 μL) were collected using a Microvette® 200K3E with potassium salt of ethylenediaminetetraacetic acid (EDTA) as an anticoagulant. After sampling, mice were returned to their home cage. No residual bleeding was noted in any of the animals. The blood was immediately analyzed for complete blood count: white blood cells (WBC), lymphocytes, monocytes, granulocytes, red blood cells (RBC), hemoglobin (HGB), hematocrit (HCT), mean corpuscular volume (MCV), hemoglobin (MCH) and hemoglobin concentration (MCHC), red cell distribution width (RDW), platelets (PLT), mean platelet volume (MPV), platelet distribution width (PDW), and platelet crit (PCT), using the fully automated CVM-Procell analyzer (CVM Diagnóstico Veterinario SL). Since the provider could not give us information about the exact mouse strain, age, or sex where the values were obtained, we first determined if the blood value range of male and female adult mice of different commonly used strains were within the normal range indicated by the analyzer. Our results indicated that the normal range provided for mice by the CVM-Procell analyzer can be used for adult male and female inbred C57BL/6J, outbred CD1, and immunodeficient CB17.Cg-PrkdcscidLystbg-J/Crl (SCID Beige) mice (see Supplementary Materials). ## 2.4. Fecal Corticosterone Metabolites Fecal samples were obtained by placing each animal on a grid. The fecal boluses were obtained directly, without possible contamination, placed in an Eppendorf, and stored at −80 °C to determine corticosterone metabolite levels. After sampling, mice were returned to their home cage. This sampling method may allow a more accurate interpretation of chronic stress [46]. Moreover, since there is no need to restrain the animals when collecting the samples, this is a good method for enabling repeated sampling without affecting the animal, meaning that fecal samples are less affected by hormone secretion fluctuation or pulsatility. Each fecal sample was homogenized, and an aliquot of 0.05 g was shaken with 1 mL of $80\%$ methanol in Tris/HCl 20 mM, pH 7.5, for 30 min on a multi-vortex. After centrifugation, each aliquot was frozen at −80 °C until analysis. Fecal corticosterone metabolite levels were quantified in duplicate using an enzyme immunoassay (Corticosterone Elisa Kit, Enzo Life Sciences; ADI-900-097), in accordance with the manufacturer’s recommendations, and a Synergy HT microplate reader (BioTek Instruments, Inc., Winooski, VT, USA). Data were analyzed by means of a four-parameter logistic curve fit using MyAssays (Data Analysis Tools and Services for Bioassays; available at https://www.myassays.com/ accessed on 10 March 2023). The sensitivity of the assay was 27.0 pg/mL, and the intra- and inter-assay variation coefficients were between $7\%$ and $8\%$. ## 2.5. Statistical Analyses Experimental data were analyzed using GraphPad Prism software (6.01, GraphPad Software, Inc, San Diego, CA, USA). Group comparisons were performed using a two-way repeated-measures ANOVA, followed by Bonferroni’s post hoc test. Values of $p \leq 0.05$ were considered statistically significant ($95\%$ confidence). Data are expressed as the mean ± standard deviation (SD). The results are described in accordance with the ARRIVE guidelines [47]. ## 3.1. Body Weight Both groups of animals gained weight over the duration of the experiment (F[4,56] = 34,78, $p \leq 0.0001$). Grouped mice weighed 36.27 ± 2.46 g at week 0 and 39.46 ± 2.99 g at week 4. Single-housed mice weighed 38.20 ± 3.55 g at week 0 and 41.19 ± 4.29 g at week 4 (Figure 2). No significant differences were observed between grouped or single-housed mice. ## 3.2. Hematological Parameters The results indicated no significant differences between grouped and single mice in the number of cells in the white series at either week 1 or week 4. However, significant differences were observed as a function of time (F[1,14] = 5.52; $p \leq 0.05$; Table 1). The post hoc analysis indicated a significant decrease in WBC after 4 weeks of single housing ($t = 2.21$; $p \leq 0.05$). When white cell type was analyzed in more detail, significant time-dependent differences were observed in monocytes (F[1,14] = 10.45; $p \leq 0.01$), and the post hoc analysis indicated a significant drop in monocytes in single-housed mice after 4 weeks ($t = 2.714$ $p \leq 0.05$). Similarly, significant time-dependent differences were observed in granulocytes (F[1,14] = 7.63; $p \leq 0.05$), which dropped in single-housed mice after 4 weeks ($t = 2.46$, $p \leq 0.05$). The results indicated no significant differences between groups or timepoints in terms of the number of red blood cells and platelets (Table 2). ## 3.3. Fecal Corticosterone Metabolites The statistical study of fecal corticosterone metabolite levels revealed a significant interaction between variables (F[1,14] = 11,40, $p \leq 0.01$). The post hoc analysis indicated significantly higher corticosterone metabolite levels in single-housed (0.225 ± 0.05 ng/mg) than in grouped animals (0.132 ± 0.02 ng/mg) after 1 week ($t = 4.523$; $p \leq 0.001$). At 4 weeks, no differences were observed between groups (grouped: 0.165 ± 0.06 ng/mg vs. single: 0.168 ± 0.04 ng/mg; $t = 0.488$, $p \leq 0.05$), and single-housed corticosterone metabolite levels were normalized (Figure 3). ## 4. Discussion It is well known that animal welfare has an effect on the outcome of experiments. We must, therefore, always consider this factor when designing and carrying out experimental procedures. However, many researchers systematically tend to individualize animals in their experiments. Thus, the question we aimed to answer in this study was whether a lack of social interaction may modify physiological parameters, which may in turn interfere with experimental results. Our findings indicate that social isolation modifies some physiological parameters. As previously reported for CD1 male mice [48,49,50], social isolation for 4 weeks did not affect body weight gain. Similarly, our results revealed that social isolation did not modify RBC parameters. As far as we are aware, this is the first study in mice to analyze RBC parameters; thus, we cannot compare our results with previous findings. Mice that were changed from sharing a cage with littermates to living alone showed higher fecal corticosterone metabolites than those maintained in the group after the first week, although levels normalized after 1 month. These same results were recently observed in adult CD1 mice housed in the same conditions as our animals, in a ventilated rack with environmental enrichment [50], which may indicate habituation to the new situation. Due to the nature of our experimental design, we were unable to determine when exactly corticosterone metabolite levels normalized, and this is one of our study’s limitations. However, data from a previous study [33] indicated that fecal corticosterone metabolite levels start to decrease and remain stable from the second week onward. These data are consistent with those described previously in relation to the return of plasma glucocorticoids to baseline values during the first week after transport or translocation [51,52,53,54]. Among the grouped animals, no significant changes were observed across individuals, and the standard deviation within groups was very small. Our data, therefore, seem to suggest that, in contrast to observations by some authors [37,38], remaining grouped together does not appear to cause the animals any stress. We believe the main reason for this is that, as has indeed been pointed out previously [32], our mice were littermates and were grouped together from weaning. It is well known that increased glucocorticoid levels suppress cellular immunity [55]. No changes in monocytes and granulocytes were observed in single-housed animals after 7 days, although changes were found after 4 weeks. A previous study found no significant differences in the overall number of blood-circulating leukocytes between CD1 male mice that were socially isolated for 2 weeks and their socially housed counterparts [36]. However, C57BL6/J adult mice separated into individual cages for 2 h every day for 25 days were found to have a decrease in T cells, B cells, monocytes, and neutrophils [41]. Unfortunately, our system is not able to distinguish between the different types of lymphocytes and granulocytes; however, overall, our results are consistent with these findings and highlight the fact that isolation time is a factor to be considered. Another limitation of the study is that we did not study humoral immunity; previous studies found that fecal immunoglobulin A (IgA) excretion (a marker of long-term stress) takes at least 4 weeks to normalize [53]. It is important to note that CD1 adult males isolated for 21 days and subjected to mild psychological stress had lower splenocyte proliferation and lower IL-2 and IL-4 cytokine plasma levels than their grouped counterparts [32]. The same results were reported using shock as a stressor [55]. In addition to the limitations outlined above, our study had some further limitations. When designing the experiment, we wanted it to be as realistic as possible in terms of the day-to-day management of our animal facility technicians and researchers. Therefore, the animals were moved from dirty to clean cages by picking them up by the tail. In recent years, less aversive handling methods (e.g., tunnel or cup handling) have been shown to mitigate anxiety and depressive-like behaviors [56,57,58]. However, a recent study showed that picking mice up by their tail may not be a significant source of chronic husbandry stress [59]. In view of the results of this study and our daily practice, we decided to change the location of animals in this experiment by picking them up by their tail. We are all aware that efforts have to be made to implement less aversive methods of handling in daily practice in animal facilities. Nevertheless, it should also be kept in mind that this procedure takes more time; hence, the amount of work assigned to each technician when changing cages should also be reviewed. In our work, we did not study whether social isolation induced behavioral changes in our animals, because we were more interested in peripheral biomarkers than behavioral parameters. In a recent study performed on C57BL/6JRj mice housed singly for 10 weeks, no behavioral changes were observed in exploratory activity, anxiety, working memory, and fear memory [60]. However, a previous study using C57BL/6J and DBA/2 kept in individual housing for 7 weeks revealed that individual housing has strong strain- and test-specific effects on emotional behavior and impaired memory in certain tasks. Single-housed mice were hyperactive and displayed reduced habituation to novel environments. Reduced anxiety was established in the elevated plus-maze, but not in the dark/light test. Immobility in the forced swimming test was reduced by social isolation. Novel object recognition and fear conditioning were impaired in the single-housed mice, whereas water-maze learning was not affected [61]. In the same way, 2 weeks of single housing plus acute injection stress induced anxiety-like behavior in C57BL6/J mice [30]. Mouse strain and social environment also influence depression-like behavior caused by an immune challenge. In this sense, group-housed CD1 mice exhibited depression-like behavior 1 day after bacterial lipopolysaccharide (LPS) injection, while the behavior of single-housed CD1 mice was little affected during the 4 weeks of the experiment. In contrast, both grouped and single-housed C57BL/6 mice responded to LPS with an increase in depression-like behavior [62]. It would be interesting to conduct future behavioral studies to determine if, under our conditions, single-housed CD1 male mice show any behavioral changes. Another parameter we did not measure was body temperature. In recent years, it has been observed that laboratory mice suffer from thermal stress, and that this affects their immune system, among other physiological parameters [63,64]. In this sense, huddling, a form of social thermoregulation, is a major contributor to mice’s thermal physiology. Thus, single-housed mice are usually more affected by cold temperatures than grouped mice [65]. In order to mitigate this effect, two sheets of tissue were added to their home cage, and we ensured that they made a proper nest. In light of all these data, we recommend keeping males in stable groups from weaning onward. Researchers should be aware that the change from grouping to living alone induces stress and mild immunosuppression in CD1 male mice; hence, if the mice need to be separated for experimental reasons, these factors should be taken into consideration. ## 5. 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--- title: Dietary Frankincense (Boswellia serrata) Oil Modulates the Growth, Intestinal Morphology, the Fatty Acid Composition of Breast Muscle, Immune Status, and Immunoexpression of CD3 and CD20 in Broiler Chickens authors: - Shimaa A. Amer - Ahmed Gouda - Gehan K. Saleh - Arwa H. Nassar - Abdel-Wahab A. Abdel-Warith - Elsayed M. Younis - Dalia E. Altohamy - Maha S. Kilany - Simon J. Davies - Anaam E. Omar journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044135 doi: 10.3390/ani13060971 license: CC BY 4.0 --- # Dietary Frankincense (Boswellia serrata) Oil Modulates the Growth, Intestinal Morphology, the Fatty Acid Composition of Breast Muscle, Immune Status, and Immunoexpression of CD3 and CD20 in Broiler Chickens ## Abstract ### Simple Summary The expanding knowledge of risks posed by antibiotic resistance in the past decades has led the livestock industry to encourage antibiotic-free production. The search for alternatives to antibiotic growth stimulants has shown a rapid increase. The current work assessed the outcomes of dietary frankincense resin (Boswellia serrata) oil inclusion (0, 200, 400, or 600 mg kg−1 diet) on the performance, carcass traits, the fatty acid content of breast muscle, protein profile, thyroid hormones, and immune status of broiler chickens. The collective outcomes of this experiment suggested that frankincense oil supplementation exerted a positive effect on the growth and intestinal histology of broilers, and enriched the n-3 and n-6 fatty acid content and enhanced their immunity. ### Abstract This investigation explored the impact of dietary frankincense resin oil (FO) on growth performance parameters, intestinal histomorphology, fatty acid composition of the breast muscle, and the immune status of broilers. We allotted 400, three-day-old, male chicks (Ross 308 broiler) into four treatment groups (ten replicates/group; ten chicks/replicate). They were fed a basal diet with different concentrations of FO (0, 200, 400, and 600 mg kg−1). FO supplementation increased the overall body weight (BW) and body weight gain (BWG) by different amounts, linearly improving the feed conversion ratio with the in-supplementation level. Total feed intake (TFI) was not affected. Growth hormones and total serum protein levels also linearly increased with the FO level, while albumin was elevated in the FO600 group. Moreover, total globulins increased linearly in FO400 and FO600 treatment groups. Thyroxin hormone (T3 and T4) levels increased in all FO treatment groups without affecting glucose and leptin serum values. Different concentrations of FO supplementation in the diet increased the activities of Complement 3, lysozyme, and interleukin 10 levels in the serum. Dietary FO in broilers increased the total percentage of n-3 and n-6 fatty acids. It also increased the ratio of n-3 to n-6 linearly and quadratically. Additionally, FO supplementation led to the upregulation of immune clusters of differentiation 3 and 20 (CD3 and CD20) in the spleen, along with improving most of the morphometric measures of the small intestine. In conclusion, FO up to 600 mg kg−1 as a feed additive in broiler chicken production is valuable for promoting their growth, intestinal histomorphology, and immune status along with enriching breast muscle with polyunsaturated fatty acids (PUFA). ## 1. Introduction For an extended period, essential oils from aromatic and medicinal plants have been widely employed in poultry production due to their helpful pharmacologically active components and their minimum side effects, as mentioned in the WHO’s recommendations [1,2]. One such compound is Frankincense, which contains numerous biologically active constituents [3]. Frankincense is obtained from the Boswellia genus that belongs to the Burseraceae family. It is an aromatic resin that solidifies to form a yellow–brown granular substance, identified as olibanum [4]. Commercially, it is traded as granules, pellets, or powder [5]. Numerous species of Boswellia have been identified, including Boswellia sacra, B. frereana, B. Serrata (B. thurifera, Indian frankincense), and B. papyrifera. These species produce frankincense oil with different compositions, which largely depend on the geographical source, conditions of harvesting, and climate [6]. Enrichment of poultry diets with frankincense (Boswellia serrata) as a natural product exerted many beneficial effects on productive performance, including increased body weight, the efficiency of feed consumption and absorption, enhanced meat quality, i.e., meat production with low-fat content and no residue, along with increased serum levels of globulin, lymphocyte numbers, and immunity [7]. The principal constituent of frankincense resin is its oil content ($60\%$), which includes monoterpenes ($13.1\%$), sesquiterpenes ($1\%$), and diterpenes ($42.5\%$). FO encourages pancreatic enzyme secretion, increases protein and energy digestibility, and reduces nitrogen, ammonia, and microbial metabolite losses [8]. A decrease in the uric acid level in broilers’ blood by FO suggests high efficiency in protein absorption and lower loss of endogenous protein [7]. FO also exhibits immunostimulatory and antibacterial activity against many gram-positive and gram-negative bacteria [9,10]. Moreover, broilers’ diet supplementation with Frankincense positively influences the microbes (microbiome) in the gastrointestinal tract, increasing the counts of beneficial bacteria Lactobacillus and Enterococcus but decreasing the pH of the digestive tract. During subclinical infection, this reduces microorganisms’ energy and protein consumption in the host, therefore, enhancing growth and minimizing the flux of ammonia and immune intercessor [11,12]. In vitro, Boswellia serrata oil exhibits antimicrobial activities with maximum inhibition zone [13,14,15], antibacterial and antifungal activities [16,17], anti-inflammatory and anti-diarrheal activities associated with anti-cholinergic effects [18], hepatoprotective and antioxidant activities [5,15], and neuroprotective activity [19,20]. It is also helpful for asthma patients as it eases breathing and has a calming effect during cough, cold, and inflammation of bronchus and larynges [21]. These beneficial effects are associated with several antioxidant compounds present in FO, including mono and diterpenes, ethyl acetate, octyl acetate, and methyl anisole [11]. However, there is a lack of studies exploring FO supplementation on broiler performance. Thus, our study determined the effect of FO dietary supplementation on selected performance parameters of broiler diets, such as intestinal histomorphology, the immune status of birds, the fatty acid profile of breast muscle, blood biochemical parameters, and immunoexpression of CD3 and CD20 in the spleen. The study replicated a typical broiler production system within an experimental facility, thus providing a practical basis for interpretation and potential applications. ## 2.1. Gas Chromatography–Mass Spectrometry (GC-MS) Analysis of FO Frankincense resin (Boswellia serrata) oil was obtained from Organic Egypt Company (Cairo, Egypt). The active compounds of FO were determined using a Trace GC1310-ISQ Mass Spectrometer (Thermo Scientific, Austin, TX, USA), with a direct capillary column TG–5MS (30 m × 0.25 mm × 0.25 µm film thickness), following the previous description of Amer, et al. [ 22]. ## 2.2. Birds The experiments were performed at the Faculty of Veterinary Medicine in the Poultry Research unit, Zagazig University, Egypt. The Ethical approval for the experimental protocol was obtained from the Institutional Animal Care and Use Committee of Zagazig University, Egypt (Approval No. ZU-IACUC/2/F/$\frac{152}{2022}$). We obtained 400, one-day-old, Ross 308 broiler chicks from a local hatchery. Chicks were reared in an open, well-ventilated house with sawdust bedding (7 birds/m2). During the first week, the building temperature was set at 34 °C, which was reduced gradually to 25 °C at the end of the experiment. Initially, the illumination regime was set to 23 h light/1 h dark condition and then changed to 20 h light/4 h dark condition until the end of the experiment. Standard health and vaccination programs were implemented against Newcastle and Gumboro diseases. The chicks were monitored daily for any health problems. ## 2.3. Experimental Design and Diets On arrival, chicks were exposed to a three-day adaptation period, wherein they were fed a control diet to attain an average initial weight of 99.18 ± 0.14 g. Next, the birds were randomly assigned into four treatment groups (100 chicks each; ten replicates/group; ten chicks/replicate). The experimental groups were as follows: T1 (control group), a basal diet without FO addition (FO0); T2, basal diet + 200 mg FO/kg (FO200); T3, basal diet + 400 mg FO/kg (FO400); T4, basal diet + 600 mg FO/kg (FO600). FO was mechanically mixed with the feed ingredients and offered to the birds in a mashed form. The experiment lasted for 35 days. The feeding period was divided into the following three periods: starter (4th–10th day), grower (11th–23rd day), and finisher periods (24th–35th day). Throughout the experiment, feed and water were added ad libitum. We formulated the ration for each feeding period (starter, grower, and finisher) according to Ross’s manual guide [23], which is illustrated in Table 1. ## 2.4. Growth Performance To calculate the average initial BW, birds were weighed individually on their 4th day, and then to determine their BW and BWG; they were reweighed at 10, 23, and 35 days. The feed intake (FI) was calculated as the difference between the amount of feed offered and the amount of feed residue left at the end of each feeding period, which was divided by the number of birds in each replicate. The feed conversion ratio (FCR) was computed as per the following equation:FCR=FIBWG ## 2.5. Percentage Calculations of the Dressing, Internal Organs, and Immune Organs To calculate the percentage of dressing, nine chicks were chosen from each group, weighed, and euthanized using cervical dislocation [24]. The carcasses were plucked, eviscerated, and weighed to determine the carcass weight. The percentage of dressing was determined as follows:Dressing % = Carcass weight (g)/Live BW (g) × 100. While the percentages of the internal organs (liver, gizzard, and intestine) and immune organs (spleen and bursa of Fabricius) were determined as follows:Weights of the organs (g)/the live weight (g) × 100. ## 2.6. Sampling For further analyses, three birds were randomly chosen from each replicate ($$n = 30$$/group) and euthanized using cervical dislocation [24]. Blood samples ($$n = 30$$/group) were collected into sanitized tubes without anticoagulant and were allowed to clot at room temperature. The tubes were then centrifuged at 3500 rpm for 15 min to separate the serum. The separated serum samples were stored at –20 °C until further chemical analysis. Samples from breast muscles ($$n = 5$$/group), intestinal samples from the duodenum, jejunum, and ileum ($$n = 10$$/group, 2 cm), and spleen samples ($$n = 10$$/group) were collected for fatty acid analysis, histomorphological examination, and immunohistochemistry, respectively. ## 2.7. Fatty Acid Analysis of the Breast Muscle For the fatty acid analysis, five breast muscle samples (50 g/sample) were collected from each group. A chloroform/methanol (2:1, v/v) solvent method, as described by Belitz et al., was used to extract fat [2].The extracted fatty acids were then measured according to AOAC [25]. ## 2.8. Intestinal Histology and Morphometric Measures Intestinal specimens were stored in $10\%$ neutral buffered formaldehyde for 72 h and then dehydrated using an ascending grade of ethanol (75–$100\%$). Next, they were treated with xylol I and II and later embedded in paraffin. Finally, the samples were sliced into 4 µm longitudinal and cross-sections using a microtome (Leica RM 2155, England). Slides were stained using Hematoxylin and Eosin (H&E) [26]. The morphometric dimensions were measured as per the description of Amer, et al. [ 27]. ## 2.9. Blood Biochemical Parameters The total serum protein level was determined according to the procedures of Grant [28]. The albumin level was assessed according to Doumas, et al. [ 29]. The serum globulin level was computed by subtracting albumin from total protein values as per Doumas, et al. [ 30]. The serum glucose value was evaluated using an automatic biochemical analyzer (Robotnik Prietest ECO, India) [31]. The hormones of the thyroid gland (triiodothyronine (T3) and thyroxin (T4)), leptin, and growth hormones (GH) were assessed using chicken ELISA kits (My BioSource Co., San Diego, CA, USA, with Cat. No. MBS269454, MBS265796, MBS025331, and MBS266317, respectively) following the manufacturer’s instructions. ## 2.10. Immunological Parameters Interleukin 10 (IL10) was quantified using a specific ELISA assay kit (MyBioSource, San Diego, CA, USA) (Cat. No. MBS701683). C3 level was determined using another ELISA kit (Life Span Biosciences, Inc., Seattle, WA, USA) (Cat. No. LS-F9287). Lysozyme activity was measured as per Lie, et al. [ 32]. ## 2.11. Immunohistochemical Examination We carried out immunohistochemical staining for CD3 and CD20 in the spleen tissues according to Saber, et al. [ 33]. Slides were first treated with mouse anti-chicken CD3, clone CT-3 (Bio-Rad Lab., Dubai, United Arab Emirates), and CD20 (ThermoFisher Scientific, Waltham, MA, USA) and then assessed as per Amer, et al. [ 27]. The intensity was expressed by the average grayscale [34]. ## 2.12. Statistical Analysis The data were analyzed using SPSS Version 16 for Windows (SPSS Inc., Chicago, IL, USA). Based on polynomial orthogonal contrasts, one-way ANOVA was applied to calculate linear and quadratic regression equations. The differences between experimental groups were expressed as the mean ± standard deviation (SD) and determined using Duncan’s multiple-range test [35]. The statistical significance of the results was set at ($p \leq 0.05$). ## 3.1. Determination of Bioactive Compounds in FO Table 2 and Figure 1 list the bioactive compounds in FO identified by GC–MS. The main bioactive compounds include farnesol ($12.42\%$), ç-elemene ($12.42\%$), à-farnesene ($12.42\%$), phenol, bis (1,1-dimethylethyl) ($7.15\%$), phenol, 2,4-bis (1,1-dimethylethyl) 2, 4-di-tert-butylphenol ($7.15\%$), phenol, 3,5-bis (1,1-dimethylethyl) ($7.15\%$), 3-thujanol ($3.62\%$), and 10-undecyn-1-ol ($3.50\%$). ## 3.2. Growth Performance Table 3 demonstrates the influence of FO supplementation on broilers’ production parameters. In the FO200 group, the BW and BWG were quadratically raised ($$p \leq 0.04$$) throughout the starter period. At different FO concentrations, the FCR decreased linearly ($$p \leq 0.04$$) and quadratically ($$p \leq 0.02$$) with no effect observed on the FI. During the grower period, the BW increased linearly ($$p \leq 0.001$$) and quadratically ($$p \leq 0.02$$), while the BWG ($$p \leq 0.001$$) and FCR ($$p \leq 0.002$$) improved linearly at different FO supplementation levels without any effect on the FI ($p \leq 0.05$) as compared to the FO0 treatment. Different concentrations of FO linearly and quadratically increased the BW and BWG. Compared with the FO0 treatment, FO supplementation decreased FCR ($p \leq 0.01$) throughout the finisher and overall periods with no effect on the total FI. The final body weight was the highest in the FO200 group and the lowest in the FO0 group ($p \leq 0.05$). ## 3.3. Percentages of the Dressing, Internal Organs, and Immune Organs FO supplementation at 200, 400, or 600 mg/kg diet showed no linear or quadratic effect on the percentages of dressing, liver, intestine, gizzard, spleen, and bursa of Fabricus compared to the live weight of birds ($p \leq 0.05$) (Table 4). ## 3.4. Fatty Acid Composition of Breast Muscle Different concentrations of FO Supplementation in broilers linearly increased ($p \leq 0.01$) the percentages of α-linolenic acid (18:3 n-3), eicosapentaenoic acid (20:5 n-3), docosapentaenoic acid (22:5 n-3), docosahexaenoic acid (22:6 n-3), and arachidonic acid, with no effect on the percentage of linoleic acid (18:2 n-6). Compared with the FO0 group, all the FO-supplemented groups showed an increase in the total percentage of n-3 (linear $p \leq 0.01$, and quadratic $$p \leq 0.03$$) and n-6 fatty acids (linear $p \leq 0.01$, quadratic $$p \leq 0.01$$), and also an increased ratio of n-3 to n-6 (linear $p \leq 0.01$, quadratic $$p \leq 0.04$$), with the highest percentage shown at 600 mg/kg diet (Table 5). ## 3.5. Histological Examination Table 6 and Figure 2 illustrate the effects of FO supplementation on the GIT histology of broilers. While FO200 and FO400 groups showed quadratically increased ($p \leq 0.01$) duodenal villus height (VH), FO400 and FO600 groups showed decreased duodenal villus width (VW) (linear $p \leq 0.01$ and quadratic $$p \leq 0.04$$). In all experimental groups, duodenal crypt depth (CD) was found to be quadratically increased ($p \leq 0.01$). Compared with the FO0 group, muscular coat thickness (MCT) was increased ($p \leq 0.01$) in the FO200 and FO400 groups but decreased in the FO600 group. Jejunal VH showed a non-significant increase in the FO200 group and an increase ($p \leq 0.01$) in the FO400 and FO600 groups compared with the control (FO0). Therefore, dietary FO supplementation linearly and quadratically increased (p ≤ 0.01) different jejunum morphometric measures (VW, CD, and MCT). Ileal VH was decreased ($p \leq 0.01$) in the FO200 and FO600 groups but increased in the FO400 group. FO200, FO400, and FO600 groups showed a linear and quadratic increase in ileal VW and CD. Compared with the control group, ileal MCT increased (p ≤ 0.01) in the FO200 group but decreased in the FO400 group. Routine H&E revealed a moderate number of goblet cells in the duodenum of broilers in FO400 and FO600 (16, 17 cells/HPF, respectively) groups but high numbers in FO0 and FO200 (25, 38 cells/HPF, respectively) groups. ## 3.6. Serum Biochemical Parameters The effect of FO supplementation on the biochemical parameters of broilers is presented in Table 7. Different concentration levels of FO supplementation increased the total protein and growth hormone levels in broilers linearly ($p \leq 0.01$). Simultaneously, serum albumin showed an increase ($p \leq 0.01$) in the FO600 group, with insignificant improvement in other treatment groups. Moreover, total globulins increased linearly ($$p \leq 0.001$$) in the FO400 and FO600 groups, with insignificant improvement in the FO200 group. The T3 and T4 hormones also increased linearly ($p \leq 0.01$). These changes were more significant in the FO600 group, followed by the FO400 group. However, glucose and leptin serum levels were not altered ($p \leq 0.05$). ## 3.7. Immunological Parameters The influence of FO on the immune status of birds is given in Table 8. Compared with the FO0 group, all FO treatments increased the levels of lysozymes, complement 3, and interleukin 10 linearly and quadratically (p ≤ 0.01). The best results were observed at the highest supplementation level (FO600). ## 3.8. Immunohistochemical Examination Morphometric analysis of the spleen sections taken from different experimental groups revealed the following average percentage of positive cells (from three high power fields (HPF)) to CD3 T-cell marker: 1.39, 5.34, 15.9, and 26.3 for FO0, FO200, FO400, and FO600 groups, respectively (Figure 3 and Figure 4). Similarly, the average percentage of positive cells (from three high power fields (HPF)) to CD20 B-cell marker exhibited the following values: 17.4, 28.84, 32.61, and 43.25 for FO0, FO200, FO400, and FO600 groups, respectively (Figure 3 and Figure 5). ## 4. Discussion The main bioactive compounds identified in FO by GC–MS were farnesol, sesquiterpene constituents, ç-elemene, and α-farnesene along with phenolic compounds, such as bis (1,1-dimethylethyl), phenol,2, 4-bis (1,1-dimethylethyl) 2, 4-di-tert-butylphenol, and phenol, 3, 5-bis (1,1-dimethylethyl). Our results revealed a positive effect of dietary FO supplementation on BWG and FCR of broiler chickens without impacting their feed intake. The most significant results were observed in the group supplemented with 200 mg/kg FO, followed by the group supplemented with 400 mg/kg FO. These inclusive improvements in the growth parameters may be attributed to the following various reasons: [1] the overall good health of birds and improved gastrointestinal tract morphology suggested by an increase in villus height, crypt depth, and absorptive surface area [11,36]; [2] improved absorption of essential nutrients (calcium, phosphorus, and iron) [37]; [3] stimulated activities of gastrointestinal tract enzymes, reduced gas flow, and enhanced gastric juice secretion and flow, leading to increased nutrient digestibility of dry matter and organic matter [38]. Furthermore, the improved growth reported in this study could be due to the increased secretion of growth and thyroid hormones along with improved intestinal histomorphology. Improved feed efficiency and performance of broilers supplemented with Boswellia serrata (BS) were the results of the better configuration of intestinal villi, gastric microflora, and the overall health of broilers [11,39]. Moreover, BS supplementation in broiler diets at 0.5, 1, and 1.5 g/kg increased their body weight and weight gain linearly; therefore, improving their FCR linearly and quadratically [40]. Previous studies have shown that enriching rabbit diets with 0.25, 0.50, 0.750, and 1.00 g/kg BS improved their BWG and FCR [41]. However, this was in contrast with other researchers who observed a non-significant effect of olibanum (Boswellia thurifera) supplementation at 0.01, 0.015, 0.02, 0.03, or $0.05\%$ and BS resin supplementation at 1.5, 2, or $2.5\%$ on the performance parameters of broilers [36,42]. Moreover, Tabatabaei, et al. [ 43], reported that during the grower period, broilers supplemented with $0.5\%$ BS exhibited the lowest FCR compared with the control birds. Our results revealed that supplementation of diets with 200, 400, or 600 mg/kg FO showed no linear or quadratic effect on the percentages of dressing, internal organs, and immune organs’ weight. These findings conferred with the results of Ismail, et al. [ 41], who confirmed that most traits relating to the composition of rabbit carcass and edible organs were insignificantly affected by diets supplemented with BS. However, our results disagree with that of Mohamed, et al. [ 40], who reported that supplementing different BS levels to broiler diets improved relative weights of the liver, heart, spleen, bursa, and thymus gland while quadratically increasing the relative weights of gizzard and giblet compared with the control. Moreover, Al-Yasiry, et al. [ 42] reported good carcass quality of chickens fed with 2.0–$2.5\%$ BS-containing diets compared with that of non-treated chickens. The difference between our results and the previous results may be attributed to the form of the additive. While we used BS oil, the previous studies used the whole plant, which definitely differed in composition. Enriching broiler diets with PUFA leads to improved meat quality [44]. Various research studies have been conducted to change the fatty acid content in poultry meat [45,46]. Enriching broiler diets with herbal extracts and oils has received much attention due to their application in enhancing production parameters and poultry health [47]. In this work, dietary FO supplementation enriched the breast muscle with n-3 PUFA, mainly the α-linolenic acid, eicosapentaenoic acid, docosapentaenoic acid, and docosahexaenoic acid, and also n-6 PUFA, particularly the arachidonic acid. It also increased the n-3/n-6 ratio, favoring the acceptance of consumers. The positive effect of FO supplementation on the fatty acid composition of breast muscles may be attributed to different boswellic acids, terpenoids, polyphenols and flavonoids of BS. These can improve the composition of fatty acids, and thereby, the quality of meat [48]. The fatty acid content in broilers’ meat is affected by their diet composition [49] and genetic structure [50]. Our results were in line with the results of previous research in broilers, which demonstrated the positive effect of 1.5, 2, or $2.5\%$ BS resin supplementation on the percentage of PUFA, the sum of total fatty acids, n-3/n-6 saturation, hypocholesterolemic/hypercholesterolemic ratio in breast, abdominal fat, and drumstick muscles in broilers [48]. Furthermore, Nkukwana, et al. [ 51] showed that diets supplemented with Moringa oleifera leaves increased fatty acids in poultry meat, while broilers fed with Lippia javanica, consisting of the highest terpenes, showed increased levels of oleic acid content in their drumstick muscle [52]. The small intestine in poultry animals is an important gut organ required for nutrient digestion and absorption. In this work, supplementing broilers’ diets with different concentrations of FO increased different morphometric measures of the duodenum and jejunum (VH, VW, CD, and MCT), ileal VW, CD, and MCT. The ileal VH was reduced in the FO200 and FO600 groups but increased in the FO400 group. Lower VH with a linear increase in the VW of the ileum indicated that with the increase in the FO levels, the absorptive surface area also increased, which has the advantage of more nutrient absorption. Moreover, polyphenols and some terpenes decrease extreme oxidative stress by reducing plasma lipid peroxidation [53]. These bioactivities enhance gut health, thus improving the overall health of the animal. Several aromatic plant biostimulants have been reported to enhance intestinal morphology and expression of tight junction proteins, benefiting animals [54,55,56]. BS resin supplementation in broiler diets at 2 or $2.5\%$ levels decreased crypt depth and increased the ratio of villus: crypt without altering VH, while BS levels at 3 and $4\%$ increased duodenal length [54]. Tabatabaei [36], reported that adding different increments of olibanum at 0.01, 0.015, 0.02, 0.03, and 0.05 increased VH and crypt depth of duodenum and jejunum insignificantly but increased ileum VH significantly. The epithelium turnover was positively affected by the increase in intestinal villi height and villus crypt ratio. The reduction in intestinal crypts indicated a decrease in the exchange of enterocytes and also a lower requirement for tissue development [55,56]. Although the experimental data showed no effect of FO supplementation on serum levels of glucose and leptin, an increase was observed in the serum levels of albumin, total globulins, total proteins, thyroxin, and growth hormones. T3 and T4 are produced by the thyroid gland and are essential for regulating many metabolic and feeding processes. They also control the gain rate through several metabolic mechanisms [57]. Higher serum levels of T3 and T4 hormones may result in greater BWG [58]. The anterior pituitary gland’s somatotroph cells secrete growth hormone (GH), which is essential for numerous biological activities. It manages animal growth and the progression of tissue levels [59,60]. The results of growth and thyroid hormones in our study explain the improved growth by FO. A higher globulin level is a valuable index for higher immune response and antibody production [39,61]. Boswellia serrata encourages the function of the thyroid gland, leading to the upregulation of metabolism and an increase in the basal metabolic rate. Our results are consistent with previous results, where the supplementation of BS at 0.5, 1, and 1.5 g/kg increased the globulin level in broilers [40,42]. Additionally, the enrichment of drinking water with different concentrations of Frankincense powder at 0.25, 0.50, 0.75, and 1 g/Liter significantly increased the plasma concentration of total proteins [62]. In contrast to our work, the supplementation of Boswellia serrata resin (BSR) in broiler diets at 1.5, 2, or $2.5\%$ did not alter the values of total serum levels of protein, albumin, and globulins [39]. Another study confirmed that rabbits fed with a BS-enriched diet showed lower albumin levels and A/G ratio compared with the control diet [41]. Our investigation showed an increase in the activities of lysozyme, complement 3, and interleukin 10, with the highest increase observed in the uppermost supplementation level (FO600). Due to its effectiveness and well-established role in the immune process, lysozyme is considered an essential component of non-specific humoral immunity; it has a bactericidal impact and can activate the complement system and phagocytic activity, leading to the destruction of the glycosidic bonds of E.coli and Staphylococcus walls, preventing infection and disease [63]. Lymphocyte subpopulations are identified by specific cell surface biomarkers. After antigen recognition, the T-cell co-receptor CD3 initiates a signaling cascade that activates helper and cytotoxic T cells [64]. A B-lymphocyte surface antigen, CD20, regulates B-cell activity, differentiation, and proliferation [65]. The present study showed that FO supplementation in broiler diets led to significant upregulation of the immunoexpression of CD3 and CD20 genes in the spleen tissue. These results indicate the immunomodulatory effects of FO supplementation in broiler chickens. FO supplementation results in the activation of B- and T-lymphocytes and the production of IgG and IgM antibodies that protect the body from bacterial and viral infection [5,66]. Mikhaeil, et al. [ 12] performed a lymphocyte proliferation assay and reported an intense immunostimulant activity of FO, which revealed $90\%$ lymphocyte transformation. Undoubtedly, the immunomodulatory effect of FO is due to its bioactive compound profile. Farnesol, a bioactive compound in FO, has exhibited antibiofilm, fungicidal, antitumor, and anticancer properties [67,68]. Sachivkina, et al. [ 69] showed that farnesol increased the resistance against yeast-like fungi, suggesting its ability as an antimicrobial compound [70,71,72,73]. Moreover, farnesol stimulates the NF-kB pathway via MEK$\frac{1}{2}$-ERK$\frac{1}{2}$-MSK1-dependent phosphorylation of p65, consequently stimulating cytokine production, including IL-6 and IL-1α [74]. Additionally, sesquiterpene constituents, such as c¸-elemene and α-farnesene, exhibit immunomodulatory effects by modulating the anti-inflammatory response through the inhibition of prostaglandin, lipoxygenase, and leukotriene biosynthesis [75]. It is well known that terpenes inhibit bacterial cell division [76] and are quorum sensing (QS) inhibitors. QS is an intracellular bacterial communication system that permits various activities, for example, biofilm formation and expression of virulence factors [77]. Phenol-2, 4-bis (1,1-dimethylethyl) is used as an antioxidant, UV stabilizer, or light protection agent [78,79]. Besides its antimicrobial activity [79,80,81,82], Ren, et al. [ 78] confirmed that phenol-2, 4-bis (1,1-dimethylethyl) from Pseudomonas fluorescens TL-1 possessed antifungal activity. In ethanol-induced gastric damage models, phenol, 3, 5-bis (1,1-dimethylethyl) and phenol-2, 4-bis (1,1-dimethylethyl) were established to exhibit anti-inflammatory and gastroprotective activities [76]. ## 5. Conclusions Our results have suggested that supplementing broiler diets with up to 600 mg kg−1 of FO enhances growth performance by stimulating the secretion of growth and thyroid hormones and improving intestinal histomorphology. Dietary FO enhances the birds’ immunity by increasing serum levels of lysozyme, interleukin 10, and complement 3 and also by increasing the expression of the immunolabelling indexes of CD3 and CD20. Dietary FO enriched the breast muscles of birds with n-3 and n-6 PUFA. Thus, FO can be used as a native growth promoter and immune-stimulating agent for broilers. In light of serious issues such as antimicrobial resistance in global animal production, the use of functional feed additives is of paramount interest. Our study on broilers validates the prophylactic benefits of FO in the livestock industry. ## References 1. Abd El-Hack M.E., Alagawany M.. Performance, egg quality, blood profile, immune function, and antioxidant enzyme activities in laying hens fed diets with thyme powder. J. Anim. Feed Sci. (2015.0) 24 127-133. DOI: 10.22358/jafs/65638/2015 2. Belitz H.-D., Grosch W., Schieberle P.. Meat. Food Chemistry (2009.0) 563-616 3. 3. 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--- title: 'Global research trends and hotspots analysis of hallux valgus: A bibliometric analysis from 2004 to 2021' authors: - Shulong Wang - Ping Deng - Xiaojie Sun - Jinglu Han - Shanshan Yang - Zhaojun Chen journal: Frontiers in Surgery year: 2023 pmcid: PMC10044137 doi: 10.3389/fsurg.2023.1093000 license: CC BY 4.0 --- # Global research trends and hotspots analysis of hallux valgus: A bibliometric analysis from 2004 to 2021 ## Abstract ### Background Hallux valgus (HV) is a common foot and ankle surgery disease. The correction of HV deformity relies on a highly challenging surgical treatment. Thus, widely adopted evidence-based clinical guidelines are still needed to guide the selection of the most appropriate interventions. Recently, the study of HV has been growing and scholars are increasingly paying particular attention to this area. However, bibliometric literature remains lacking. Therefore, this study aims to reveal the hotspots and future research trends in HV via bibliometric analysis to fill this knowledge gap. ### Methods Literature related to HV from 2004 to 2021 was retrieved from the Science Citation Index Expanded (SCI-expanded) of the Web of Science Core Collection (WoSCC). Quantitative and qualitative analyses of scientific data are performed using software such as CiteSpace, R-bibliometrix, and VOSviewer. ### Results A total of 1,904 records were identified for analysis. The United States had the most number of published articles and total citations. Thus, the United States has made an essential contribution to the field of HV. Meanwhile, La Trobe University in Australia was the most productive institution. Menz HB and Foot & Ankle International were the most influential authors and the most popular journals among researchers, respectively. In addition, “older people,” “chevron osteotomy,” “Lapidus,” and “hallux rigidus” have always been the hotspots of attention. Changes and developments in the surgery of HV have gained researchers' interest. Future research trends are more focused on “radiographic measurement,” “recurrence,” “outcome,” “rotation,” “pronation,” and “minimally invasive surgery.” Thus, focusing on these subject directions can facilitate academic progress and provide the possibility of better treatments for HV. ### Conclusion This study summarizes the hotspots and trends in the field of HV from 2004 to 2021, which will provide researchers with an updated view of essential information and somehow guide future research. ## Introduction Hallux valgus (HV) is a common foot and ankle surgery disease, often with symmetrical onset [1, 2]. The main clinical manifestations of HV are the lateral deviation and displacement of the hallux at the first metatarsophalangeal joint accompanied by pain and deformity [3]. However, the pathogenesis of HV remains unclear, which may be closely related to abnormal bone structure, soft tissue imbalance, shoe-wearing habits, heredity, and other related factors [4, 5]. The incidence of HV was approximately $23\%$ in adults aged 18–65 years and $35.7\%$ in those over 65 years old [6]. With progressive subluxation of the first metatarsophalangeal (MTP) joint, foot function is disrupted, thereby resulting in postural instability and an increased risk of falls in older adults [7]. Orthopedic surgery is the primary treatment modality for HV. HV correction surgery is one of the 10 most common foot and ankle surgeries in the United States [8]. More than 150 surgical procedures have been used to treat HV, but most have now been eliminated considering that surgical modalities are constantly being updated. Therefore, percutaneous or minimally invasive procedures are gaining popularity, but have been controversial compared to traditional open osteopathic techniques (9–11). Despite the high prevalence of HV and the associated quality of life impairments, no widely adopted evidence-based clinical guidelines can guide the selection of the most appropriate surgical approach [12]. While some systematic reviews and meta-analyses focus on a specific aspect of HV, the proliferation of scientific publications has made it increasingly difficult for researchers to keep track of recent discoveries and capture the latest hotspots and trends, even within their fields of expertise. The bibliometric analysis provides a complete overview of a huge body of study literature [13]. It expands previously unappreciated insights by providing quantitative and objective identification of historical and current research subjects [14, 15]. Bibliometric methods have been increasingly used in medicine over the past few years. However, only few bibliometric studies in the field of HV research have been conducted. Ferreira GF's article focused on a general knowledge framework [16]. Accompanied by continuous improvements in bibliometric software, the number and methodologies of the included literature, and the content of the analysis, sophisticated visualization tools are essential in HV applications, especially in research direction diversification to search for global research trends and hotspots. Therefore, this paper primarily aims to: (I) analyze the general knowledge structure and major contributors (country, institution, author, and journal); (II) clarify the research topic, research hotspots, and research directions; and (III) anticipate future research trends. ## Data source and search strategy Web of Science Core Collection (WoSCC) is the world's largest comprehensive academic information resource covering the broadest range of disciplines [17, 18]. Hence, it is widely used in the scientific fields as one of the most regularly used databases for bibliometrics [19]. This study used the Science Citation Index Expanded (SCI-expanded) of the WoSCC despite the availability of other scientific databases, such as Scopus, PubMed, or EMBASE. However, different databases include different methods of forming literature, export formats of documents, and so on, and thus it may not be appropriate to combine multiple data from other databases [20]. First, this study used the subject term “hallux valgus” to search articles and reviews published from 2004 to 2021. The following were the search terms: TS = (“hallux valgus” OR “bunion” OR “hallux abduct” OR “metatarsus primus varus”). However, only the English language was accepted. All retrievals were completed and downloaded on September 20, 2022. ## Inclusion and exclusion criteria Following the above search strategy, retrieved datasets were exported as “full record and cited references” for further analysis. The titles, abstracts, and keywords of the datasets were read one by one. Inclusion criteria were HV-related articles and reviews (e.g., epidemiological investigations, clinical treatment, recurrence, outcome, etc.) containing all study types. Meanwhile, exclusion criteria were articles and reviews unrelated to HV studies (including disease type, research purposes, interventions, outcome indexes, etc.). The data collection and entry are independently determined by two authors (WS and PD). The disagreement was a consensus reached by the third author (XS). After pre-processing, a total of 1,904 papers in the field of HV were obtained, including 1,771 articles and 133 reviews. The downloaded information was saved in text format. Informed consent was not necessary because these were secondary data with no personal information. The retrieval process was illustrated in Figure 1. Figure 1: Retrieval strategy and number of publications. ## Data analysis A suite of tools for sociometric quantitative research is provided by the Bibliometrix R package. Massimo Aria created Biblioshiny for the secondary development of the Bibliometrix-based Shiny package in R [21]. It encapsulates the core code of Bibliometrix and generates a web-based framework for online data analysis. Herein, users can use the interactive web interface to perform relevant scientific measurement and visual analysis work, which reduces the user usage threshold and information input intensity to a certain extent [22]. In addition, VOSviewer and CiteSpace were widely used for bibliometric analysis. Bibliometric analysis was primarily performed using three specific programs from Rstudio's Bibliometrix R package (version 2022.03.10, RStudio team, Boston, MA, United States). This software allows the construction and visualization of bibliometric networks to facilitate research understanding. Particularly, the distribution of each component analyzed in the bibliometric analysis is evaluated by a software package that applies machine learning. Given this, the following variables were used: annual scientific production, most relevant sources, source dynamics (occurrences cumulate, the number was set to 10), source local impact by H-index or total citations (number of sources was set to 10), most relevant authors, top authors' production over time (number of authors was set to 10), author local impact, most relevant affiliations, collaboration network by affiliations, country scientific production, collaboration network by countries, corresponding authors' country, historical direct citation network (number set to 20 nodes), multiple correspondence analysis of high-frequency keywords, tree dendrogram of hierarchical cluster analysis of keywords, and thematic map. CiteSpace (version 5.7R5W, Drexel University, Philadelphia, PA, United States) was utilized to analyze the following: [1] a dual-map overlay of journals contributed to publications; [2] the top 20 co-citation references with the most citation bursts; [3] timeline distribution of keywords cluster analysis; and [4] the top 20 keywords with the most citation bursts [23]. CiteSpace parameters are defined as follows: period (2004–2021), years per slice [1], selection criteria (top 20), and pruning (minimum spanning tree, pruning sliced networks). Moreover, VOSviewer (version 1.6.17, Leiden University Science and Technology Research Center, The Netherlands) was primarily applied to conduct visualization networks including author co-authorship analysis, cluster visualization of the journal co-citation analysis, and the overlay visualization map of the keywords co-occurrence analysis [24]. Nodes indicate authors, journals, keywords, and so on, and the size of nodes and the lines between nodes represent the citation or the number of publications and the interrelationship between nodes (co-authors, co-citation, or co-occurrence), respectively. ## Annual growth trends in publications Microsoft Office Excel 2019 was used to process the data and constructed a linear model to predict the number of papers published in 2022. Figure 2 showed that the number of papers published in the HV field increased in volatility from 2004 to 2021. An increase was observed with scientific publications growing at an average rate of $34.79\%$. The linear model of time prediction was established by fitting the formula. A statistically significant relationship was observed between the year and the number of papers (R2 = 0.9069) with a good fit. The number of publications in HV would reach approximately 184 in 2022 based on the fitted curve. Figure 2: A linear model fitting of publication growth in HV. ## Analysis of major countries/regions The distribution features of the significant research countries/regions indicate the influence of countries/regions in the field of HV and create conditions for ongoing growth. The different shades of color shown in Figure 3A represent the number of papers published in each country/region. The top 20 countries or regions in terms of the total publications are primarily from North America, Europe, and East Asia. Figure 3: (A) distribution of publications from different countries/regions. The color intensity is proportional to the number of documents. Higher blue intensity refers to the greater number of documents, and grey means no scientific output. (B) Countries’ collaboration network. There are 20 nodes in the picture. Each node represents a different country and the diameter of each node represents the strength of collaboration between the country and other countries, the line represents the collaboration network or path between each country, and the thickness of the line represents the strength of collaboration. (C) Institutions’ collaboration networks in HV. The Bibliometrix and Biblioshiny software packages were used to screen for cooperation between countries, as shown in Figure 3B. When the number of nodes was set to 20 and the number of cooperation was more than one, the thicker the border between the two labels, the stronger the cooperation intensity between countries, and vice versa. A country's research quality can be reflected by total citations and average total citations [25]. The United States was at the core of international collaboration and has the most published articles and total citations. Thus, the United States has made an essential contribution to the field of HV. Extensive collaboration has been established between countries in North America and Europe. As seen from Table 1, Australia had a relatively lower total number of published articles but a higher average of article citations, which may be related to Australia's La Trobe University. La Trobe University may play an essential role in enhancing national academic influence. While China and Turkey top the list of total published articles, the country's average article citations remain in a low position. The quantity and quality are not yet proportional. Table 1 \| Position \| Country \| NP \| Total Citations \| Average Article Citations \| Institutions \| NP.1 \| \| --- \| --- \| --- \| --- \| --- \| --- \| --- \| \| 1 \| USA \| 527 \| 8427 \| 15.09 \| La Trobe Univ \| 76 \| \| 2 \| UK \| 150 \| 2735 \| 18.23 \| Hosp Special Surg \| 50 \| \| 3 \| Japan \| 131 \| 1469 \| 11.21 \| Univ Barcelona \| 33 \| \| 4 \| China \| 123 \| 1121 \| 9.11 \| Med Univ Innsbruck \| 29 \| \| 5 \| Korea \| 105 \| 1231 \| 11.72 \| Univ Pittsburgh \| 26 \| \| 6 \| Turkey \| 97 \| 517 \| 5.33 \| Univ Seville \| 26 \| \| 7 \| Spain \| 94 \| 1079 \| 11.48 \| Univ N Carolina \| 25 \| \| 8 \| Australia \| 81 \| 2838 \| 35.04 \| Des Moines Univ \| 23 \| \| 9 \| Germany \| 53 \| 1209 \| 22.81 \| Singapore Gen Hosp \| 21 \| \| 10 \| Italy \| 52 \| 813 \| 15.63 \| Duke Univ \| 20 \| \| 11 \| Austria \| 48 \| 799 \| 16.65 \| Univ A Coruna \| 20 \| \| 12 \| France \| 44 \| 522 \| 11.86 \| Univ Minnesota \| 19 \| \| 13 \| Switzerland \| 41 \| 402 \| 9.8 \| Inje Univ \| 18 \| \| 14 \| Netherlands \| 32 \| 681 \| 21.28 \| Keele Univ \| 18 \| \| 15 \| Poland \| 32 \| 149 \| 4.66 \| Keio Univ \| 17 \| \| 16 \| Brazil \| 30 \| 272 \| 9.07 \| Sapporo Med Univ \| 17 \| \| 17 \| Singapore \| 28 \| 363 \| 12.96 \| Harvard Univ \| 16 \| \| 18 \| Belgium \| 22 \| 261 \| 11.86 \| Kent State Univ \| 16 \| \| 19 \| Canada \| 17 \| 177 \| 10.41 \| Osaka Med Coll \| 16 \| \| 20 \| Israel \| 17 \| 265 \| 15.59 \| Univ Utah \| 16 \| ## Analysis of major research institutions The study included a total of 2,025 institutions that made relevant contributions. In terms of number of published papers, Table 1 listed the top 20 institutions, with La Trobe University, Hospital for Special Surgery, University of Barcelona, Innsbruck Medical University, and the University of Pittsburgh being the top five. In terms of institutional cooperation, each node and the diameter of each node represent different institutions and strengths of collaboration between that institution with others, meanwhile, the line and the thickness of the line indicate the network or path of cooperation between each institution and the strength of collaboration, respectively. The number of nodes was set to 20, and 20 institutions were analyzed, as shown in Figure 3C. La Trobe University had the highest network of collaborations, followed by the Hospital for Special Surgery. La Trobe University in Australia and Keele University in the UK have established stable cooperation. However, other institutions, such as Inje University, have less cooperation. Thus, focusing on highly productive research institutions and collaborations can help researchers stay abreast of developments and frontiers in their discipline. ## Analysis of main researchers Co-author analysis is commonly used to establish a similar relationship between individuals or groups based on the number of co-author publications [26]. Figure 4A showed a network map and overlay visualization of 176 co-authors who have published more than five articles. Menz HB, Coughlin MJ, and Ellis SJ were at the center of the collaboration clusters. Table 2 displayed the ranks and they landed in the top three in terms of total number of publications, with 43, 26, and 18 publications, respectively. The H-index and total citations can be used to analyze the quality of current papers and anticipate the performance of future authors, as well as estimate the importance and broad influence of authors' cumulative research contributions (27–29). Menz HB, Coughlin MJ, and Roddy E were the three most influential authors, they are from Australia, the United States, and the United Kingdom, respectively. In HV, the productions of the top 10 authors over time were shown in Figure 4B. The node of Menz HB's most frequently cited paper appeared in 2005 (indicated by the darkest blue color in the circle) and continued to publish in 2021. Figure 4: (A) network visualization of authors co–authorship in HV. (B) Top 10 authors’ productions over time in HV. The circle size shows the number of papers, and the color from light to dark indicates the total citations per year. TABLE_PLACEHOLDER:Table 2 ## Analysis of major research journals A total of 303 academic journals published papers related to HV research were found within the analyzed time frame. Table 3 listed the names of the top 10 journals by the number of papers published in the professional journals. Foot & Ankle International has the highest number of publications ($$n = 405$$), followed by the Journal of Foot & Ankle Surgery ($$n = 264$$), Foot and Ankle Surgery ($$n = 113$$), Foot and Ankle Clinics ($$n = 79$$), and Journal of the American Podiatric Medical Association ($$n = 77$$). The top 10 journals by total number of publications were primarily from the United Kingdom, the United States, and Germany. The annual changes in the number of papers published in the top 10 journals are shown in Figure 5A. The cumulative number of papers published was 1,130, accounting for about $59.35\%$ of all documents. Therefore, the more indicators used to evaluate a journal, the more objective and comprehensive it will be. Foot & Ankle International was the journal with the highest total citations and H-index (total citations = 7,473, H-index = 41). It suggested that this journal has more academic influence and attention from researchers in the field of HV. The results of the visualization analysis of journal co-citation were shown in Figure 5B and the top 10 research categories ranked by the number of publications were shown in Figure 5C. The three major research categories of most interest in this field were orthopedics, surgery, and rheumatology. Figure 5: (A) cumulative number of publications in the top 10 journals in HV. (B) Cluster visualization of the journal co-citation analysis. (C) The top 10 research categories ranked by the number of publications. (D) The three- field plot showing the knowledge flow (country, institution, and journal). (E) The dual-map overlay of journals contributed to publications in HV from 2004 to 2021. TABLE_PLACEHOLDER:Table 3 The double-map overlay of journals reflects the citation relationships between disciplines, thereby highlighting paradigm shifts in research across disciplines [30]. In Figure 5E, the left and right sides represent the citing journals and the reference journals (cited journals), respectively. Overall, there were four colored paths, including two green trails and two pink trails. Different colored rectangles represent significant features in the figure. The height of a rectangle in a three-domain diagram depends on the ratio or value of the sum of the relationships that arise between the rectangle's components (country, institution, and journal) [31, 32]. Finally, a three-field plot was drawn, where each column is limited to 10 variables. The most productive countries collaborating with top scientific institutions and publishing academic results in international journals are shown in Figure 5D. ## Historical cited papers of HV The historical direct citation network for HV was investigated to explore the changes in the relevant research content over time. The Bibliometrix installation package in R Studio was used to perform the historical citation visualization analysis and selected 20 nodes to find essential publications and classical studies on the subject. CiteScore was released in December 2016 and is the most recent indicator. This study examines the methods and substance of classical literature using LCS and GCS. The reference scores in the downloaded dissertation dataset and WoSCC database are referred to as LCS and GSC, respectively [33, 34]. These two indicators reflect the contribution of an article to the research field from a different perspective, with a higher score indicating that the researcher values the article more. As seen in Figure 6A and Table 4, the earliest node in the literature was an article published in Foot & Ankle International in 2004 entitled “The Lapidus Procedure: A Prospective Cohort Outcome Study.” Coetzee JC. et al. analyzed that the Lapidus procedure was a great alternative to treat moderate to severe HV deformities [35]. In the same year, other authors published articles about additional surgical procedures for HV, such as Scarf and Chevron osteotomies [36]. In 2005, Menz et al. suggested that as a simple and non-invasive screening tool, the Manchester scale could effectively reflect the degree of HV deformity determined by x-ray measurement of HV and intermetatarsal angles [37]. In the same year, another of Menz et al. 's studies showed that HV has a adverse effect on gait patterns, which may lead to instability and fall risk in older adults, particularly when walking on irregular terrain. It has also gained scholars' attention to the health problems of the elderly with HV. In addition, LCS and GCS have higher scores in Robinson AHN's study. The authors thoroughly reviewed that while technically demanding, HV surgery had a high success rate in appropriately selected patients. However, few patients had a poor postoperative prognosis. Thus, randomized, controlled trials were needed to illustrate the factors that determine good outcomes [38] and a favorable validation result score was also necessary. In 2007, Coughliny MJ's article had a significant influence. Both LCS and GCS ranked second, with 178 and 244, respectively. This article stated that the severity of HV was not related to Achilles tendon or gastrocnemius tendon tightness, increased first-ray range of motion, bilaterality, or flat feet. The degree of preoperative angular deformity and increasing age were not associated with the range of motion of the first metatarsophalangeal joint [39]. In 2007, Easley ME. et al. present the first American Orthopedic Foot and Ankle Society (AOFAS) guidelines for the operative treatment of HV. His paper provided information on how to select an appropriate surgical treatment based on the severity of HV. It guided clinical and scientific researchers to treat HV better [40]. Hence, the research has gradually deepened. Researchers set out to comprehensively evaluate HV surgery in terms of recurrence and post-operative complications. In 2010, among all articles, Nix S's article had the highest LCS and GCS and was a classic document in the field of HV. HV was prevalent and became more common among aging women [6]. From the historical nodes of HV, $70\%$ of the nodes are in the period from 2004 to 2007, thereby indicating that researchers have a high degree of recognition for the literature quality of HV in this period. Various researchers focus on clinical studies of HV but note that the articles with higher scores are primarily reviews. Possibly because these high-quality papers are published by well-known research teams in high-impact journals, thereby enabling academics to rapidly and in greater detail understand the overall progress of HV and better guide clinical practice. Thus, studying this literature has led to a clearer understanding of the developmental trajectory of HV and the latest results. Figure 6: (A) historical direct citation network of HV. (B) The top 20 co-citation references with the most citation bursts. TABLE_PLACEHOLDER:Table 4 ## Mutation detection by co–citation analysis Co-citation analysis establishes a relationship between items based on how many times they have been cited together and has been demonstrated as a way to assist in identifying critical literature for cross-disciplinary ideas [41]. Co-citation analysis helps researchers understand the past research priorities and predict the research direction of HV in the future. Figure 6B listed references with an outbreak duration of at least 2 years (2004–2021). The top 20 references with the most powerful citation bursts are depicted. The three articles with the strongest citation bursts were published in 2010, 2011, and 2017. First, among all studies, Nix et al. 's study had the highest strength (24.48). His article also had the highest LCS and GCS scores in the historical node. In addition, his article concluded the prevalence of HV in the general population through systematic review and meta-analysis [6]. We also found that Menz et al. 's studies possessed a higher burst strength (14.55). He discovered that the general and foot-specific health-related quality of life decreased gradually as the severity of the HV deformity increased [42]. In addition, Lee M's article had a high burst intensity (14.33). His study pointed out that with the increasing use of minimally invasive techniques for HV, the pain was significantly reduced in the first 6 weeks after percutaneous chevron/akin procedures compared to a traditional open scarf/akin osteotomies [43]. ## Conceptual structural map In bibliometrics, cluster and multiple correspondence analyses are commonly used methods for analyzing keywords [44, 45]. As an independent tool, cluster analysis obtains the distribution of data, observes the characteristics of each cluster of data, and focuses on specific clusters for further analysis. Multiple correspondence analysis can reveal differences between categories of the same variable and correspondence between categories of different variables. Figures 7A,B showed several clustering results of correspondence analysis in the HV domain. Particularly, it can be divided into three categories. Figure 7: (A) multiple correspondence analysis of high-frequency keywords in HV. (B) Tree dendrogram of hierarchical cluster analysis of keywords in HV. The first cluster analysis (green topic): This category was primarily related to the risk factors and epidemiological investigation of HV (keywords: prevalence, pain, population, older people, and risk factors). The second cluster analysis (red topic): This category was primarily related to the diagnosis, examination, and surgical treatment of HV (Keywords: etiology, gait, ray, angular measurements, deformities, metatarsophalangeal joint, metatarsal osteotomy, bunionectomy, osteotomy, fusion, arthroplasty, and fixation). The third cluster analysis (blue topic): This category was primarily related to follow-up and recurrence after HV surgery. ( Keywords: follow-up, recurrence, angle, deformity, and metatarsal). ## Analysis of research hotspots and trends Keywords are high-level summaries of research topics and content. The timeline view of keywords cluster analysis can outline the relationship between keywords and the period of important nodes. In Figure 8A, X and Y axes represent the year when the citation was published and the clustering number, respectively [14]. Through analyzing the timeline view in the field of HV, “older people,” “chevron osteotomy,” “Lapidus,” and “hallux rigidus” have always been the hotspots of attention. Figure 8: (A) timeline distribution of keywords cluster analysis. (B) The top 20 keywords with the most citation bursts. (C) Thematic map generated was displayed in four quadrants. (D) The overlay visualization map of the keywords co-occurrence analysis. The gradual development of color from blue to yellow indicated the evolution of hotspots. In contrast to common high-frequency words, burst keywords better reflect the dynamic deduction and development mechanisms of academic research. As shown in Figure 8B, the terms “radiographic measurement,” “recurrence,” “outcome,” and “minimally invasive surgery” were the latest popular keywords in the past 5 years. Developing trends and frontiers of research areas can be obtained by analyzing thematic maps. In Figure 8C, the thematic map generated by the R-Bibliometric software package was displayed in four quadrants, and the importance and centrality of the theme were represented by X and Y axes, respectively [46]. The motor theme represented an important and well-developed theme, the niche theme represented a highly developed and isolated theme, the basic theme represented a basic area of research, and the emerging or declining themes emerge a new theme, that is, minimally invasive surgery. Co-occurrence analysis is a quantitative study of the phenomenon of co-occurrence to reveal the knowledge implied by the content associations and feature items of information. Keywords co-occurrence analysis not only identifies prevalent areas and research directions but is also an important indicator to monitor the development of scientific fields [15]. An overlay visualization map of the co-occurrence analysis using VOSviewer for 114 keywords with more than 25 occurrences was illustrated in Figure 8D. All keywords were indicated by different colors, with blue keywords and yellow keywords indicating early and late appearance, respectively. The evolution of hotspots was indicated by the gradual development of color from blue to yellow. Different bibliometric software emphasizes different aspects based on different algorithms. Thus, we comprehensively used the thematic map generated by the R-Bibliometric software package, the burst keywords of CiteSpace, and the overlay visualization map VOSviewer to identify six terms as the focus and the forefront of the field, which were “radiographic measurement,” “recurrence,” “outcome,” “rotation,” “pronation,” and “minimally invasive surgery” to comprehensively analyze the future trends in the field of HV. ## The general knowledge structure and major contributors (country, institution, author, and journal) In this study, bibliometric and visual analysis was used to identify hotspots and collaborations across different countries, institutions, and authors to better understand the global research trends and hotspots of HV. Although the number of research papers published in HV slightly fluctuated from 2004 to 2021, there was a general tendency to increase. This indicates that an increasing number of researchers focus on the topic in HV. Of the top 20 countries in terms of total publications, North America, Europe, and East Asia dominated the HV. With the rapid economic development of Asian countries, regional disparities are gradually narrowing. However, China and Turkey should also pay more attention to the quality of published papers. The United States had the most significant number of published articles and total citations and thus made an essential contribution to the field of HV. As for institutions, La Trobe University in Australia and the Hospital for Special Surgery in the United States were not only the two with the most publications but also the highest collaboration networks. La Trobe University is known for its outstanding teaching and research capabilities and enjoys a global reputation. Meanwhile, the Hospital for Special Surgery is the earliest orthopedic hospital in the United States and has been ranked first in the list of the best orthopedic hospitals in the United States for 11 consecutive years. Not only is it the cradle of medical masters, but it is also a place of pilgrimage for orthopedic surgeons worldwide. The United States accounts for nearly half of the top 20 institutions in the field of HV, which can reflect the scientific research strength of American scientific research institutions. Thus, other scientific research institutions should also strengthen cooperation and exchanges. Menz HB, Coughlin MJ, and Roddy E were the three most influential authors who might determine the focus and direction of the study. Menz HB, a foot and ankle specialist from La Trobe University in Australia, has been focusing on the research of footwear characteristics and foot problems of the elderly. Professor Coughlin MJ is an internationally renowned specialist in foot and ankle surgery who specializes in forefoot and hindfoot reconstructive surgery as well as total ankle reconstruction. Professor Roddy E's main contribution was to determine the population prevalence and factors associated with HV in the primary care population. They have been committed to research in the field of foot and ankle and made great contributions to its development. Therefore, we should place more emphasis on their work to keep up with the latest developments in this field. Few researchers are familiar with all the important journals in their area. However, they often struggle to pick the appropriate journals to publish their research results. This conclusion may be obtained from journal metrics collected through bibliometric analysis. We also observed that most of the major papers on related studies in HV were published in professional journals such as Foot & Ankle International, Journal of Foot & Ankle Surgery, and Foot and Ankle Surgery. Foot & Ankle International has the highest number of publications, total citations, and H-index in this field. In addition, as a professional journal of foot and ankle surgery published in the United States and the official journal of the American Orthopedic Foot & Ankle Society (AOFAS), Foot & Ankle International has been emphasizing clinical research related to the foot and ankle joint. The journal primarily focuses on surgery, wound care, bone healing, pain management, diabetes, and sports medicine. The Journal of Foot & Ankle Surgery is the leading source for original, clinically focused articles on foot and ankle surgery and medical management. Foot and Ankle Surgery is the official journal of the European Foot and Ankle Society. The journal aims to advance the art and science of ankle and foot surgery, thus publishing peer-reviewed research articles. The reason for the greater attention paid to these journals may lie in the following: first, these journals have a high influence in the field of foot and ankle; second, the research directions covered are closely related to the clinical research related to HV; and third, researchers are more willing to promote their research results and opinions in well-known journals to improve their academic level and scientific research ability. The dual map overlay of journals makes the interdisciplinarity of the research possible, and following the trail of the map allows tracking the development of the scientific frontier (Figure 5E). The citing journals for all publications came from two main fields: (I) Medicine/Medical/Clinical and (II) neurology/sports/ophthalmology. Meanwhile, the reference journals (cited journals) originated from (I) Health/Nursing/Medicine; (II) Sports/Rehabilitation/Sport. ## The research topic, research hotspots, and research directions Keywords and reference analysis are the essence of academic papers, which can reflect the current research hotspots and help researchers understand the evolution trend [47]. Cluster analysis helps to examine the conceptual structure of research. Multiple correspondence and cluster analyses showed three themes in the conceptual structure maps. The green topic was mainly related to the risk factors and epidemiological investigation of HV, the red topic was mainly related to diagnosis, examination, and surgical treatment of HV, and the blue topic was mainly related to follow-up and recurrence after HV surgery (Figure 7). Through analyzing the timeline view of keywords in the field of HV (Figure 8A), “older people,” “chevron osteotomy,” “Lapidus,” and “hallux rigidus” have always been the hotspots of attention. Older people are at high risk for HV, and as the degree of HV deformity increases, the overall health and foot health of older adults gradually decline. Thus, managing foot function through proper care and control of the foot is necessary to prevent the appearance or progression of HV deformities [48, 49]. The severity of the deformity determines the choice of the surgical plan. While mild-to-moderate HV is commonly treated with a distal operation like a Chevron osteotomy, more severe HV is generally treated with a proximal technique like Scarf osteotomy or Lapidus surgery. Chevron osteotomy is one of the most frequently used methods by foot and ankle surgeons worldwide, and the technique is constantly modified by the angle of the osteotomy with fixation [50]. Meanwhile, as first metatarsal wedge fusion, Lapidus surgery is indicated primarily for patients with hypermobility of the first ray [51]. Senga et al. believed that HV is a risk factor for HR (hallux rigidus). Although HV is one of the biological factors of HR, some researchers believe that current research has failed to provide a clear link [52]. More pathophysiological studies of the relationship between HV and HR may be available in the future. Through the analysis and evaluation of the research directions of the historical cited papers and the top 20 references with the most powerful citation bursts in the field of HV (Figures 6A,B), 16 ($40\%$) publications were related to the changes and development of HV surgery, 8 ($20\%$) were related to epidemiological research, 7 ($17.5\%$) were related to radiological measurement, 3 ($7.5\%$) were related to the efficacy comparison of the scoring system, and 3 ($7.5\%$) were related to the analysis of the causes of postoperative recurrence. Moreover, 2 ($5\%$) of the publications involved the impact of quality of life and 1 ($2.5\%$) research was related to gait. Changes and developments in the surgery of HV have gained researchers' interest. Although there are more than 150 surgical types for HV, most have been eliminated [53]. There are now more than 10 common procedures, which primarily include soft tissue surgery, arthroplasty, osteotomy, joint fusion, and joint replacement [54]. In recent years, minimally invasive surgery has become increasingly important and is being assessed more scientifically [55]. No operation is perfect and no one can solve all problems. Surgical plans need to be fully considered by experienced surgeons, including the severity of HV and the patient's needs [56]. It is significant to note that surgical treatment of HV has greatly advanced from traditional open surgery to the present minimally invasive treatment. ## Future research trends As HV research progresses, several emerging research directions are emerging as research topics of interest. After overlapping analysis of multiple bibliometric software (Figures 8B–D), the following seven keywords were identified as future research trends: “radiographic measurement,” “recurrence,” “outcome,” “rotation,” “pronation,” and “minimally invasive surgery.” Thus, focusing on progress in these research directions may lead to remarkable research results that will significantly advance the development and advancement of the field. ## Outcome, recurrence, rotation, and pronation The surgical treatment of HV is primarily to relieve pain and correct deformity. However, postoperative recurrence is one of the most common complications after HV surgery. Recurrence is often caused by various factors, mostly related to preoperative evaluation, intraoperative technique, postoperative management, and patient reasons [57]. Common risk factors for HV recurrence are severe HV deformity before the operation, metatarsal pronation, improper surgical selection or manipulation, and so on. Preoperative HVA (hallux valgus angle) >40°, oversized IMA (intermetatarsal angle) can increase the probability of recurrence after HV surgery. Excessive adduction, elevation, and pronation of the metatarsals are also recognized risk factors for recurrence, particularly the coronal plane was unstable [58]. HV is associated with the axial rotation of the first metatarsal, but failure to recognize and correct the malrotation of the first metatarsal may result in recurrent HV deformity. Wagner et al. concluded that identification of the anterior metatarsal rotation deformity is the key to achieving complete correction of the deformity and reducing the recurrence rate [59]. Conti MS. et al. found that the recurrence rate was significantly lower in the group with reduced first metatarsal pronation than in the group with no change/increase in first metatarsal pronation [60]. The traditional option of distal metatarsal osteotomy for severe HV deformities results in a high possibility of recurrence. Therefore, the ability to achieve good results and avoid recurrence also greatly depends on the degree of deformity correction and the surgeon's experience. Thus, individualized preoperative planning based on the patient's specific situation can effectively reduce the probability of recurrence [61]. Future research may focus on the origins of HV disease and the causes of postoperative recurrence to develop better strategies for treating HV. ## Radiographic measurement Radiographic measurement is the basic parameter and gold standard imaging modality to assess the severity of HV. Radiographs of foot weight-bearing are most commonly used in clinical practice to measure the HVA and IMA to assess the severity of HV and its degree of deformity [62]. Radiographic measurement is essential for the surgical treatment of HV, as well as the correct positioning of the foot, the experience of the investigators, and so on, which can affect the accuracy of the measurements and may lead to incorrect decisions. A randomized controlled experiment found the radiographic measurement of the HV angle to be reliable by smartphone applications for both experienced and inexperienced investigators, and these tools improved the accuracy of the HV angle measurement and thus saved measurement time [63]. With the rise of modern technology, future radiographic measurements of HV angle will be more precise and easier. ## Minimally invasive surgery Percutaneous or minimally invasive procedures are becoming increasingly popular, and the results achieved in forefoot surgery are similar to those of the traditional open approach [10, 11, 64]. Although there has been controversy about the effectiveness of minimally invasive techniques for HV, the clinical results are reliable in studies of minimally invasive surgery. Minimally invasive osteotomy has the advantages of less trauma, rapid recovery, and low complications. In a systematic review reporting a total of 1,762 patients (2,279 feet), investigators found little difference in outcomes between techniques. A meta-analysis reported that minimally invasive surgery was more effective than open surgery in the treatment of HV [65]. In addition, better radiological and clinical outcomes were obtained in the minimally invasive group than in the open group [66]. There may be some complications in minimally invasive surgery that are not present in open surgery, such as damage to soft tissue structures or skin burns that are not under direct visible control. However, minimally invasive surgery carries the risk of failure in patients with severe HV. Furthermore, minimally invasive techniques are the future trend of HV surgery treatment. With the continuous development and application of new technologies and improvement of surgical techniques, minimally invasive techniques for HV will be raised to another level, which will then provide better treatment for HV patients. ## Strengths and limitations This is the first bibliometric article to analyze hotspots and trends in the field of HV from 2004 to 2021. Nevertheless, this research has certain limitations. First, an unavoidable limitation of bibliometrics is that it may lead to incomplete searches of the papers because of the restriction of search terms. Although it may affect the precision of the study section, it is unlikely to change the outcomes. Second, the WoSCC database is the most commonly used and comprehensive in bibliometrics. Although there are other types of databases, the WoSCC database is sufficient to reflect the field and dynamics of research considering the compatibility and recognition of bibliometric software. We finally obtained research data from this database. Third, only English articles and reviews were included in the data. Finally, there may be differences between bibliometric studies and real-world research. ## Conclusions This article provides a comprehensive overview of the current status, hotspots, and trends in HV research from 2004 to 2021 through bibliometrics. The number of publications shows an overall increasing trend and the research of HV has a great research prospect. Thus, the United States has made an essential contribution to the field of HV. La Trobe University in Australia was the most productive institution. Menz HB and Foot & Ankle International were the most influential authors and the most popular journals among researchers, respectively. However, global research in HV is unevenly distributed. Thus, cooperation between countries and institutions should be strengthened. Meanwhile, “older people,” “chevron osteotomy,” “Lapidus,” and “hallux rigidus” have always been the hotspots of attention. Changes and developments in the surgery of HV have stimulated the interest of scholars. Future research trends are more focused on “radiographic measurement,” “recurrence,” “outcome,” “rotation,” “pronation,” and “minimally invasive surgery.” Thus, focusing on these subject directions can facilitate academic progress and provide the possibility of better treatments for HV. This study offers a valuable reference to help researchers, particularly new entrants, to better understand HV from a macroscopic perspective. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found in the article/Supplementary Material. ## Ethics statement Ethical review and approval was not required for the study on human participants in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements. ## Author contributions SW, PD and XS contributed equally to this work. SW and XS performed the data curation. SY and JH performed the statistical analysis. ZC performed the supervision. SW wrote the original draft of the manuscript. SW and PD reviewed and edited the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher's note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher. ## References 1. Meyr AJ. Multivariate analysis of hallux valgus radiographic parameters. J Foot Ankle Surg (2021) 61 776-9. DOI: 10.1053/j.jfas.2021.11.014 2. Sari E, Umur LF. Quality analysis of hallux valgus videos on YouTube. J Am Podiatr Med Assoc (2021) 111 5-12. DOI: 10.7547/20-191 3. Steadman J, Barg A, Saltzman CL. 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--- title: 'The association between PTPN22 C1858T gene polymorphism and type 1 diabetes mellitus: an Indonesian study' authors: - Nur Rochmah - Fatimah Arief - Muhammad Faizi - Sukmawati Basuki journal: Annals of Medicine year: 2023 pmcid: PMC10044151 doi: 10.1080/07853890.2023.2190162 license: CC BY 4.0 --- # The association between PTPN22 C1858T gene polymorphism and type 1 diabetes mellitus: an Indonesian study ## Abstract ### Background Type 1 diabetes mellitus (T1DM) is disease caused by the destruction of β pancreatic cells. The activation of T-lymphocyte and proliferation inhibitor are induced by protein tyrosine phosphatase non-receptor type 22 (PTPN22). However, the link between PTPN22 C1858T gene polymorphism and T1DM is still controversy. This study aimed to analyse the C1858T gene polymorphism in Indonesian children with T1DM. ### Materials and methods This case-control study was conducted from March 2021 to May 2022 in the Endocrinology Outpatient Clinic at Dr. Soetomo Hospital and Tropical Disease Center Universitas Airlangga. Patients with controlled T1DM during the study period were included. The PTPN22 analysis used polymerase chain reaction restriction fragment length polymorphism (PCR-RFLP) method. ### Results Sixty-two children voluntarily participated in this study, and were equally divided into the T1DM and control groups. Most of the patients ($94\%$, $\frac{58}{62}$) are Javanese. This study revealed a more frequent CC genotype ($9.4\%$) and allele-C ($54.6\%$) polymorphism in the T1DM group, while more frequent CT genotype ($100\%$) and allele-T ($50\%$) polymorphism were in the control group. The C- and T-allele frequency was $54.6\%$ and $45.4\%$ in the T1DM group, respectively. The T1DM and control groups did not significantly differ ($$p \leq .2381$$). ### Conclusions PTPN22 homozygous genotype-CC and allele-C polymorphisms are more frequent in patients with T1DM. However, the PTPN22 C1858T gene polymorphism did not significantly correlate to T1DM children in this study. Key Messages:The PTPN22 C1858T gene polymorphism does not significantly affect the susceptibility of T1DM in Indonesian children. PTPN22 homozygous genotype-CC polymorphism was more observed in the T1DM group; thus, this genotype may play as a risk factor for T1DM children in the Indonesian population. ## Background Diabetes mellitus (DM) is a metabolic disorder characterized by persistent hyperglycaemia. Two forms of DM frequently affect children; type 1 diabetes mellitus (T1DM) is commonly reported as an autoimmune illness due to β-pancreatic cell destruction, and type 2 DM (T2DM), also known as non-insulin dependent DM. Both types of diabetes are polygenic, which means they are linked to various genes [1]. The prevalence of T1DM was $10\%$ from diabetic patient [2]. The prevalence of T1DM is <$1\%$ of the entire population, and its incidence rate rapidly increased globally, an estimated threefold increase in prevalence by 2040 [3]. T1DM incidence is very different in some places. Some studies have found that the prevalence of T1DM was lower among Asians than Caucasians [4–10]. China reported a T1DM annual incidence of $\frac{0.1}{100}$,000, Japan with $\frac{1.4}{100}$,000, and Finland with $\frac{43}{100}$,000, while in Indonesia is approximately $\frac{0.3}{100}$,000 [2]. There are various causes of T1DM, such as genetic risk factors including β-pancreatic cell destruction induced by T-cell [2,11,12]. In 2008, Ikegami et al. reported that Japanese and Korean populations had five new single-nucleotide polymorphisms (SNPs). The −1123G > C SNP was correlated with T1DM in both populations. The human leukocyte antigen (HLA), cytotoxic T lymphocyte antigen-4 (CTLA-4) and protein tyrosine phosphatase non-receptor type 22 (PTPN22) are the crucial genes associated with T1DM susceptibility [13]. In Indonesian study, the HLA-DQA1 and HLA-DQB1 subtypes mainly found in Indonesian children with T1DM are HLA-DQA1 $\frac{0101}{0102}$ and HLA-DQB1 0301 [14]. Furthermore, the CTLA-4 1822 C/T polymorphism might be a protective factor against T1DM [15]. The PTPN22 gene has a significant role in preventing T cell activation and proliferation. The mutation of this gene can induce and maintain the autoimmunity [16]. PTPN22 polymorphism varies among races. Studies have investigated the relationship between T1DM and the PTPN22 C1858T. There have been reports of the 1858T allele being linked to T1DM in several countries, including Italy, Germany, Spain, Ukraine and France [13]. This topic has been studied since 12 years ago [12,17,18]. However, PTPN22 studies in Indonesia is limited. Hence, this study aims to evaluate the association between children with PTPN22 C1858T gene polymorphism and T1DM. ## Materials and methods Study was conducted in the Pediatric Endocrine Outpatient Clinic at Dr. Soetomo Hospital and Tropical Disease Center (TDC) Universitas Airlangga from March 2021 to May 2022. Children aged 4–18 years, who are willing to join in this study, were included in the T1DM group. Type 1 DM diagnosis was based on classic symptoms, elevated blood glucose level, low C-peptide and positive antibodies (GAD-65 and ZnT8) [19]. Meanwhile, children without T1DM, who visited the Pediatric Outpatient Clinic at Dr. Soetomo Hospital, in stable condition, and willing to join this study, belonged to the control group. Children due to severe illness and their parents who refused to join in the study were excluded. Consecutive random sampling was used for collecting samples and the sample size was determined by using the sample calculation formula for a cross-sectional study [20]. This study was approved by the Clinical Research Unit at Dr. Soetomo Hospital, Surabaya, Indonesia with the number of 1889/KEPK/III/2020. ## Genetic analysis The QIAmp DNA Mini Kit was used to extract DNA from peripheral blood mononuclear cells according to standard procedure. The DNA fragment was 218 bp resulted from the amplification of forward primer: 5′-ACTGATAATGTTGCTTCAACGG-3′ and reverse primer: 5′-TCACCAGCTTCCTCAACCAC-3′ by polymerase chain reaction (PCR) (Applied BioSystems, Foster City, CA). The PCR mixture consisted of 10× PCR buffer, 250 ng template DNA, 20 pmol of each primer, 1.5 mM MgCl2, 0.2 mM of each of the deoxyribonucleotide triphosphates, and 1 U GoTaq DNA polymerase (Promega, Fitchburg, WI). The amplification consists of denaturation for 2 min at 94 °C followed by 35 cycles at 94 °C for 30 s, annealing 30 s at 60 °C and 30 s at 72 °C, and the final extension for 3 min at 72 °C. The polymorphism of C1858T was identified by restriction fragment length polymorphism (RFLP) as described by Bulut et al. [ 21]. The PCR product was cut by RsaI enzyme (New England Biolabs, Ipswich, MA) at 37 °C for 4 h and resulted in two fragments, 176 bp and 42 bp that indicated the homozygous CC (wild type), whereas the heterozygous CT had three fragments at 218, 176 and 42 bp (see Figure 1). The mutant type (homozygous TT) was at 218 bp that cannot be cut by RsaI enzyme. The PCR product, which was not cut by RsaI enzyme, was used as the control of 218 bp position. Figure 1.: PTPN22 gene variant C1858T was analysed by PCR-RFLP and visualized by ethidium bromide stained polyacrylamide gel that were line 1 of PCR product without cutting and line 2–9 of samples with cutting by RsaI enzyme. M: marker (One STEP Marker 9, Wako, Japan). ## Statistical analysis SPSS version 20.0 (IBM, Armonk, NY) was applied for data analysis. Either Fisher’s exact or Chi-square test was used as requisite for the genotypes polymorphisms and allele distribution comparison between T1DM and control groups. The significant difference or correlation was shown by p value (.05). ## Results Sixty-two children involved in this study were divided as follows: 31 in the T1DM and 31 in the control group. The median age of all subjects was 12.6 (2.7–16.8) years, with 27 males and 35 females, and mostly Javanese tribe ($94\%$, $\frac{58}{62}$). The onset of T1DM occurred mostly in the childhood under 12 year-old ($90.3\%$, $\frac{28}{31}$) (see Table 1). Table 1. \| Characteristics \| Case (n = 31), n (%) \| Control (n = 31), n (%) \| Median (min–max) \| \| --- \| --- \| --- \| --- \| \| Age groups \| \| \| \| \| 0–12 years \| 5 (16.12%) \| 21 (67.74%) \| 12.64 (2.74–18.00) \| \| 12–18 years \| 26 (83.87%) \| 10 (32.25%) \| \| \| Sex \| \| \| \| \| Male \| 12 (38.70%) \| 15 (48.38%) \| \| \| Female \| 19 (61.29%) \| 16 (51.61%) \| \| \| Race/tribe \| \| \| \| \| Javanese \| 27 (87.09%) \| 31 (100%) \| \| \| Madurese \| 1 (3.22%) \| 0 (0%) \| \| \| Chinese \| 1 (3.22%) \| 0 (0%) \| \| \| Malay \| 2 (6.45%) \| 0 (0%) \| \| \| T1DM onset \| \| \| 9.00 (1.00–16.00) \| \| 0–12 years \| 28 (90.32%) \| \| \| \| 12–18 years \| 3 (9.67%) \| \| \| The CC genotype was mostly found in T1DM group but not significantly different in the controls ($$p \leq .238$$ $95\%$ CI: 0 (0–NA)). The prevalence of CT genotype in T1DM children was $90.3\%$ ($\frac{28}{31}$), while $100\%$ ($\frac{31}{31}$) was in the control. The frequency of the C allele and T allele in children with T1DM was $54.8\%$ and $45.2\%$, respectively (Table 2). Table 2. \| Variables \| T1DM (N = 31) \| Control (N = 31) \| p Value \| OR (95% CI) \| \| --- \| --- \| --- \| --- \| --- \| \| Genotype \| \| \| \| \| \| CC \| 3 (9.7%) \| 0 (0%) \| .238a \| 0 (0–NA) \| \| CT \| 28 (90.3%) \| 31(100%) \| \| \| \| TT \| 0 (0%) \| 0 (0%) \| \| \| \| Allele \| \| \| \| \| \| C \| 68 (54.8%) \| 62 (50%) \| .525b \| 0.8235 (0.5–1.3564) \| \| T \| 56 (45.2%) \| 62 (50%) \| \| \| ## Discussion Genetics and environment contribute to the risk of T1DM. Although the major histocompatibility complex (MHC) is closely connected to the genetic susceptibility to T1DM, the non-HLA gene is also thought to be present to promote the disease. The non-HLA genes such as PTPN22 may confer the risk of T1DM through T-cell-mediated immune response [22,23]. The PTPN22 C1858T gene polymorphism in T1DM children was not significantly different in the control group. It seems that PTPN22 C1858T gene polymorphism is not associated with the occurrence of T1DM. Therefore, interestingly, the variant C allele was present homozygous in $9.7\%$ ($\frac{3}{31}$) children with T1DM and not in the control group. This might play a role in T1DM susceptibility. Hence, it needs further study with a lot of samples. Our study was in line with previous studies, which provided no significant association between PTPN22 C1858T polymorphism and T1DM [22,23]. A recent study from Azerbaijani also reported that polymorphisms of the PTPN22 gene (polymorphisms −1123 C/G and +2740 A/G) did not correlate with T1DM [24]. The same result was shown in a study of Egyptian children with systemic lupus erythematosus [25]. Furthermore, a meta-analysis study that described the PTPN22 C1858T polymorphism in Europe and the American population may play as a risk factor in T1DM. However, in contrast with this study, PTPN22 was associated with T1DM in the Colorado, Egyptian children and Asian population [26–28]. This study found genotype-CC and allele-C more frequent in T1DM children. In contrast, another study showed that the TT-1858 genotype was more prevalent in children with T1DM ($$p \leq .038$$, OR: 3.16; $95\%$ confidence interval (CI): 1.28–7.09). The study concludes that PTPN22 C1858T polymorphisms increased the risk factor of T1DM [27]. Other studies also suggest that the prevalence of the PTPN22 variant (rs2542151), the G allele, may increase the risk of T1DM [28]. The limitation is that this is a single-centre study, further multicentre research is necessary to gather additional information on Indonesian races. ## Conclusions The homozygous genotype-CC and allele-C are more often found in T1DM. This study shows that PTPN22 C1858T polymorphism did not significantly had a role in T1DM genetic susceptibility. Further studies, such as multicentre studies and studies in different ethnic groups, are needed for the influence of the PTPN22 C1858T polymorphism to the T1DM susceptibility. ## Author contributions Study concept and design: FA and MF. Acquisition of the data: FA. Analysed and interpreted the data: SB and NR. Drafted the manuscript: FA and NR. Critical revision of the manuscript: NR and MF. Supervised the study: NR and SB. All authors read and approved the final manuscript. ## Ethics statement This study was approved by Dr. Soetomo Hospital Clinical Research Unit with the reference number of 1889/KEPK/III/2020. The Declaration of Helsinki was followed by all research participants. 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Ozougwu JC, Obimba KC, Belonwu CD. The pathogenesis and pathophysiology of type 1 and type 2 diabetes mellitus. J Physiol Pathophysiol (2013) 4 46-57 18. Bottini N, Peterson EJ.. Tyrosine phosphatase PTPN22: multifunctional regulator of immune signaling, development, and disease. Annu Rev Immunol (2014) 32 83-119. PMID: 24364806 19. Mayer-Davis EJ, Kahkoska AR, Jefferies C. ISPAD clinical practice consensus guidelines 2018: definition, epidemiology, and classification of diabetes in children and adolescents. Pediatr Diabetes (2018) 19 7-19 20. Pourhoseingholi MA, Vahedi M, Rahimzadeh M.. Sample size calculation in medical studies. Gastroenterol Hepatol Bed Bench (2013) 6 14-17. PMID: 24834239 21. Bulut F, Erol D, Elyas H. Protein tyrosine phosphatase non-receptor 22 gene C1858T polymorphism in patients with coexistent type 2 diabetes and Hashimoto’s thyroiditis. Balkan Med J (2014) 31 37-42. PMID: 25207165 22. Elsisi O, Kamal M, Madani H. Association of protein tyrosine phosphatase non receptor type 22 (PTPN22) C1858T gene polymorphism with type 1 diabetes mellitus in Egyptian children cohort. Gaz Egypt Pediatr Assoc (2015) 69 75-79 23. Baniasadi V, Das SN.. No evidence for association of PTPN22 R620W functional variant C1858T with type 1 diabetes in Asian Indian. J Cell Mol Med (2008) 12 1061-1062. PMID: 18194462 24. Ahmadov G.. Study of the PTPN22 gene in children with type 1 diabetes mellitus in the Azerbaijani population. Georgian Med News (2017) 271 45-49 25. Hamza RT, Awwad KS, Temsah KA. R620W polymorphism of protein of protein tyrosine phosphatase PTPN22 in Egyptian children and adolescents with systemic lupus erythematosus: relation to thyroid autoimmunity. Int J Adolesc Med Health (2013) 25 143-149. PMID: 23314521 26. Steck AK, Liu SY, McFann K. Association of the PTPN22/LYP gene with type 1 diabetes. Pediatr Diabetes (2006) 7 274-278. PMID: 17054449 27. Abou SS, Tawfik MA, Muhammed ZS. 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--- title: Bovine Colostrum Supplementation Modulates the Intestinal Microbial Community in Rabbits authors: - Stella Agradi - Paola Cremonesi - Laura Menchetti - Claudia Balzaretti - Marco Severgnini - Federica Riva - Bianca Castiglioni - Susanna Draghi - Alessia Di Giancamillo - Marta Castrica - Daniele Vigo - Silvia Clotilde Modina - Valentina Serra - Alda Quattrone - Elisa Angelucci - Grazia Pastorelli - Giulio Curone - Gabriele Brecchia journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044174 doi: 10.3390/ani13060976 license: CC BY 4.0 --- # Bovine Colostrum Supplementation Modulates the Intestinal Microbial Community in Rabbits ## Abstract ### Simple Summary Recently, research has focused on the modulation of the gut microbiota because of its central role in several digestive physiological functions and its involvement in the onset of not only gastrointestinal but also systemic diseases. Supplementing rabbit diets with nutraceutical substances could be a strategy to prevent dysbiosis, strengthen the immune system, and reduce mortality during the critical weaning period. Bovine colostrum (BC) is a by-product of the dairy industry and is very rich in compounds with several biological activities. Its use as an intestinal microbiota modulator in rabbits has never been investigated. This study evaluates the effects of diet supplementation with two different percentages of BC (2.5 and $5\%$) on luminal and mucosa-associated microbiota and its metabolism-associated pathways in the jejunum, caecum, and colon of rabbits. Although our results showed no effect of BC on microbiota biodiversity, there were significant differences between experimental groups in the microbial composition, mainly at the level of sub-dominant components depending on the dose of supplementation. The metabolism-associated pathways have also been affected, and particularly interesting are the results on the amino acids and lactose metabolism. Overall, findings suggest that BC could be used as a supplement in rabbit feed, although its effects on productive and reproductive performances, intestinal disease resistance, and economic aspects need to be further evaluated. ### Abstract BC is a nutraceutical that can modulate intestinal microbiota. This study investigates the effects of BC diet supplementation on luminal and mucosa-associated microbiota in the jejunum, caecum, and colon of rabbits. Twenty-one New Zealand White female rabbits were divided into three experimental groups ($$n = 7$$) receiving a commercial feed (CTRL group) and the same diet supplemented with $2.5\%$ and $5\%$ BC ($2.5\%$ BC and $5\%$ BC groups, respectively), from 35 (weaning) to 90 days of age (slaughtering). At slaughter, the digestive tract was removed from each animal, then both content and mucosa-associated microbiota of jejunum, caecum, and colon were collected and analysed by Next Generation 16SrRNA Gene Sequencing. Significant differences were found in the microbial composition of the three groups (i.e., beta-diversity: $p \leq 0.01$), especially in the caecum and colon of the $2.5\%$ BC group. The relative abundance analysis showed that the families most affected by the BC administration were Clostridia UCG-014, Barnesiellaceae, and Eggerthellaceae. A trend was also found for Lachnospiraceae, Akkermansiaceae, and Bacteroidaceae. A functional prediction has revealed several altered pathways in BC groups, with particular reference to amino acids and lactose metabolism. Firmicutes:Bacteroidetes ratio decreased in caecum luminal samples of the $2.5\%$ BC group. These findings suggest that BC supplementation could positively affect the intestinal microbiota. However, further research is needed to establish the optimal administration dose. ## 1. Introduction Rabbits are herbivores with a digestive system anatomically and physiologically evolved to obtain nutrients from low-calories and fibre-rich food thanks to the intense fermentative activity in the large intestine, particularly in the caecum [1,2]. A eubiotic condition for the digestive tract microbiota is pivotal for the maintenance of health and both productive and reproductive performances [3,4]. In rabbit breeding, weaning is a very critical period because the diet changes from milk to solid food. This induces a profound modification in the intestinal microbial population resulting in the onset of enteric diseases caused by inflammatory and infectious events [5,6] due to the impaired function of the immune system. Although these inconveniences could be reduced with the use of antibiotics, the use of these drugs has been greatly cut down over the years following the European guidelines related to antibiotic resistance. Natural substances integrated into the diet can favour the development of beneficial gut microbial flora and, therefore, adequately stimulate the immune system not only in human beings but also in animals. In rabbits, this could contribute to the reduction of the use of antibiotics and the improvement in animal welfare as well as the profitability of the farm [7]. Moreover, rabbits are valid experimental animal models to evaluate the changes induced by the diet in the commensal intestinal bacterial population [8,9], as well as the relationships between nutrition and immunological functions [10,11,12], productive [13,14,15] and reproductive performances [16,17,18,19]. Among the natural substances which can be supplemented to the feed, bovine colostrum (BC) has recently drawn the attention of the scientific community. Colostrum is the secretion produced by the mammary glands immediately after delivery [20], which is commonly treated as a by-product of the dairy industry. The main functions of BC, besides providing essential nutritional components for the newborns’ growth, are to boost the natural defence system, regulate the immune response, equilibrate the intestinal microbiota, and improve various tissues’ growth and repair [21,22]. Defense-acting substances such as immunoglobulins, lactoferrin, lysozyme, and glycomacropeptide can have a direct action on pathogens, while other substances, such as oligosaccharides, gangliosides, and nucleosides, can act indirectly by promoting the growth of beneficial microbial flora. Bacteria like Lactobacillaceae, Bifidobacteriaceae, Lachnospiraceae, Akkermansiaceae, and Bacteroidaceae can, indeed, strengthen immune defences and modulate the physiology of the digestive system [23,24,25]. In recent years, a large number of studies have evaluated the potential role of BC in the health [26,27], energy balance, and sports performance [28] of humans. Moreover, some studies have also highlighted the beneficial effects of BC supplementation for animals such as mice [29], rats [30], horses [31], piglets [32], dogs [33,34], lambs [35], poultry [36], and calves [37]. With regards to rabbits, the effects of BC supplementation have been assessed on meat quality [38] and diarrhoea prevention at weaning [39], yet its action on the gut microbiota remains unexplored. Only recently, the bacterial microbiota composition along the gastrointestinal tract of rabbits has been characterized [40,41], and its changes after dietary supplementations have been considered [8,9]. To our knowledge, changes in the intestinal microbiota induced by BC diet supplementation in rabbits have not been evaluated yet. Moreover, only in one study mucosal and luminal gut microbiota have been compared at the Sacculus rotundus level [42]. In this study, we hypothesized that BC-enriched diets in rabbits can influence gut bacterial richness, diversity, and functional potential and that these modifications are dose-dependent. The purpose of this experimental trial was to discern the luminal and mucosal microbiota composition, as well as their metabolism-associated pathways, in different digestive tract sections (jejunum, caecum, and colon) of rabbits fed with diets supplemented with two percentages of BC. ## 2.1. Animals and Samples Collection The experimental trial was conducted in the facilities of the Department of Agricultural, Food and Environmental Sciences of the University of Perugia, Italia. In accordance with the European and Italian laws (EU Directive $\frac{2010}{63}$ and Decreto Legislativo $\frac{26}{2014}$) regarding the protection of animals used for scientific purposes, the rabbits were maintained under the supervision of a responsible veterinarian, and the experimental protocol was approved by the Ethical Committee of the Department of Veterinary Medicine of the University of Milano with the code OPBA_42_2021. All efforts were made to minimize animal discomfort and to reduce the number of experimental animals. According to dietary treatment, 21 New Zealand White female rabbits were randomly assigned to three groups from 35 days (weaning) until 90 days of age (slaughtering). The control group ($$n = 7$$ animals, CTRL) was fed with a commercial feed including the following ingredients: dehydrated alfalfa meal ($43.0\%$), wheat bran ($30.0\%$), barley ($9.5\%$), sunflower meal ($4.6\%$), rice bran ($4.0\%$), soybean meal ($4.0\%$), calcium carbonate ($2.2\%$), cane molasses ($2.0\%$), soybean oil ($0.4\%$), and salt ($0.3\%$). The other two groups were fed with the same diet, to which, before pelleting, was added lyophilized BC at the rate of $2.5\%$ ($2.5\%$ BC group, $$n = 7$$) and $5\%$ ($5\%$ BC group, $$n = 7$$). The lyophilized BC was derived from high-quality colostrum from multiparous dairy cows. The basic composition and IgG concentration were evaluated before lyophilisation: total solids $22.1\%$, fat $4.5\%$, total protein $14.2\%$, lactose $2.9\%$, and IgG 3.3 g/100 mL. Table 1 shows the analytical chemical composition of the feeds. The animals used in the trial were suckled by mothers who were fed the same diet they received after weaning. These diets were previously used in another experiment [38]. Rabbits were raised in individual cages and kept in a conditioned environment with a temperature ranging between 18–20 °C, relative humidity of 60–$65\%$, and a photoperiod of 16 h of light for the duration of the entire trial; water and feed were provided ad libitum. Rabbits were slaughtered following the EU Regulations currently in force in an authorized slaughterhouse. The animals were first stunned by mechanical stunning and then slaughtered by jugulation. The digestive tract was removed from each animal, and the content of the different intestinal tract sections (jejunum, caecum, and colon) was collected. Specifically, regarding the colon, the anatomical region sampled was the proximal part between the Ampulla caecalis coli and Fusus coli. Five cm of each intestinal tract were dissected and longitudinally opened. The content of these tracts was collected in 15 mL sterile tubes and then stored at −80 °C until analysis. Each sample was examined individually for the determination of the luminal microbiota. Moreover, the same three collected tracts of the gastrointestinal apparatus were used for the collection of the mucosa-associated microbiota. After luminal microbiota collection, the intestinal tissue was gently rinsed with a sterile saline solution to remove any residual contents. A sterile scalpel blade was then used to scrape the luminal surface of the tissue samples (2 × 2 cm) in order to collect the mucosa-attached bacteria for mucosa-associated microbiota determination. The achieved samples were stored in sterile 1.5 mL tubes at −80 °C until analysis. ## 2.2.1. DNA Extraction The bacterial DNA was extracted from each sample of intestinal contents by using the commercial QIAamp PowerFecal Pro DNA Kit (Qiagen, Hilden, Germany), as already described [8]. The NanoDrop ND-1000 spectrophotometer (NanoDrop Technologies, Wilmington, DE, USA) was used to verify the DNA quality and quantity; the isolated DNA was then stocked at −20 °C until use. ## 2.2.2. 16S Ribosomal RNA (rRNA) Gene Sequencing Bacterial DNA was amplified and sequenced as described in Cremonesi et al., 2022 [8]. Briefly, 16S rRNA amplicons were prepared following the 16S Metagenomic Sequencing Library Preparation Protocol (Illumina, San Diego, CA, USA); libraries were pooled in equimolar proportion and then sequenced in one MiSeq (Illumina) run with 2 × 250-base paired-end reads. The dataset comprised a total of 126 samples, deriving from 3 tissues (jejunum, caecum, and colon), 2 sites (lumen and mucosa), and 3 diet groups (CTRL diet, $2.5\%$ BC, and $5\%$ BC). Each combination had 7 independent replicates. ## 2.2.3. Sequence Analysis Raw reads from each sample were subjected to a preliminary filtering pipeline that comprised the merging of the two paired reads coming from the same fragment in one single sequence by PandaSeq [43] and the trimming/filtering of low-quality bases/reads (i.e., trimming from the 3′-end stretches of bases whose Phred quality score was <3; resulting fragments having a length shorter than $75\%$ of the initial fragment length were discarded). Filtered reads were clustered into zero-radius operational taxonomic units (zOTUs) by USEARCH (v. 11.0.667, [44]) in order to merge together reads putatively coming from the same species. Only zOTUs supported by 5 or more reads were retained. Downstream analyses (including alpha- and beta-diversity evaluations) were performed in QIIME 1.9.0 suite [45]. Taxonomic assignment of zOTUs was performed by the RDP classifier [46] against SILVA 138 database [47] using 0.5 as the confidence threshold. ## 2.2.4. Functional Predictions Functional predictions from the 16S rRNA-derived microbial profiles were estimated by applying PICRUSt2 (v 2.5.1, [48]) on the zOTU table of the 125 samples considered in the experiment after normalization to the least sequenced samples. Abundances were converted into copies-per-millions (CPM) by using the humann2_renorm_table utility script from HUMANn2 [49]. Lineages were associated with the MetaCyc pathways, and abundances at each level were calculated by using the script categorize_by_function.py from PICRUSt (v 1.0.0, [50]). ## 2.3. Statistical Analysis Biodiversity analysis (alpha-diversity analysis) was performed using several metrics (i.e., Shannon’s diversity, chao1 diversity index, observed species, and Faith’s phylogenetic diversity index). A non-parametric permutation-based t-test (equivalent to Mann–Whitney U-test), with 999 random permutations, was employed in order to assess whether the samples belonging to one experimental class were more or less diverse than those of a different class. Microbial profile analysis (beta-diversity analysis) was based on unweighted and weighted UniFrac distances [51] and represented by a Principal Coordinate Analysis (PCoA) aimed at reducing the complexity of the variance. The “Adonis” test function (Permutational Multivariate Analysis of Variance Using Distance Matrices using pseudo-F ratios) was used in order to define whether there was a significant difference among the experimental groups, using 999 random permutations. Composition analysis in terms of taxa relative abundances was performed by grouping the classified zOTUs to different taxonomic levels (phylum, class, order, family, genus). A Kruskal–Wallis test was employed to assess whether there was a significant difference among levels of the experimental categories on the relative abundance of the taxa, on the predicted pathway abundances, and on Firmicutes:Bacteroidetes ratio evaluation; Dunn’s post hoc pairwise test was employed if necessary. All statistical analyses and plots were performed using Matlab (v. 2008a, Natick, MA, USA). ## 2.4. Data Availability Statement The data presented in this study are openly available in NCBI Short Read Archive (SRA) under experiment IDs SRR22879769-SRR22879893 (BioProject ID PRJNA915237, https://www.ncbi.nlm.nih.gov/bioproject/PRJNA915237, accessed on 5 January 2023). ## 3.1. Sequencing Results The microbiota structure of the rabbits’ gastrointestinal tract was characterized by a total of 2,797,273 high-quality reads (after filtering). Sample 114 (colon tract, lumen content of a $2.5\%$ BC-treated rabbit) had a very low number of raw reads (i.e., 825) and was therefore discarded. The average read depth per sample was 22,374 ± 9372. All the downstream analyses, then, were performed on 125 samples (excluding sample 114), and all samples were normalized to the least sequenced sample ($$n = 4856$$). The analysis of the rarefactions curves for both the chao1 and the observed species metrics showed that the majority of the samples had a tendency toward reaching a plateau, thus suggesting that the depth of coverage was sufficient to describe the biological diversity within the samples (Figure S1). ## 3.2. Taxonomic Composition of Gut Microbiota along the Rabbit Gastrointestinal Tract of CTRL and 2.5 and 5% BC Groups Overall, the microbial composition of the mucosa-derived samples was less defined, with higher percentages of bacteria unclassified at lower levels, compared to the corresponding lumen samples. The degree of microbial definition at the genus level was, anyway, low due to the relatively poor characterization of rabbit metagenomes in the taxonomy reference database (i.e., SILVA). The major genera found in the samples were Dubosiella, Akkermansia, Bacteroides, Methanobrevibacter, Ruminococcus, Marvinbryantia, and Alistipes, which, however, made up only $15.5\%$ of the relative abundance on average. Figure 1 depicts the average composition at the phylum level for the samples divided by the intestinal tract, sampling site, and diet, whereas Figure S2 summarizes the relative abundances of the samples at all taxonomic levels (phylum to genus). Jejunum lumen samples were characterized by a relatively high content of Firmicutes (~$80\%$), Actinobacteriota (~$7\%$), Euryarchaeota (~$6\%$), and Patescibacteria (~$2.5\%$), whereas Bacteroidota were very low (<$0.5\%$); at the family level, these samples were rich in Eubacteriaceae (~$58\%$), Erysipelotrichaceae (~$12\%$), Methanobacteriaceae (~$6\%$), and Saccharimonadaceae (~$2.5\%$). Furthermore, the corresponding jejunum mucosa samples had about $35\%$ of the overall abundance (on average) composed by “Unclassified” and “Bacteria” unclassified at lower levels; Firmicutes composed about $55\%$ of the relative abundance, whereas Proteobacteria and Actinobacteriota accounted for about $4.5\%$ and $2.5\%$, respectively. This was reflected also at the family level, where apart from Eubacteriaceae (accounting for ~$40\%$ of the relative abundance) and Erysipelotrichaceae (~$4\%$), all the other families were scarcely present. The microbiota of the caecum lumen samples was mainly composed of Firmicutes (~$65\%$), Bacteroidota (~$19\%$), Actinobacteriota (~$2.6\%$), and Verrucomicrobiota (~$6.5\%$) at the phylum level, and by Eubacteriaceae (~$25\%$) and members of Lachnospiraceae, Oscillospiraceae, Ruminococcaceae, Akkermansiaceae, Bacteroidaceae, Christensenellaceae, as well as Muribaculaceae at the family level, each accounting for $3.9\%$-$9.4\%$ of the relative abundance. Caecum mucosa samples were characterized by Firmicutes (~$68\%$), Bacteroidota (~$7\%$), and Verrucomicrobiota (~$3\%$), with a higher abundance of unclassified bacteria (accounting for 7.7–$18.0\%$), as previously observed in mucosa samples; at the family level, these samples were mainly constituted by Eubacteriaceae (~$30\%$), Lachnospiraceae, Erysipelotrichaceae, Oscillospiraceae, and Ruminococcaceae, each in the range of 3.4–$9.8\%$ of the overall abundance. Colon lumen samples had about ~$66\%$ Firmicutes, ~$19\%$ Bacteroidota, ~$6\%$ Verrucomicrobiota, and ~$2\%$ Euryarchaeota, whereas, at the family level, they were composed of Lachnospiraceae ($9\%$), Ruminococcaceae ($6.5\%$), Oscillospiraceae ($6.5\%$), Christensenellaceae ($3.5\%$), Akkermansiaceae ($6\%$), Muribaculaceae ($5\%$), and Bacteroidaceae ($6\%$). Finally, colon mucosa samples, besides ~$50\%$ Firmicutes, ~$5\%$ Proteobacteria, and ~$3\%$ Bacteroidota, had an additional ~$35\%$ composed of “Unclassified” bacteria; the abundance of Eubacteriaceae was the lowest (~$20\%$), with a consistent presence of Lachnospiraceae (~$8\%$), Oscillospiraceae (~$6.5\%$), Ruminococcaceae (~$3\%$), and Christensenellaceae (~$2.3\%$). ## 3.3. Comparison of Microbiota Composition in Different Intestinal Tracts Considering both the intestinal tract and the site ($$n = 21$$), jejunum mucosa samples had the lowest biodiversity (statistically different, $$p \leq 0.015$$, from all the other conditions, except jejunum lumen), followed by the jejunum lumen samples; on the other hand, caecum and colon (both lumen and mucosa) had a higher diversity (Figure S3A and Table S1). Each condition was characterized by a different microbial composition, with the beta-diversity analysis revealing significant differences on both the unweighted and the weighted UniFrac distances ($p \leq 0.002$ and $p \leq 0.02$, respectively) for all comparisons, except for caecum and colon lumen samples, which were similar (Figure S3B and Table S2). Consistent results were also found when comparing the samples over the three intestinal tracts (lumen and mucosa together, $$n = 42$$ per group), with the biodiversity of jejunum samples lower than the caecum and colon samples for the PD whole tree ($$p \leq 0.003$$, Figure S3C and Table S3), and the three tissues significantly separated on both unweighted ($$p \leq 0.001$$) and weighted (p ≤ 0.02) UniFrac distances (Figure S3D and Table S4). Similarly, this was also observed for the comparison between the lumen versus the mucosa samples ($$n = 63$$ per group), with lumen samples having a higher biodiversity than mucosa samples ($$p \leq 0.003$$, Figure S3E) and a microbial profile between the two sites significantly different, as well, on both the weighted and the unweighted UniFrac distances (p-value = 0.001) (Figure S3F). ## 3.4. Comparison of Microbiota Composition in Rabbits according to Bovine Colostrum Diet Supplement In order to determine eventual changes in microbial diversity and composition according to the BC supplementation ($2.5\%$ and $5\%$, as compared to the CTRL diet), a comparison of the microbiota composition in rabbits was investigated. Results were computed stratifying samples in diet groups homogeneous for the intestinal tract and site sampled ($$n = 7$$), i.e., considering differences only in the diet supplementation, provided that the intestinal tract (jejunum, caecum or colon) and site (lumen and mucosa) were the same. As far as the alpha-diversity analysis, there was a non-significant difference among samples according to the BC supplementation. However, according to the chao1 metric, in samples with increasing percentage of BC, the biodiversity increased in the jejunum lumen and in caecum mucosa, whereas it decreased in jejunum mucosa and caecum lumen samples; no trend was observed for colon samples (both mucosa and lumen) (Figure 2). The tendency towards increasing biodiversity in caecum mucosa was also confirmed for the observed species metric, whereas no differences were highlighted for PD whole tree and Shannon diversity indices. The microbial composition (beta-diversity), on unweighted UniFrac distance, of the three diet groups (CTRL, $2.5\%$, and $5\%$) resulted in significantly different (p ≤ 0.002 for all pairwise comparisons) in caecum and colon lumen samples. At the same time, in colon mucosa samples, significant differences were reported for CTRL vs. $5\%$ BC and $2.5\%$ BC vs. $5\%$ BC, whereas no difference was observed between CTRL and $2.5\%$ BC. No significant differences were reported for caecum mucosa and jejunum (both lumen and mucosa) samples (Figure 3 and Table S5). As a whole, the microbiota of the three diet groups ($$n = 42$$) did not show different biodiversity for any of the tested metrics, whereas the microbial profiles resulted somehow different for the unweighted UniFrac ($$p \leq 0.002$$, p ≤ 0.014) (Table S6). Regarding the relative abundance analysis, due to the reduced number of samples per group, the significantly altered taxa were few, and some of them were at relatively low relative abundances. This is somehow expected, also considering the results from the beta-diversity analysis, for which the differences were observed for the unweighted UniFrac distance, suggesting modifications in the sub-dominant components of the microbiota. The following data only consider the taxa present at a relative abundance > $1\%$ on average in at least one of the experimental groups, and the indication of an “increase” or a “reduction” is provided using the CTRL diet as a reference. The reported data are for the phylogenetic level of families since it is the lowest for which the majority of the taxa are properly defined (i.e., genus-level classifications contained many “unclassified” and “uncultured” taxa). Jejunum lumen samples showed a significant increase of Clostridia in $2.5\%$ BC and $5\%$ BC groups, together with a tendency towards a reduction of Eubacteriaceae and Erysipelotrichaceae in the $2.5\%$ BC group, as well as an increase of Oscillospiraceae and Ruminococcaceae in $2.5\%$ BC group. Jejunum mucosa samples showed a tendency towards an increase of unclassified bacteria and Clostridiaceae in the $2.5\%$ BC group, together with a tendency towards a reduction of Eubacteriaceae (in $2.5\%$ BC) and Lachnospiraceae (in both $2.5\%$ BC and $5\%$ BC groups). Caecum lumen samples showed a significant increase of Erysipelotrichaceae (in $5\%$ BC), Eggerthellaceae (in $2.5\%$ BC and $5\%$ BC), and family UCG-014 of Clostridia (in $2.5\%$ BC and $5\%$ BC), as well as a tendency towards an increase of Bacteroidaceae (in $5\%$ BC); on the other hand, we found a significant decrease of Barnesiellaceae (in $2.5\%$ BC) as well as of UCG-010 and vadinBB60 families of Clostridia (both in $5\%$ BC). Caecum mucosa samples had a significant increase of Clostridia (in $2.5\%$ BC) and a tendency towards the increase of Lachnospiraceae (in $2.5\%$ BC), Akkermansiaceae (in $2.5\%$ BC and $5\%$ BC), Bacteroidaceae (in $2.5\%$ BC), Barnesiellaceae (in $2.5\%$ BC), and towards the reduction of Eubacteriaceae (in $5\%$ BC) and unclassified bacteria (in $2.5\%$ BC). Colon lumen samples were characterized by a significant increase of Eggerthellaceae (in $2.5\%$ BC and $5\%$ BC) and by a tendency towards an increase of Akkermansiaceae (in $5\%$ BC), Bacteroidaceae (in $2.5\%$ BC), and Rikenellaceae (in $5\%$ BC); on the other hand, Ruminococcaeceae (in $2.5\%$ BC) and Monoglobaceae (in $2.5\%$ BC and $5\%$ BC) were significantly reduced. Colon mucosa samples highlighted a tendency towards an increase of Eubacteriaceae (in $5\%$ BC) and Campylobacteriaceae (in $2.5\%$ BC and $5\%$ BC) and a tendency towards a reduction of unclassified bacteria (in $5\%$ BC) (Figure 4D). The complete list of altered taxa abundances is available in Table S7. ## 3.5. Functional Prediction on Microbial Profiles of Rabbits according to Bovine Colostrum Diet Supplement The PICRUSt2-based functional prediction from the 16S rRNA microbial profiles of the samples revealed several bacterial metabolism-associated pathways significantly altered in the rabbits fed a BC-supplemented diet (Table 2). The effect of BC supplementation on predicted pathways was less evident for jejunum samples (mucosa: 13 altered pathways, lumen: 6), more consistent in the caecum (mucosa: 38 altered pathways, lumen: 10), and most abundant in the colon (mucosa: 62 altered pathways, lumen: 47). In order to better describe the metabolic functions involved, we grouped the raw pathways (“level 5”) to upper lineages (levels 1 to 4). In the jejunum lumen, processes belonging to pathways of proteinogenic amino acid (phenylalanine and tyrosine) biosynthesis and NAD synthesis were depleted in BC groups, whereas phylloquinone biosynthesis was enriched in $2.5\%$ BC samples; no differences were observed for $5\%$ BC samples. On the other hand, in jejunum mucosa samples, several level-4 pathways were significantly more abundant in $2.5\%$ BC and $5\%$ BC, such as the biosynthesis of peptidoglycans, NAD, geranylgeranyl pyrophosphate (GGPP) (cofactor-biosynthesis group), lactose, and diterpenoids as well as isoprenoids (both methylerythritol phosphate pathways I and II were found altered), both belonging to the terpenoid-biosynthesis group and the degradation of lysine (involving L-lysine fermentation to acetate and butanoate). In the caecum lumen, several altered pathways were reported for $2.5\%$ BC samples and included the increase of histidine and lysine degradation and of sugar nucleotides and vitamin B6 biosynthesis (via pyridoxal 5′-phosphate biosynthesis I); at the same time, pathways related to nucleotide biosynthesis (5-aminoimidazole ribonucleotide and purine nucleotides salvage), and proteinogenic amino acid synthesis (threonine, lysine, phenylalanine, and tyrosine) were depleted. On the other hand, results for $5\%$ BC samples were contrasting, with phenylalanine and tyrosine synthesis increased. In caecum mucosa samples, contrastingly, only one level-4 pathway was altered, i.e., histidine degradation, which resulted in more abundant samples from BC-supplemented rabbits than those fed the CTRL diet. Colon lumen samples were characterized by a significant increase in quinone biosynthesis, including DHNA, demethylmenaquinone (pathways of demethylmenaquinol-6, -8, and -9), menaquinone (pathways of menaquinol 6–13), phylloquinone, vitamin B6 biosynthesis, and glutamate degradation in both $2.5\%$ BC and $5\%$ BC, whereas the biosynthesis of glycogen, acetylmuramoyl-pentapeptide, GGPP, purine nucleotides salvage, and isoprenoids was depleted. Finally, colon mucosa samples showed a significant increase in histidine and lysine degradation, and many level-4 pathways were depleted in BC-supplemented samples. The latter included biosynthesis of glycogen, cofactor-biosynthesis members (NAD, GGPP), unsaturated fatty acids biosynthesis (involving many level-5 pathways, such as fatty acid biosynthesis initiation, cis-vaccenate, palmitoleate, gondoate, oleate, and (5Z)-dodec-5-enoate biosynthesis), phospholipid biosynthesis (cdp-diacylglycerol I, II and phosphatidylglycerol I, II), purine nucleotides salvage (adenine and adenosine salvage III), and isoprenoids, together with a reduction of glycogen degradation. Notably, two pathways, biotin and stearate biosynthesis, showed a discordant behaviour in BC-supplemented samples, being increased in $2.5\%$ BC and decreased in $5\%$ BC compared to the CTRL diet. ## 3.6. Firmicutes:Bacteroidetes Ratio The Firmicutes:Bacteroidetes (now so-called “Bacteroidota”, F:B) ratio was found to be very different in the mucosa and luminal samples (in both caecum and colon), with the former characterized by a higher F:B (average of 82.4 and 3.7, respectively). The F:B ratio was found to be significantly reduced in caecum lumen samples of the $2.5\%$ BC group ($$p \leq 0.0112$$, Dunn pairwise test). On the other hand, regardless of being from the colon or mucosa, jejunum samples had a notably higher ratio (range 987.9–58117.8, no significant differences among groups) (Table 3). ## 4. Discussion This is the first study investigating the effects of BC diet supplementation on the intestinal microbial community and its metabolism-associated pathways in rabbits. Moreover, for the first time, the differences among luminal and mucosa-associated microbiota in the jejunum, caecum, and colon of rabbits have also been investigated. First of all, the taxonomic composition of gut microbiota independently from the experimental group was investigated. In jejunum lumen samples, about $80\%$ of the microbial population belonged to the Firmicutes phylum. The most represented family was Eubacteriaceae, followed by Erysipelotrichaceae. This family is involved in lipid metabolism [52], confirming that it mainly occurs at the level of the jejunum [53]. On the other hand, caecum and colon luminal microbiota showed similar composition at the phyla level, with Firmicutes and Bacteroidetes as dominant phyla. They are bacteria specialized in the degradation of insoluble fibre and polysaccharide utilization [54], respectively, consistent with the fermentative functions of the caecum and colon [8,9,40]. At the family level, both Ruminococcaceae and Lachnospiraceae showed relevant abundances in the caecum and colon lumen, in agreement with other studies on rabbits [40,41]. They are responsible for the hydrolyzation of starch and other saccharides, producing short-chain fatty acids at the caecum and colon levels [55]. Regardless of the intestinal tract considered, at the genus level, the degree of microbial definition was low, probably due to the relatively poor characterization of rabbit metagenomes in the taxonomy reference database (i.e., SILVA). Among the major genera reported, Akkermansia and Ruminococcus agree with other investigations [40,41]. The mucosa-associated microbiota showed significant differences from the luminal microbiota. Firmicutes was the most abundant phylum in the jejunum and colon, while Bacteroidota prevailed in the caecum. Overall, the mucosa-associated microbiota had a less characterized microbial population with a higher percentage of undetermined taxa and, therefore, deserves further investigation. Regardless of the experimental group, the alpha-diversity showed higher values in the caecum and colon compared to the jejunum. This was a foreseeable result, considering that the rabbit is a hindgut fermenter, and the biggest microbial population lives in the caecum and colon [8,9,40]. As regards the site, higher biodiversity was found in the luminal samples compared with the mucosal ones. In rabbits, to date, no study has compared luminal and mucosa-associated microbiota, and also in other animal species, the information is still limited and variable according to the gastrointestinal tract considered [56]. The analysis of the beta-diversity indicated that a different microbial composition characterizes the different tracts of the rabbit intestine, except for colon and caecum luminal samples, which showed similar results. This is probably due to the similar physiological fermentative function of these two tracts, which, therefore, have a homogenous microbial population [40]. Interestingly but not surprisingly, lumen and mucosa-associated microbiota also showed significant differences in beta-diversity, highlighting the presence of a different microbial community in these two sites. In this study, the effect of the BC diet supplementation on the intestinal rabbit microbiota biodiversity and microbial composition has been evaluated for the first time. The alpha-diversity index indicated several differences due to the BC administration, although only as a trend toward statistical significance. Findings suggested a dose-dependent effect of BC on the microbiota’s biodiversity of jejunum and caecum samples, but the direction of these changes (i.e., increase or decrease in diversity) was different between lumen and mucosa. In particular, the microbiota’s biodiversity in the caecum mucosa tended to increase as the dose of BC supplementation increased. Similar results were found after BC administration on mice’s gut microbiota, mainly due to an increase in short-chain fatty acids-producing microorganisms [57]. In the same investigation, the effect of BC administration was also found for the microbial composition evaluated by beta-diversity analysis. That is consistent with another investigation in mice fed a diet supplemented with goat colostrum [58] and was confirmed by our findings. Unweighted UniFrac and principal coordinates analysis showed, indeed, a different qualitative microbial composition in the three experimental groups (significant at 0.01 level). Interestingly, the $2.5\%$ BC group greatly differed from the other groups, while the distance between CTRL and the $5\%$ BC groups was smaller, especially in the caecum and colon. This suggests that the $2.5\%$ BC diet supplementation can modify the microbial composition of the intestinal microbiota in rabbits and that different doses of supplementation can influence the phylogenetic microbial composition in different ways. The relative abundance analysis showed that the greater modulation by BC mainly interested the sub-dominant components of the microbiota. Few but interesting differences were, however, found for the phyla. Specifically, Firmicutes decreased in the colon luminal samples of both BC groups, probably as a result of the reduced fibre content of BC diets. At the family level, the most important taxa showing significant differences among groups were Clostridia UCG-014, Barnesiellaceae, and Ruminococcaceae. Clostridia UCG-014 increased in the caecum luminal samples of BC groups. This family is positively considered as one of the main bacteria involved in the production of tryptophan metabolites, which in turn are pivotal for the regulation of gastrointestinal homeostasis in humans [59,60]. They exert a preventive effect on dysbiosis during induced ulcerative colitis, as demonstrated in mice [61], while their reduction has been highlighted in Campylobacter jejuni-infected turkeys [62]. Barnesiellaceae decreased in the caecum luminal samples of the $2.5\%$ BC group. Barnesiellaceae is a less investigated family, and just a few studies have reported its increase in the gut microbiota of human patients affected by cardiovascular disease [63] and in women with a sedentary lifestyle [64]. Conversely, the Ruminococcaceae is a family which usually has a high prevalence in the rabbit gastrointestinal tract [8,9,40]. These bacteria are involved in fibre digestion and are responsible for the production of short-chain fatty acids. The present study showed a decrease in Ruminococcaceae in the colon luminal samples of the $2.5\%$ BC group, and this could be due to the lower fibre content of the BC diets. Other minor families were modulated by the BC administration. Specifically, Eggerthellaceae, a polyphenol-degradating family specifically correlated to lipid metabolism, has increased its prevalence in both BC groups. This could be due to a higher lipid content of the BC-supplemented diets compared to the control group. Its increment has been previously linked to weight loss and intestinal histomorphology restoration in obese mice [65]. Considering the positive role of Eggerthellaceae in gut microbiota, our result is particularly encouraging for further research in this direction. BC-supplemented rabbits also tended to have a higher abundance of Lachnospiraceae, Akkermansiaceae, and Bacteroidaceae. Lachnospiraceae are among the main producers of short-chain fatty acids in the gut microbiota, particularly butyrate, which is among the main energetic sources for colonocytes, while Akkermansiaceae metabolize the mucin present in the mucus gel layer in the colon tract. As hypothesized in another study evaluating the effect of bovine colostrum feed supplementation on mice [57], the increase of Lachnospiraceae and Akkermansiaceae could have a positive effect at multiple levels, such as on the intestinal barrier, on the nervous system development, and on the immune system of the host. Finally, the Bacteroidaceae family provides nutrients and vitamins to the organism through the metabolization of polysaccharides and oligosaccharides, and, as a consequence, it is considered beneficial for the intestinal microbiota [66]. Thus, it can be inferred that the rabbit diet supplementation with BC, especially at the $2.5\%$ dose, could positively modulate the intestinal microbiota, even if the role and prevalence of Ruminococcaceae in the rabbit gut should be better investigated. In this study, the gut microbiota metabolism-associated pathways were also investigated to better understand the influence that BC supplementation could have on the functional potential of the microbiota. Genes of microorganisms encode for several enzymes, which are involved in protein, lipid, carbohydrate, and nucleotide metabolism. For this reason, microbiota alterations, also due to diet modifications, are linked to gut microbiota metabolic activity variations. In our study, biosynthesis of several pathways, such as that of cofactors (e.g., quinones and vitamin B6), diterpenoids, and sugar nucleotides, was generally increased after BC supplementation. These modifications could be related to the increment of some bacterial populations, such as Eggerthellaceae, which are involved in the synthesis of some of these cofactors [67]. On the other hand, biosynthesis of amino acids (i.e., phenylalanine, tyrosine, threonine, and lysine), nucleotides, glycogen, and lipids (i.e., unsaturated fatty acids) was decreased in several tracts of BC groups. The decreased lipids biosynthesis could be related to the decrease of Firmicutes in the colon of both BC groups, as has already been noted in Grass Carp [68]. Isoprenoids, NAD, and GGPP, had contrasting results, while the amino acid degradation pathways increased in most of the intestinal tracts of the BC groups. It is worth highlighting the peculiar behavior of the amino acids’ metabolism as their biosynthesis decreases while the degradation increases. This pattern could be related to the better amino acidic profile of BC compared to feeds of plant origin [69]. It could be responsible for the higher bioavailability of amino acids at the gut level and, therefore, for the lower biosynthetic and higher degradation activity of the microbiota. Interestingly, lactose degradation was increased in the jejunum mucosa-associated microbiota of BC groups. Thus, although the lactose content in the BC diets was low, it was enough to increase its degradation pathways in microbiota. In rabbits, the lactase activity abruptly decreases after weaning and cannot increase after dietary lactose supplementation [70]. For this reason, the lactose supplemented by the diet in the weaned rabbit is not digested by the enzymatic activity of the organism and remains available for microbiota fermentations. It has been previously shown that the substitution of starch by lactose in the rabbit diet causes impaired feed efficiency and greater mortality in fattening animals caused by diarrhoea events [70]. No diarrhoea events were recorded in our study, but further investigations into the effects of colostrum on productive performance are needed. Finally, the Firmicutes:Bacteroidetes ratio (F:B), an important index for the evaluation of the eubiosis, has also been evaluated. Its alterations have been linked to the presence of inflammatory and metabolic diseases in humans, being a good indicator of microbiota homeostasis [71]. Specifically, an increase in its value has been correlated with obesity, while its reduction has been linked to inflammatory bowel disease [72]. In rabbits, the F:B reduced after n-3 PUFA and Goji berries supplementations [8,9]. These studies also showed a progressive decrease in F:B along the gastrointestinal tract [8]. Our findings confirm this pattern, with higher F:B values in the jejunum than the caecum or colon, regardless of the experimental groups. This pattern was in agreement with the differences in the relative abundances of the microbiota of the different intestinal tracts. Our study has also found higher values of the F:B ratio in every mucosal sample when compared to the luminal ones, also reflecting, in this case, the different taxonomic compositions of the two sites. On this topic, the scientific literature is scarce, and another study on mice found opposite results [56]. As regards the diet effect, F:B only showed a decrease in the caecum luminal microbiota of the $2.5\%$ BC group. These findings were consistent with the reduction of Ruminococcaceae and the increase of Bacteroidaceae mentioned above. In rabbits, a decrease of F:B is associated with the administration of different probiotics, although with contrasting results [8,9]. The association between F:B and metabolic disease in the rabbit remains unexplored. Further research will be needed to better investigate the effects of the BC diet supplementation on the histological structure of the rabbit intestine, the immune response, metabolic diseases, and the animals’ productive and reproductive performances. Furthermore, it should be noted that functional predictions and the related pathways analyses performed in this study cannot substitute whole shotgun metagenomics in the evaluation of the actual functions and pathways altered by the BC-supplementation in the diet. As a matter of fact, differential abundance testing results are known to vary between shotgun metagenomics data and amplicon-based metagenome predictions based on the same samples, especially for community-wide pathway predictions [48,73]. Moreover, the short-chain fatty acids analysis could also help in the understanding of the functional modifications of the gut microbiota, together with a deeper investigation of the effect of lactose inclusion in the diet. Finally, since the percentage of BC inclusion in the diet seems to give non-linear results in microbial changes, further studies will have to establish the optimal dose of administration and its balance with the amount of fibre in the feed. ## 5. Conclusions The results of this investigation have evidenced that dietary supplementation with BC could modulate the gut microbiota and its metabolic-associated pathways in fattening rabbits. Although microbial diversity was not strongly modified, the $2.5\%$ BC supplementation changed the phylogenetic microbial composition, especially in the caecum and colon. Clostridia UCG-014, Barnesiellaceae, and Eggerthellaceae were the families most affected in their prevalence by the dietary treatment and altogether suggest a positive microbiota modulation exerted by the BC administration, even if the consequences of these changes should be better investigated. 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--- title: In Vivo Effect of a Nisin–Biogel on the Antimicrobial and Virulence Signatures of Canine Oral Enterococci authors: - Eva Cunha - Ana Filipa Ferreira - Sara Valente - Alice Matos - Luís Miguel Carreira - Marta Videira - Lélia Chambel - Luís Tavares - Manuela Oliveira journal: Antibiotics year: 2023 pmcid: PMC10044209 doi: 10.3390/antibiotics12030468 license: CC BY 4.0 --- # In Vivo Effect of a Nisin–Biogel on the Antimicrobial and Virulence Signatures of Canine Oral Enterococci ## Abstract Periodontal disease is a relevant oral disease in dogs and nisin–biogel has been previously proposed to be used in its control. Enterococci, as inhabitants of the oral cavity with a high genetic versatility, are a reliable bacterial model for antimicrobial studies. Our goal was to evaluate the in vivo influence of the long-term dental application of the nisin–biogel on the virulence and antimicrobial signatures of canine oral enterococci. Twenty dogs were randomly allocated to one of two groups (treatment group—TG with nisin–biogel dental application, or control group—CG without treatment) and submitted to dental plaque sampling at day 0 and after 90 days (T90). Samples were processed for Enterococcus spp. isolation, quantification, identification, molecular typing and antimicrobial and virulence characterization. From a total of 140 enterococci, molecular typing allowed us to obtain 70 representative isolates, mostly identified as E. faecalis and E. faecium. No significant differences ($p \leq 0.05$) were observed in the virulence index of the isolates obtained from samples collected from the TG and CG at T90. At T90, a statistically significant difference ($$p \leq 0.0008$$) was observed in the antimicrobial resistance index between the isolates from the TC and CG. Oral enterococci were revealed to be reservoirs of high resistant and virulent phenotypes. ## 1. Introduction Enterococci are commensal inhabitants of the intestinal tract of humans and other mammals [1]. These bacteria have a high genome plasticity, resulting in the ability of acquiring, conserving and disseminating genetic determinants, being an interesting bacterial model for antimicrobial studies [2,3]. Nevertheless, Enterococcus faecium, classified by the World Health Organizaion (WHO) as a high-priority pathogen for the research and development of new antimicrobial compounds, along with other enterococcal species, may become an opportunistic pathogen and be associated with life-threatening infections [2,4,5]. If present in the dental plaque microbiota, enterococci can participate in chronic endodontic lesions and periodontitis, being associated with systemic consequences both in humans and dogs [6,7,8,9]. Among Enterococcus species, E. faecium and E. faecalis are the two most common species isolated from clinical specimens in dogs [9,10,11]. Periodontal disease (PD) is a widespread oral inflammatory disease, presenting high impact in the veterinary field. Studies describe PD prevalences higher than $80\%$ in dogs over 2 years of age, reaching $100\%$ in some breeds [12,13]. Additionally, PD may be associated with several local and systemic consequences, reinforcing the impact of this disease on global animal health [14,15,16,17]. Considering that, new therapeutic and preventative approaches are required to control PD in these animals. Previous studies have focused on the potential of the antimicrobial peptide nisin incorporated in a guar gum biogel as a promising compound for PD control in dogs [3,18,19,20]. Cunha and collaborators have demonstrated that the nisin–biogel has anti-biofilm activity against different bacterial species from the canine dental plaque, keeping its antimicrobial activity in the presence of canine saliva and over two years of storage at different temperatures [3,19,20]. This anti-biofilm ability may contribute to the prevention of biofilm formation by inhibiting the bacterial dental attachment, but the nisin–biogel can also act on mature biofilms, since nisin can penetrate the biofilm structure without being inactivated by its matrix, leading to bacterial death and biofilm destruction [3,19,21,22]. Additionally, the nisin–biogel showed no toxicity against eukaryotic cells [20]. However, previous in vitro studies have revealed that nisin may induce changes in the antimicrobial resistance profile of enterococci [23]. In order to understand the in vivo impact of the dental application of the nisin–biogel to dogs on the virulence and antimicrobial profile of oral enterococci, we evaluated the influence of the in vivo long-term dental application of the nisin–biogel in the virulence and antimicrobial signatures of oral enterococci, using samples collected during a previous randomized controlled clinical trial with dogs. ## 2. Results Supragingival dental plaque samples were collected from twenty dogs at two timepoints (T0—timepoint 0, beginning of the study; T90—timepoint 90, 90 days after). Each animal was allocated to one of two groups: the control group ($$n = 10$$), to which no treatment was applied, or the treatment group ($$n = 10$$), in which each animal was submitted to a dental, topical application of the nisin–biogel (200 µg/mL), each 48 h. Then, samples were processed for Enterococcus spp. isolation, quantification, identification, molecular typing and antimicrobial and virulence characterization. ## 2.1. Enterococci Identification and Typing At T0, 17 animals were positive for oral enterococci, while at T90, 18 animals were positive for this bacterial group. From each positive sample, four colonies with macroscopic morphology compatible with enterococci were selected, allowing us to collect a total of 68 isolates from samples obtained at T0 and 72 isolates from those collected at T90, in a total of 140 isolates. Mean enterococci counts obtained from T0 samples were of 3 × 107 CFU/mL, while in T90 samples a reduction in bacterial quantification was observed, with a mean count of 5.8 × 105 CFU/mL. Considering only the treatment group, T0 samples presented an enterococci concentration of 2.2 × 107 CFU/mL, while in the T90 samples the concentration was 5.9 × 105 CFU/mL. In the control group, a value of 4.1 × 107 CFU/mL was obtained in the T0 samples, while in the T90 samples an enterococci count of 5.6 × 105 CFU/mL was observed. Isolate’s genotyping allowed us to gather a collection of 70 representative isolates, including 38 enterococci collected from the T0 samples and 32 isolates from the T90 samples (See Supplementary File S1). Species identification of the 38 enterococci recovered from the T0 samples revealed that $39.47\%$ ($$n = 15$$/38) of the isolates belonged to the species E. faecalis, $18.42\%$ ($$n = 7$$/38) to E. faecium and $42.11\%$ ($$n = 16$$/38) were identified as Enterococcus spp. ( Figure 1). Considering the isolates recovered from samples collected at the end of the clinical trial (timepoint 90), species distribution was as follows: $46.88\%$ ($$n = 15$$/32) of the isolates were identified as E. faecalis, $31.25\%$ ($$n = 10$$/32) as E. faecium, $12.50\%$ ($$n = 4$$/32) as E. hirae and $9.34\%$ ($$n = 3$$/32) as Enterococcus spp. ( Figure 1). E. durans isolates were not detected in any of the oral samples. ## 2.2. Enterococci Virulence Signatures The number of isolates that phenotypically expressed the virulence determinants under study is described in Table 1, being organized by timepoint and animal group. The ability of producing biofilm was the most prevalent virulence factor detected in this study, regardless of the sample group or timepoint, followed by lipase and haemolysin production. The isolates mean virulence index is presented in Table 1. A slight increase in the mean virulence index was observed in the isolates from the TG samples collected at T0 and T90, but without statistical significance (p-value > 0.05). No significant differences (p-value > 0.05) were observed when comparing the virulence index of the isolates obtained from the TG and CG samples collected after nisin–biogel application at T90. ## 2.3. Enterococci Antimicrobial Resistance Profile It was possible to observe a mean multiple antimicrobial resistance (MAR) index equal or higher than 0.4 in all the isolates from the bacterial collection, independently of the timepoint or test group of origin. At the end of the clinical trial (T90), a statistically significant difference ($$p \leq 0.0008$$) was observed between the mean MAR index of the isolates from the treatment and control groups. Additionally, the MAR values increased from timepoint 0 to timepoint 90 in both groups (TG and CG) (Table 2). Isolates were classified as showing multidrug resistance (MDR) when they were non-susceptible to at least one agent in three or more different antimicrobial classes [24]. A high MDR prevalence was detected in the isolates under study, independently of the timepoint or test group, ranging from 90 to $100\%$ (Table 2). Considering high-level aminoglycoside resistance (HLAR) determination, the control group showed a higher number of positive isolates in comparison to the treatment group (Table 2). ## 3. Discussion Antimicrobial peptides (AMPs) are relevant molecules in the fight against antimicrobial resistance dissemination. Until now, several AMPs have been investigated with this aim, with nisin being one of the most well-studied compounds from this antimicrobial class [25]. Recent reports have shown promising results from in vitro and in vivo studies regarding nisin and nisin–biogel use for canine PD control [3,18,19,20,26]. PD onset includes the formation of a dental plaque polymicrobial biofilm composed by several bacterial species [27,28,29,30]. Among this complex biofilm, enterococci can be found, being commensal inhabitants of the oral cavity of dogs, with high genetic plasticity [8]. This study analysed oral samples obtained from animals that participated in a previous clinical trial [31] in which the efficacy of the administration of the nisin–biogel for PD prevention in dogs was evaluated. In the present work, enterococci were used as bacterial models to study the in vivo influence of nisin–biogel on virulence and antimicrobial resistance profiles. It was possible to isolate enterococci from the samples collected from the majority of dogs at the start ($$n = 17$$/20) and at the end ($$n = 18$$/20) of the clinical study, allowing us to observe a reduction in enterococci counts from T0 to T90 in the animals from both test groups (CG or TG). Considering that, we believe that the dental application of the nisin–biogel had no direct effect on bacterial counts, and the observed reduction may be attributed to the dental plaque scaling procedure performed on all animals at T0, as suggested by other studies [32,33,34]. In fact, the animals from both groups (CG and TC) showed a reduction in their periodontal indices (gingivitis, dental plaque accumulation and periodontal pocket depth) at the end of the clinical trial [31], which was also related to the scaling procedure, in agreement with the results from the present report. Molecular characterization of isolates allowed us to establish a bacterial collection of 70 representative enterococci. A predominance of E. faecalis and E. faecium was observed in dental plaque samples, agreeing with other studies on enterococci from healthy dogs [8,9,10,35]. In the animals submitted to nisin–biogel treatment (TG), it was possible to observe a reduction of Enterococcus spp.; however, animals from both test groups (TG and CG) showed similar species distribution at the end of the clinical trial (Figure 1). Several virulence determinants have been identified and studied in enterococci from different origins [36,37,38,39,40]. The production of virulence factors has been associated with host immune system evasion, degradation of substrates, and adhesion to cell surfaces, favouring disease establishment [41]. In our study, biofilm forming ability was the most prevalent virulent determinant, followed by lipase production and haemolytic activity (Table 3). Several studies have reported similar virulence factors prevalences [42,43]. Biofilm forming ability is linked to an increased antimicrobial resistance profile and recalcitrant conditions, impairing bacterial infections resolution via conventional treatments [44,45]. The complex biofilm environment allows bacteria to interact and exchange genetic material, protects them from the host immune system and from antimicrobials inhibitory action and helps bacterial survival and dissemination [46,47]. Along with a high biofilm ability, the isolates from this study presented mean virulence indexes ranging from 0.39 to 0.49 (Table 3), which may indicate that the enterococci under study have the potential to become pathogenic, as stated by Singh at al. [ 48]. In addition, it was possible to observe an increase in the mean virulence index of the isolates from the animals of the treatment group from T0 to T90, which was not observed in the control group. This may suggest that the nisin–biogel application may have contributed to a slight virulence increase, probably by inducing selective pressure in the oral cavity. Along with virulence evaluation, to better understand bacterial pathogenic potential it is essential to study their antimicrobial resistance signatures. Antimicrobial resistance (AMR) is a major health problem of our time. Before applying new antimicrobial compounds, as the case of the nisin-biogel, it is essential to study their potential contribution to AMR development. A previous in vitro study has proposed that nisin may lead to an increase in antimicrobial resistance in oral enterocicci [23]. In this study, a MDR prevalence of 90 to $100\%$ was detected among the isolates, independently of the timepoint or test group of the associated samples. Previous studies also have described high resistance profiles in canine enterococci, with Bertelloni et al. [ 9] observing an MDR prevalence of $61\%$ and Stepien-Pysniak et al. [ 10] describing a MDR prevalence of $86\%$ in enterococci obtained from healthy dogs. In addition, Cunha et al. [ 2020] [21] observed that $75\%$ of the enterococci obtained from the oral cavity of dogs with PD had an MDR profile. These results reinforce the relevance of enterococci in AMR dissemination and maintenance. Considering the AMR topic, the high-level aminoglycoside resistance of the enterococci collection was also evaluated, using gentamycin and streptomycin. It is known that enterococci are intrinsically resistant to aminoglycosides [49]; however, the aminoglycosides gentamycin and streptomycin may be successfully used in combination with β-lactams for the treatment of enterococcal infections if HLAR is not detected [49,50]. In agreement with the MDR profile detected in our study, we observed a parallel increase in HLAR in isolates from both test groups during the clinical trial. Several studies have revealed HLAR prevalences ranging from $21\%$ to $47.1\%$ in enterococci from an animal origin, with isolates showing higher resistance to streptomycin [9,10]. In our study, we observed a higher HLAR, but without a significant difference between gentamycin or streptomycin resistance. Sienko and collaborators [46] also reported high HLAR prevalences in human enterococci isolates from different countries. Unfortunately, the HLAR is now extremely widespread, and the synergistic effect between β-lactams and aminoglycosides is being lost, hindering the treatment of life-threatening enterococci infections [46]. By evaluating the MAR index of the isolates under study, high values were observed in enterococci from both groups, with a similar increase during the 90-days trial. The isolates from the control group showed the highest MAR value, being statistically different from the MAR index of the isolates from the TG at T90. According to Singh and collaborators [48], isolates with MAR index ≥0.3 and virulence index ≥0.5 are highly threatening isolates, with a significant pathogenic potential for humans or animals. Considering that, the isolates from our collection may have the potential to became pathogenic. Comparing the results from the control and treatment groups, nisin seemed to have a low influence on in vivo AMR dissemination, which is a promising result supporting its potential clinical application. This study presented some limitations, including the number of animals that participated in the trial, which could be higher to allow to evaluate a larger number of samples. Despite that, it was possible to isolate enterococci from the oral cavity of the majority of the animals under trial, allowing the construction of a relevant bacterial collection. It is important to notice that, to the authors knowledge, this was the first in vivo evaluation of the potential effect of nisin–biogel on the dissemination of antimicrobial resistance and virulence bacterial patterns. When compared with in vitro studies, a low effect on Enterococcus antimicrobial and virulence signatures was observed, which reinforces that nisin–biogel is a valuable compound to be used for PD control. ## 4.1. Nisin–Biogel Preparation Nisin–biogel was prepared as described elsewhere [3,19,20,26,51]. Briefly, a nisin solution of 1000 μg/mL was obtained by dissolving 1 g of nisin powder ($2.5\%$ purity, 1000 IU/mg, Sigma-Aldrich, St. Louis, MO, USA) in 25 mL of HCl (0.02 M) (Merck, Alges, Portugal). A $1.5\%$ guar-gum biogel (w/v) solution was obtained by dissolving 0.75 g of guar gum (Sigma-Aldrich, St. Louis, MO, USA) in 50 mL of sterile distilled water, which was then heat sterilized via an autoclave. Afterwards, nisin was incorporated within the biogel in a proportion of 1:1, in order to obtain a gel with a final concentration of 200 µg/mL to be used in the clinical trial [26,31]. The nisin–biogel concentration was selected based on previous reports regarding its cytotoxicity [20]. For posology establishment, standard procedures for PD prevention were considered [52,53]. ## 4.2. Clinical Trial and Sample Collection All samples used in this study were obtained from a clinical trial previously performed by our team (Figure 2) [48]. In this former trial, a total of 20 dogs were selected from an official animal’s rescue institution, according to the Veterinary Oral Health Council (VOHC) instructions for testing compounds aiming at PD prevention. The clinical trial was approved by the Ethical Committee for Research and Teaching (CEIE) of the Faculty of Veterinary Medicine, University of Lisbon, Portugal (N/Ref $\frac{014}{2020}$) [31]. Briefly, the dogs that participated in this study were healthy dogs, more than 2 years old, without severe PD and no history of antimicrobial therapy in the previous month. All dogs were housed in the same facilities and had access to the same food and housing routines. Prior to the study, all animals were submitted to a clinical examination and oral handling/cleaning, and a complete blood analysis to detect any deviations that would impair their inclusion in the study was performed. Then, the animals were randomly allocated to one of two groups: the treatment group ($$n = 10$$) or control group ($$n = 10$$). Animals from the treatment group were submitted to the dental topical application of the nisin–biogel (200 µg/mL) every 48 h; animals from the control group were not submitted to any treatment. Animals were kept in this trial for 90 days [31]. All dogs were submitted to a supragingival dental plaque sample collection using a swab (AMIES®, VWR, Amadora, Portugal), applied to all of the dental surface, at day 0 (Timepoint 0—T0) and at day 90 (Timepoint 90—T90). Swabs were transported to the Laboratory of Microbiology and Immunology, Faculty of Veterinary Medicine, University of Lisbon, and processed for Enterococcus spp. isolation, quantification, identification, and characterization, according to Semedo-Lemsaddek et al. [ 37], Oliveira et al. [ 8] and Belo et al. [ 54]. ## 4.3. Enterococci Identification and Typing Slanetz and Bartley agar medium (SBA, PanReac AppliChem, Barcelona, Spain) was used for performing a presumptive selection and quantification of enterococci, by using ten-fold serial dilutions, a medium inoculation and determination of the colony forming units [8,37]. From each animal and timepoint (T0 and T90), four typical single colonies presenting distinct morphologies were randomly selected from the SBA plates for further characterization using conventional microbiological procedures [8]. Then, molecular identification at the genus level was performed via conventional PCR, according to Ke et al. [ 55]. Identification at species level was performed via a multiplex PCR using species-specific primers and conditions previously described by Jackson et al. [ 51]. Genomic typing was performed using the primers OPC19 and (GTG)5 in independent mixtures, as described by Semedo-Lemsaddek et al. [ 37] and Oliveira et al. [ 8]. The profiles obtained were analysed using BioNumerics® 6.6 (Applied Maths, Kortrijk, Belgium), through a hierarchical numerical approach with Pearson correlation coefficient (optimization $1.5\%$) and the unweighted pair group method with arithmetic average (UPGMA) as the agglomerative clustering. The isolates’ similarity was evaluated using a composite analysis of the fingerprints obtained with the two primers. The cut level applied to select representative isolates from each timepoint (T0 and T90) was $95.9\%$, since it was the reproducibility value determined as the average similarity value of all replicate pairs. The selected isolates were further evaluated regarding their virulence and antimicrobial resistance signatures. All primers used in this study are presented in Table 3. Table 3 \| Target \| Primers \| Sequence \| Product Size \| Reference \| \| --- \| --- \| --- \| --- \| --- \| \| Enterococcus spp. \| Ent 1 \| 5′-TACTGACAAACCATTCATGATG-3′ \| 112 bp \| [50] \| \| Enterococcus spp. \| Ent 2 \| 5′-AACTTCGTCACCAACGCGAAC-3′ \| 112 bp \| [50] \| \| E. faecalis \| FL1 \| 5′-ACTTATGTGACTAACTTAACC-3′ \| 360 bp \| [56] \| \| E. faecalis \| FL2 \| 5′-TAATGGTGAATCTTGGTTTGG-3′ \| 360 bp \| [56] \| \| E. faecium \| FM1 \| 5′-GAAAAAACAATAGAAGAATTAT-3′ \| 215 bp \| [56] \| \| E. faecium \| FM2 \| 5′-TGCTTTTTTGAATTCTTCTTTA-3′ \| 215 bp \| [56] \| \| E. hirae \| HI1 \| 5′-CTTTCTGATATGGATGCTGTC-3′ \| 187 bp \| [56] \| \| E. hirae \| HI2 \| 5′-TAAATTCTTCCTTAAATGTTG-3′ \| 187 bp \| [56] \| \| E. durans \| DU1 \| 5′-CCTACTGATATTAAGACAGCG-3′ \| 295 bp \| [56] \| \| E. durans \| DU2 \| 5′-TAATCCTAAGATAGGTGTTTG-3′ \| 295 bp \| [56] \| \| Genotyping \| OPC \| 5′-GTTGCCAGCC-3′ \| [200–3000] \| [8,37] \| \| Genotyping \| (GTG)5 \| 5‘-GTGGTGGTGGTGGTG-3′ \| [200–3000] \| [8,37] \| ## 4.4. Enterococci Virulence Signature The production of gelatinase, lipase, DNase, lecithinase, proteinase, hemolysin and biofilm were evaluated using plate assays, according to Freeman et al. [ 57], Semedo-Lemsaddek et al. [ 37] and Fernandes et al. [ 58]. Virulence factors were evaluated after streaking the colonies onto the respective medium and incubating at 37 °C for 48 h, except for haemolysis and biofilm evaluation. Briefly, for gelatinase activity evaluation, the Nutrient Gelatin Medium (Oxoid, Basingstoke, Hampshire, UK) was used, with the presence of a liquid medium after incubation indicating a positive result. Lipase production was evaluated using Spirit blue agar and lipase reagent (BD Life Sciences, VWR, Amadora, Portugal), with lipolytic organisms showing a white clearing beneath and surrounding the colonies after incubation. The ability to produce DNase was evaluated using DNAse test agar (Remel, Termo Scientific, Lenexa, KS, USA) supplemented with blue toluidine, and the presence of pink colonies after incubation was considered a positive result. Lecitinase production was determined using Tryptic Soy agar (VWR, Leuven, Belgium) supplemented with $10\%$ of egg yolk emulsion (VWR, Leuven, Belgium), and a positive activity resulted in the development of a white precipitate around colonies after incubation. Proteinase activity was determined using a skim milk medium (VWR, Leuven, Belgium), and colonies presenting a white halo after incubation being considered positive. Hemolysin production was evaluated after isolates streaking on Columbia agar supplemented with $5\%$ sheep blood (Biomeriux, Marcy-l’Étoile, France), incubated at 37 °C for 72 h in anaerobic conditions. The presence of a clearing halo around the colonies was interpreted as a positive result (β-haemolysis), and the absence of a clearing (ɣ-haemolysis) or the presence of a greenish zone around the colonies (α-haemolysis) were considered negative results. Biofilm forming ability was evaluated using Congo Red agar medium (Sigma-Aldrich, St. Louis, MO, USA), incubated for 72 h at 37 °C, after which the development of black colonies indicated the isolates’ ability to produce biofilm. According to the time required for biofilm production, isolates were considered as strong (24 h), moderate (48 h) or weak biofilm producers (72 h). The evaluation of the isolate’s phenotypic virulence profile included the determination of their virulence index (number of positive virulence factors/number of virulence factors tested). ## 4.5. Enterococci Antimicrobial Resistance Profile Isolates’ antimicrobial resistance profile was determined using the disk diffusion method according to the guidelines of the Clinical and Laboratory Standards Institute (CLSI) (M100S and Vet01-02) [59,60]. A total of 12 antimicrobials (Oxoid, Hampshire, UK), presented in Table 4, were used, being selected based on their relevance to veterinary medicine, as well as to public health. Isolates were categorised according to their multidrug-resistant patterns, as described by Magiorakos et al. [ 24], and by their multiple antibiotic resistance (MAR) indices (number of antibiotics to which isolates were resistant/number of antibiotics tested) [58,61]. In addition, isolates with non-susceptibility to high doses of gentamicin (120 µg) and/or streptomycin (300 µg) were considered to present high-level aminoglycoside resistance (HLAR) [50,59,60]. ## 4.6. Statistical Analysis Data statistical analysis was carried out using Microsoft Excel 2016®. Differences in the virulence and MAR index between groups and timepoints were evaluated using Student’s t test. Quantitative variables are expressed as mean values ± standard deviation. A confidence interval of $95\%$ was considered, with a p-value ≤ 0.05 indicating statistical significance. ## 5. Conclusions Enterococci are organisms with an impressive genetic versatility and unparalleled ability to recruit and express antimicrobial resistance and virulence determinants. Their commensal nature allows them to colonize healthy individuals and quickly participate in complicated infections. Present in the oral cavity, they are considered an interesting bacterial model to be used in odontology and pharmaceutical research, since they can act as reservoirs of resistant and virulent phenotypes. The long-term dental application of the nisin–biogel to dogs showed to be an interesting alternative to be used for PD control, with a low effect on Enterococcus antimicrobial and virulence profiles. 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--- title: 'RETRACTED: Isolation and Characterization of a Novel Lytic Phage, vB_PseuP-SA22, and Its Efficacy against Carbapenem-Resistant Pseudomonas aeruginosa' authors: - Addisu D. Teklemariam - Rashad R. Al-Hindi - Mona G. Alharbi - Ibrahim Alotibi - Sheren A. Azhari - Ishtiaq Qadri - Turki Alamri - Ahmed Esmael - Steve Harakeh journal: Antibiotics year: 2023 pmcid: PMC10044225 doi: 10.3390/antibiotics12030497 license: CC BY 4.0 --- # RETRACTED: Isolation and Characterization of a Novel Lytic Phage, vB_PseuP-SA22, and Its Efficacy against Carbapenem-Resistant Pseudomonas aeruginosa ## Abstract Carbapenem-resistant Pseudomonas aeruginosa (CRPA) poses a serious public health threat in multiple clinical settings. In this study, we detail the isolation of a lytic bacteriophage, vB_PseuP-SA22, from wastewater using a clinical strain of CRPA. Transmission electron microscopy (TEM) analysis identified that the phage had a podovirus morphology, which agreed with the results of whole genome sequencing. BLASTn search allowed us to classify vB_PseuP-SA22 into the genus Bruynoghevirus. The genome of vB_PseuP-SA22 consisted of 45,458 bp of double-stranded DNA, with a GC content of $52.5\%$. Of all the open reading frames (ORFs), only 26 ($44.8\%$) were predicted to encode certain functional proteins, whereas the remaining 32 ($55.2\%$) ORFs were annotated as sequences coding functionally uncharacterized hypothetical proteins. The genome lacked genes coding for toxins or markers of lysogenic phages, including integrases, repressors, recombinases, or excisionases. The phage produced round, halo plaques with a diameter of 1.5 ± 2.5 mm on the bacterial lawn. The TEM revealed that vB_PseuP-SA22 has an icosahedral head of 57.5 ± 4.5 nm in length and a short, non-contractile tail (19.5 ± 1.4 nm). The phage showed a latent period of 30 min, a burst size of 300 PFU/infected cells, and a broad host range. vB_PseuP-SA22 was found to be stable between 4–60 °C for 1 h, while the viability of the virus was reduced at temperatures above 60 °C. The phage showed stability at pH levels between 5 and 11. vB_PauP-SA22 reduced the number of live bacteria in P. aeruginosa biofilm by almost five logs. The overall results indicated that the isolated phage could be a candidate to control CRPA infections. However, experimental in vivo studies are essential to ensure the safety and efficacy of vB_PauP-SA22 before its use in humans. ## 1. Introduction Pseudomonas aeruginosa (P. aeruginosa, PA) is a Gram-negative bacterium involved in opportunistic infections in humans. P. aeruginosa is the main cause of nosocomial infections, which can result in the demise of those infected, especially among immunosuppressed patients with severe wounds, chronic obstructive lung disease, cystic fibrosis, ventilator-associated pneumonia, and catheter-associated chronic urinary tract infections. Some P. aeruginosa strains cause life-threatening infections due to their capability to form biofilms and their natural and/or acquired drug resistance [1,2,3]. It has been long established that antibiotics play a crucial role in healthcare, food processing, agriculture, veterinary medicine, and many other industries. Nevertheless, the overuse and misuse of antibiotics have caused bacteria to evolve escape mechanisms and become resistant to the majority of antibiotics [4,5]. Furthermore, the shortage of new antibiotics developed over the past few years has left us with very limited therapeutic agents against pathogenic bacteria. Antibiotic resistance has been predicted to cause 10 million deaths annually by 2050 [4,6]. Antibiotics such as carbapenems have been launched for the treatment of serious P. aeruginosa infections. These antibiotics are considered the last line of treatment against diseases caused by multidrug-resistant (MDR) Gram-negative bacteria [7]. As carbapenems are the last therapeutic option for the control of bacterial infections, carbapenem-resistant P. aeruginosa (CRPA) has become a serious public health threat in several clinical settings [8]. Therefore, P. aeruginosa has been classified as a ‘Priority 1 critical pathogens by WHO, for which new antibacterial is urgently required’ [9]. Biofilms are formed as a network of microbial communities, protecting the microbes from any external damage while providing mechanical stability and adhesion to surfaces [10]. Bacterial biofilms can resist antibiotics due to impaired diffusion of the antibiotics, selection of resistant mutants, antibiotic efflux, oxygen and nutrient limitation, expression of biofilm-specific genetic mechanisms, or survival of tolerant cells [11]. The ability of microbial biofilms to resist host immunity, as well as antibiotics, is the main cause of persistent infections [11]. P. aeruginosa biofilms are significant virulence factors and may influence chronic wound healing, especially in diabetic patients with non-healing ulcers on their lower extremities, as well as chronic burn wound infections [12]. Biofilms of P. aeruginosa bacteria are especially deadly in patients with cystic fibrosis [13]. The biofilm protects the microbial organisms from disinfectants as well as antibiotics, maintaining their survival on biotic and abiotic surfaces [14]. P. aeruginosa forms thick biofilms due to the production of large quantities of alginate, a polysaccharide that consists of D-mannuronate and Lguluronate units. Compared to the planktonic forms, the biofilm form of P. aeruginosa can exhibit >100-fold greater resistance to conventional antibiotics [15,16]. In a previous study using a mouse model of early lung infection, researchers observed collateral damage in healthy cells associated with the induction of neutrophils in response to P. aeruginosa biofilms. This occurred through a phenomenon called “frustrated phagocytosis,” with the surrounding healthy host tissues being attacked by the toxins secreted by activated phagocytic cells [17]. Currently, available antibiotics may reduce the bacterial population in biofilms; however, they cannot fully eliminate the biofilm [18,19], and therefore, relapses of biofilm infections may occur. Hence, amputation of infected tissues or removal of implanted materials, as well as successive durable antimicrobial therapy, might be required to terminate the biofilm-associated infections. Furthermore, new treatments aimed at eradicating biofilms are needed; in this context, bacteriophages could provide an interesting possible alternative [16,20,21]. In terms of biofilm eradication, phages present fascinating characteristics, as they encode enzymes that enable them to directly penetrate and destroy biofilms. Depolymerases are phage-encoded enzymes that specifically destroy exopolysaccharide matrix components, preventing their formation by distracting the quorum-sensing network and enhancing the penetration of the phage [22]. These enzymes are commonly found as structural proteins such as baseplates and tail fibers but may also exist as soluble proteins during the replication cycle. They are involved in the initial attachment to the host cell surface and the destruction of the bacterial capsule to facilitate the phage infection process [15]. Phage therapy, which denotes the bacteriophage-based treatment of bacterial infections, could provide a helpful therapeutic approach for fighting infections caused by multidrug-resistant P. aeruginosa infections [23,24,25]. Knowledge about the potential therapeutic phages is vital for the development of phage therapy. Nevertheless, phages are not frequently used therapeutically due to various constraints, such as inadequate clarity of phage formulations, the narrow specific spectrum of activity, poor viability or stability of phage preparations, and a lack of understanding of the mechanism of action of phages and their heterogeneity [26,27]. Despite these constraints, the global increase in MDR pathogenic bacteria has led to the revival of interest in phage-based therapeutic approaches [23]. Phage therapy against P. aeruginosa biofilms has been assessed in both in vivo and in vitro studies. For instance, phage PELP20 showed a 3-log phage reduction of biofilm in a mouse model of cystic fibrosis infection [28]. Similarly, a temperate phage ΦPan70 significantly reduced P. aeruginosa biofilm production in a mouse model [29]. In another study, the combined use of ΦMR299-2 and ΦNH-4 eliminated the biofilm and killed P. aeruginosa growing in the cystic fibrosis bronchial epithelial CFBE41o-cell line. In in vitro experiments, bacteriophages such as M-1, PB1-like, phiKZ-like, and LUZ24-like phages have been shown to be effective in destroying the planktonic cells as well as the biofilm [30,31]. Despite the above-mentioned reports, very limited lytic bacteriophages have been characterized against CRPA infections so far [32]. Hence, this study was conducted to isolate and characterize a lytic bacteriophage targeting CRPA. In addition, the anti-biofilm effect of the isolated phage was investigated. ## 2.1. Bacterial Strains and Culture Condition The clinical strain of CRPA (P. aeruginosa strain B10) was obtained from King Abdulaziz University hospital and used as a host for the isolation of vB_PseuP-SA22 from wastewater. Identification of the organism was carried out using an API 20 E test kit (bioMerieux Industry, Hazelwood, MO, USA), a VITEK identification system (bioMèrieux Inc., Durham, NC, USA) using GN ID REF21341 cards, and 16S rRNA molecular analysis and sequencing. The bacterial strains used to evaluate the host range of vB_PseuP-SA22 are listed in Table 1. Some of these isolates were obtained from the King Fahd Medical Research Center (KFMRC), while the others were purchased from American Type Culture Collection (ATCC). All bacterial isolates used in our study were preserved using $25\%$ glycerol at −20 °C until used. ## 2.2. Antibiotic Sensitivity Test Antimicrobial susceptibility was assessed using the standard agar disk diffusion method following the CLSI guideline (Supplementary Table S1). P. aeruginosa ATCC 27853 was used as a positive control [33]. ## 2.3. Enrichment and Isolation of Bacteriophages To isolate potential lytic bacteriophages, we collected wastewater samples from Jeddah Wastewater Treatment Plant, Saudi Arabia. The samples were transported to the KFMRC laboratory on ice, stored at 4 °C, and processed within 24 h. The wastewater samples were centrifuged at 10,000× g for 10 min, and the supernatant was passed through 0.22 µm filters (Fischer Scientific, Ottawa, ON, Canada) to remove unnecessary particulates (including bacterial cells) and debris. The filtrate was then directly used for phage isolation [34]. Briefly, 20 mL of the filtrate was added to 20 mL of an overnight culture of CRPA in double-strength nutrient broth supplemented with 2 mM CaCl2. The culture was placed in a shaking incubator with gentle shaking (100 rpm) at 37 °C for 48 h. Afterward, it was centrifuged at 6000× g for 12 min at 4 °C, and the supernatant was filtered using 0.22 µm size filters to exclude host bacteria and contaminants. Finally, the filtrates were stored at 4 °C until further use. ## 2.4. Spot Assay The presence of phage in the lysate was determined by spot assay [35]. In brief, 0.1 mL of exponential phase host culture was mixed with 4 mL of molten soft agar ($0.7\%$ w/v agar), then poured on the surface of nutrient agar plates to create a lawn. Thereafter, 5 µL of the crude phage extract was spotted on top of the soft agar and kept in a laminar airflow for 20 minutes to allow for viral adsorption. The inoculated plate was incubated overnight at 37 °C. The sample was considered positive for phage if it produced a clear inhibition (lytic) zone. ## 2.5. Purification Purification of the phage was carried out through a double-layer agar assay (DLA), as described elsewhere [36]. Briefly, typical phage plaques were picked up from the soft agar layer by touching the top layer using a 1 mL pipette tip and transferred into 500 µL sterile PBS (pH 7.4) suspension. The suspension was then kept at 4 °C overnight for proper diffusion of phages from the soft agar. Thereafter, 10-fold serial dilutions of the purified lysates were conducted, and DLA assay was carried out to quantify the phage titer and the uniformity of the plaques. The purification process was repeated until similar plaques were obtained. The purified lysate was stored at 4 °C. ## 2.6. Concentration of Phages A full-plate lysate method was used to increase the titer of bacteriophages, following the technique detailed in [37,38]. Confluent lysis zone was produced in the top agar overlay, and 8 mL of PBS was poured over the top agar. Then, the plate was kept at 4 °C overnight for proper diffusion of the virions from the soft agar into PBS. Thereafter, the suspension was aspirated with a 10 mL sterile syringe and transferred into a test tube. The collected suspension was then centrifuged at 8000× g for 5 min, and the supernatant was passed through 0.22 µm filters to eliminate bacterial debris. The purified phage lysate was then used to determine the titer of phage using the DLA technique. ## 2.7. Bacteriophage Titer Determination The purified phage lysate was diluted 10-fold in PBS solution and plated using the DLA technique. The plaques on each plate were individually counted. To calculate the phage titer, we used the last plate, which had plaque counts ranging from 30–300. The titer was calculated, and the results were recorded as PFU mL− 1 [39]. The size of the plaque was measured using a ruler, and photographs were captured using a digital camera. ## 2.8.1. Thermal Stability The thermal stability test was conducted according to Jurczak-Kurek et al. with slight modifications [40]. Briefly, micro-centrifuge tubes containing a mixture of 100 μL of the purified phage lysate (1 × 108 FU/mL) and 900 μL PBS (pH 7.4) were incubated in a water bath at different temperatures (4 °C, 40 °C, 50 °C, 60 °C, 70 °C, 80 °C, or 90 °C) for 1 h. Phage suspended in an equal volume of PBS (pH 7.5) kept at 4 °C was used as a control. Then, the phage titer after treatment was determined by the DLA method. This experiment was carried out in triplicate. ## 2.8.2. Phage pH Stability Test The pH stability of the vB_PseuP-SA22 was tested as described elsewhere, with minor adjustments [41]. The pH of fresh/sterile nutrient broth (pH 2–14) was adjusted, using a pH meter, by adding 1 M HCl and NaOH drop by drop. Nine milliliters of pH-adjusted medium was mixed with 1 mL of phage suspension and incubated at 37 °C for 1 h. Then, the titers of vB_PseuP-SA22 in each suspension were determined using the DLA method. This experiment was carried out in triplicate. ## 2.9. Bacterial Reduction Assay The in vitro host reduction ability of vB_PseuP-SA22 was assessed as described elsewhere, with minor modifications [42]. Briefly, 100 μL of P. aeruginosa culture (OD 0.4, 107 CFU/mL) was added to 100 μL of diluted isolated phage (104–109 PFU/mL) in microtiter plate wells. Thus, 0.001, 0.01, 0.1, 1, 10, and 100 multiplicities of infection (MOI) were generated. The bacterial reduction potential was measured by DLA at different time intervals over 48 h. Bacterial culture without phage (OD 0.4) was used as a control (100 μL of bacterial culture mixed with 100 µL of fresh nutrient broth). The results are presented as mean values ± SD from three independent experiments, and the bacterial reduction curve (CFU/mL vs. time) was also plotted. ## 2.10. Determination of Optimal Multiplicity of Infection (MOI) The bacterial cultures in the exponential phase were washed away with PBS three times and adjusted to analogous concentrations of 106, 107, 108, 108, 108, and 108 CFU/mL, then the phages and bacteria were mixed with MOIs of 100, 10, 1, 0.1, 0.01, and 0.001, respectively. Thereafter, the suspension was incubated for 2 h at 37 °C, and the titer of phages was carried out by the DLA method [43]. The optimal MOI was determined according to the ratio of the specified titer (PFU/mL) of phages to the specified concentration of host bacteria (CFU/mL); in particular, the MOI value attaining the highest phage titer was considered the optimal MOI. ## 2.11. Phage Adsorption Assay The adsorption rate of phages onto the surface of host cell was assessed based on a formerly used protocol [44]. The exponential phase bacterial culture was collected and re-suspended in sterile nutrient broth to reach an OD600 of 0.4. Then, the phage was added at an MOI of 0.1, and the adsorption was monitored at 37 °C for 90 min. One milliliter aliquot was collected at regular time intervals and centrifuged (10,000× g, 10 min). The supernatant was filtrated using a 0.22 μm pore-size filter. The titers of the phage in the original phage stock (t0) and supernatant (t1) were determined by the DLA technique. The adsorption rate was estimated as (t0 − t1)/t0. ## 2.12. One-Step Growth Curve A cycle growth pattern test was performed using vB_PseuP-SA22 and its host bacteria in order to investigate the latent period and the burst size, as described by others, with minor modifications [35]. Briefly, the experiment was initiated at an MOI of 0.1 in a sterile centrifuge tube containing phage (1 × 107 PFU/mL) and its host strains (1 × 108 CFU/mL) in 10 mL nutrient broth. The mixture was placed in a shaker incubator and incubated at 37 °C for 10 min for proper adsorption of phage onto the host cell surface. Afterward, the suspension was centrifuged at 12,000× g for 10 min. After removing the supernatant, the pellet of infected cells was mixed with 10 mL of pre-warmed nutrient broth and incubated in a shaker incubator (120 rpm) at 37 °C. Thereafter, 100 µL of sample was collected at 10-min intervals over a period of 60 min. To determine the phage titers, we diluted aliquots with PBS (pH 7.4), and the soft agar overlying technique was used. The time difference between adsorption (excluding the first 20 min of pre-incubation) and the initial point of the first burst was considered as the latent period, and the ratio of the end count of progeny virions to the number of infected bacterial cells found at the initial point during the latent period was considered as the burst size. ## 2.13. Host Spectrum The host range of vB_PseuP-SA22 was determined by a spot assay, as described elsewhere [45], using a panel of 23 species of bacteria with different antimicrobial sensitivity profiles (Table 1). Briefly, 5 µL of the isolated phage (109 PFU/mL) was dropped onto the lawn of the tested species. Then, after overnight incubation at 37 °C, the presence of plaques (lytic zones) on the spotted area was determined to be positive, whereas the absence of any lytic zone was considered negative for the test. Positive results were further confirmed by the DLA method. ## 2.14. Transmission Electron Microscopy (TEM) The morphology of vB_PseuP-SA22 was determined by TEM. The purified phage lysate (1 × 109 PFU/mL) was placed onto thin carbon films, and the adsorbed phages were negatively stained with $2\%$ (w/v; pH 5.0) uranyl acetate [46]. The virions were observed using a TEM 910 (Carl Zeiss, Oberkochen, Germany) at 80 kV. The morphology of the virions was captured using a Slow-Scan CCD-Camera (ProScan, 1024 × 1024, Scheuring, Germany). Certain parameters, such as the head and tail length of the virion, were measured twice using the ImageJ software. The identification guidelines indicated by the International Committee of Taxonomy of Viruses were implemented to assess the phenotypic diversity of the virion [47]. ## 2.15.1. Test Tube Biofilm Production Assay The biofilm production capacity of CRPA was determined according to the tube biofilm production method (TM) described by Di Domenico et al. [ 2016] [48]. The test organism was first inoculated in a polystyrene test tube which contained nutrient broth, then incubated at 37 °C for 24 h. Next, the planktonic cells found in all tubes were discarded and rinsed twice with PBS. Thereafter, the emptied polystyrene tubes were stained with $0.1\%$ crystal violet (CV; HiMedia Labs Pvt. Ltd., Dindori, Nashik, India) for 1 h. Then, after air-drying, if stained visible film lined the inside walls, the bottom of the tube was considered to be positive for biofilm production. A biofilm-negative culture of Escherichia coli (E. coli) was used as a negative control. ## 2.15.2. Microtiter Plate Biofilm Production and Reduction Assay The anti-biofilm activity of vB_PseuP-SA22 was assessed in optically clear, flat-bottom 96-well plates (SPL, Pocheon, Korea), in accordance with the methods stated by Fong et al. [ 49], with slight modifications. Briefly, the overnight grown culture (1 × 107 cells/mL) was inoculated into the sterile nutrient broth, following which aliquots (200 µL) were distributed to individual wells. Two different microplates, one for staining and another for enumeration of bacteria, were then incubated at 37 °C for 12 h with slight shaking (120 rpm). The supernatant of each individual well was discarded and rinsed twice with PBS (pH 7.4) to eliminate all planktonic cells, followed by treatment with 200 µL of phage suspension (1 × 106 PFU/mL) for 4, 8, and 12 h. The plates were then washed twice with PBS and air-dried. The number of bacterial cells in the biofilm was enumerated by re-suspending the biofilm in PBS after scraping the well with a sterile pipette tip, following which the suspension was diluted and plated. The total biomass of the biofilm was quantified by staining with CV ($0.1\%$, w/v) for 15 min, and the wells were then rinsed with PBS. CV was dissolved in an acetone–ethanol solution (20:80, v/v), and the intensity of CV staining (OD value) was quantified at 595 nm using a Plate Reader Infinite 200 pro (MTX Lab Systems, Austria) [49]. This assay was repeated three times. The OD values higher than the control or blank well were considered to indicate biofilm producers. ## 2.16. Scanning Electron Microscopy Scanning electron microscopy (SEM) was conducted at KFMRC in order to assess the biofilm reduction potential of vB_PseuP-SA22, following a previously described method [50,51]. Biofilms were grown on a glass coverslip initially placed into a 96-well microtiter plate, then rinsed twice with PBS and dried in a bacteriological incubator at 37 °C for 20 h. The mounted biofilms were fixed with glutaraldehyde ($2.5\%$) and dehydrated continuously with different concentrations of ethanol (30–$100\%$) for 5 min in each. Thereafter, they were sputtered with gold, and the status of the biofilms was examined using SEM (SEM; Norcada Inc., Edmonton, AB, Canada). ## 2.17. Extraction of Bacteriophage DNA The DNA of the concentrated phage (1 × 1012 PFU/mL) was extracted using a phage DNA extraction kit (QIAGEN, Germany) based on the instructions provided by the manufacturers. The concentration and purity of the extracted DNA were assessed using a spectrophotometer (Invitrogen Qubit) and examined by agarose gel electrophoresis [52]. ## 2.18. Whole-Genome Sequence and Bioinformatic Analysis Sequencing of the vB_PseuP-SA22 genomic DNA was performed at the Pittsburgh genome sequencing center (USA) using an Illumina HiSeq platform. The sequence output was assembled using ABySS v2.0.2, and the resulting contigs were assembled using the assembly algorithm Newbler version 3.0, with default parameters. RAST and GeneMark were used to predict and annotate potential open reading frames. The circular map was built using the CGView online tool, and the GC content and GC skew of the genome were assessed accordingly [53]. We used the PHIRE platform to generate promoters specific to the vB_PseuP-SA22 DNA sequence [54]. The ARNOLD online tool was used to identify the number and types of Rho-factor independent terminators [55]. GeneMarks [56] and PHAST were employed to locate the open reading frames (ORFs) [57]. The function of each coding sequence was predicted by the protein basic local alignment search tool (Blastp) of the NCBI server [58]. GtRNAdb and tRNA Scan-SE tools were used to predict putative tRNAs [59,60]. The existence of allergic proteins in the predicted coding sequences was also inspected using the food and allergy search tool. Finally, the existence of virulence factors was assessed by ResFinder and the virulence factor database (VFDB) [61,62]. ## 2.19. Phylogenetic Analysis Nucleotide sequence alignment was performed using ClustalW, and the resulting aligned sequence was used to construct the phylogenetic tree using the MAFFT v.7 software. During the construction of the tree, the neighbor-joining method and 1000 bootstrap replicates were applied [63,64]. ## 2.20. Comparative Genomic Analysis The comparative genomic sequence was conducted using the circoletto program, and a circular ideogram was built according to a method described elsewhere [65]. ## 2.21. Statistical Analysis The data were recorded as means ± SE of triplicate experiments. The statistical analysis was conducted using two-way analysis of variance (ANOVA). We used version 6 of the GraphPad Prism software to perform the analyses. The significance level was set at $p \leq 0.05.$ ## 2.22. Nucleotide Sequence Accession Number The sequence data for the P. aeruginosa phage vB_PseuP-SA22 was deposited in the GenBank under accession number OP793496. ## 3.1. Identification of the Host Bacteria and Its Antibiotic Sensitivity Profile The host bacteria were identified phenotypically by API 20 (Supplementary Figure S3) and Vitek (Supplementary Table S3) and further characterized at the molecular level, with 16s rRNA sequence analysis carried out for confirmation. The BLAST nucleotide similarity search revealed that the used CRPA showed $99\%$ sequence similarity to P. aeruginosa deposited in the national database (Accession: JQ900545.1). The sensitivity of P. aeruginosa strain B10 to common conventional antibiotics was determined in accordance with the CLSI guidelines (Supplementary Table S1). The strain was resistant against $93.8\%$ of the tested antibiotics, including aminoglycosides (amikacin, tobramycin, streptomycin, neomycin, and gentamicin), second- and third-generation cephalosporins (cefuroxime, cefotaxime, ceftriaxone, and ceftazidime), fluoroquinolones (levofloxacin and ciprofloxacin), and carbapenems (meropenem, ertapenem, and imipenem), but not to colistin (Figure 1). ## 3.2. Phage Isolation and Morphology We isolated a novel wastewater phage, vB_PseuP-SA22, using CRPA P. aeruginosa strain B10 as a host. The isolated phage produced round, halo plaques of 1.5 ± 2.5 mm in diameter on a lawn of the host cells (Figure 2B). The TEM results revealed that vB_PseuP-SA22 had an icosahedral head of 57.5 ± 4.5 nm in length and had a short, non-contractile tail (19.5 ± 1.4 nm). It was found to have a podophage morphology (Figure 2C). ## 3.3. Phage Adsorption Assay and One-Step Growth Curve The rate of adsorption of the vB_PseuP-SA22 phage on the host cell surface is presented in Figure 3A. The virions attached to the host bacterial cell surface quickly, and $60\%$ had adsorbed at 20 min post-infection. Nearly all virions were attached to the surface of the host cells at 23 min post-infection (Figure 3A). A one-step growth experiment was carried out to assess the burst size and the latent period of vB_PseuP-SA22. The growth pattern indicated that the latent period of vB_PseuP-SA22 was roughly 30 min, and the mean burst size was found to be 300 PFU/infected cell (Figure 3B). ## 3.4. Stability Test and Bacterial Challenge Test The temperature stability assessment indicated that vB_PseuP-SA22 was stable between 4–60 °C for 1 h, while the viability of the virus was reduced at temperatures over 60 °C. The approximate titer was 8 log10 PFU/mL upon incubation at 4 °C, 37 °C, 40 °C, 50 °C, or 60 °C for 1 h, and no significant differences ($p \leq 0.05$) were noticed in this temperature range. Nevertheless, we noticed that the thermal resistance rate declined to roughly 5.35 log10 PFU/ml ($p \leq 0.05$) at 70 °C, and the phage fully lost viability at 80 °C, as shown in Figure 4A. The pH stability test results indicated that the activity of vB_PseuP-SA22 was relatively stable at pH values between 5 and 11; in particular, vB_PseuP-SA22 retained approximately 8 log10 PFU/mL from pH 5–11 for 1 h. Nevertheless, it displayed a significant reduction ($p \leq 0.05$) in titer at pH 4 and 12, with titers of 3.5 and 3.15 log10 PFU/mL, respectively. Furthermore, vB_PseuP-SA22 was inactivated at pH ≤ 2 and pH ≥ 12. pH 7 and 8 were found to be the optimal pH values for vB_PseuP-SA22 (Figure 4B). The lysis kinetics of vB_PseuP-SA22 were determined at different MOIs (100, 10, 1, 0.1, 0.01, and 0.001). Based on two-way ANOVA tests, significant differences were observed for all MOIs in comparison to the control (0 MOI; $p \leq 0.05$). As shown in Figure 4D, the density of bacteria in the untreated control increased by ∼3.05 log CFU/mL at 12 h of incubation. The highest reduction in the concentration of live bacteria, by 4.67 log10 CFU/mL at 12 h, was recorded at an MOI of 0.1 compared to the original concentration ($p \leq 0.05$). Similar reductions (by approximately 2.85 log10 CFU/mL) were observed at MOIs of 1, 10, and 100. ## 3.5. Host Range The spectrum of lytic activity of vB_PseuP-SA22 was assessed by spot assay and confirmed by DLA. To do this, we used a phage stock with a titer of 1 × 108 PFU/mL against a panel of 23 bacterial strains (Table 1). The results revealed that vB_PseuP-SA22 showed lytic activity against $47.8\%$ ($$n = 11$$) of tested bacterial strains, including $\frac{8}{11}$ ($72.7\%$) of P. aeruginosa strains (Table 1). These results, therefore, indicated that vB_PseuP-SA22 has a broad host range. ## 3.6. Genomic Analysis of the vB_PseuP-SA22 Phage Genome Whole-genome sequence analysis revealed that the genome of vB_PseuP-SA22 was 45,458 bp (45.5 Kb) in length, with a GC content of $52.5\%$ (Figure 5). Using Artemis and BLAST analysis, 58 ORFs were predicted as protein-coding sequences (CDSs) transcribed in both directions. Of the 58 putative ORFs, 32 ORFs were on the negative strand, while the other 26 ORFs were on the positive strand. Among the total encoding sequences, only 26 ($44.8\%$) ORFs were found to encode the products homologous to proteins with known function, while 32 ($55.2\%$) ORFs were annotated as hypothetical proteins (Supplementary Table S2). Twenty-six ORFs were predicted as functional proteins by BLASTP and RAST analyses, distributed in the following major functional groups: head-associated proteins (ORF4, ORF6, ORF7, ORF8, ORF22), tail-associated proteins (ORF14, ORF32, ORF13), DNA replication and regulation proteins (ORF27, ORF31, ORF37, ORF38, ORF45, ORF30), protein biosynthesis (ORF40, ORF42), packaging proteins (ORF24, ORF25, ORF26, ORF3), other proteins (ORF10, ORF20, ORF21, ORF18, ORF19), and a host cell lysis protein (ORF33); see Table 2. The genome of vB_PseuP-SA22 was identified to be linear, as it was cleaved by the exonuclease Bal31, which degrades only double-stranded linear DNA from both ends simultaneously. It does not comprise genes coding for recombinases, integrases, excisionases, or repressors, which are the chief markers of lysogenic bacteriophages. Hence, we confirmed that our isolate was likely an obligate lytic phage following only a lytic cycle. Additionally, no genes were found that encode virulence factors, as assessed by testing against the VFDB. Thus, the results suggest that vB_PseuP-SA22 meets multiple requirements of a safe and virulent phage and, therefore, is a candidate for use in treating P. aeruginosa-associated infections. ## 3.7. Predicted tRNA and Rho-Independent Transcription Terminators A total of three tRNAs were predicted using the tRNAscan-SE de facto tool. They were asparagine (Asn), aspartic acid (Asp), and proline (Pro) tRNAs with GTT, GTC, and TGG anti-codons, respectively (Table 3). The predicted tRNAs were situated at different locations in the whole genomic sequence with the specified isotype score (Supplementary Figure S2). A total of eight rho-independent terminators were predicted by ARNold (RNAmotif and/or Erpin tool). The terminators were composed of loops and stems located at different regions of the genomic sequence (Supplementary Figure S1). ## 3.8. Phylogenetic Analysis Phylogenetic analyses of vB_PseuP-SA22 were conducted using BLASTn in comparison to the reference phage sequences deposited in a public database (NR, NCBI), and the tree was constructed using MEGA software. The whole-genome phylogenetic analysis indicated that the isolated phage showed high homology with Pseudomonas phages that belong to the family Podoviridae, genus Bruynoghevirus, as shown in Figure 6A. It had $96\%$, $94\%$, and $90\%$ similarity with $95\%$, $98\%$, and $74\%$ query coverage with Pseudomonas phages PSA16 (MZ089733.1), SaPL (MH973725.1), and Epa1 (MT108723.1), respectively. This suggests that vB_PseuP-SA22 also belongs to the family Podoviridae, genus Bruynoghevirus. Similarly, the constructed phylogenetic analysis relying on the terminase large sub-units indicated that the upper five phages classified under the Podoviridae family—namely, Pseudomonas phage phiPA01_302, Pseudomonas phage oldone, Pseudomonas phage U47, Pseudomonas virus Pa22, and Pseudomonas phage vB_PaeP_C2-10_Ab22—showed higher sequence similarity than the other phages classified under the same family (Figure 6B). ## 3.9. Comparative Genomic Analysis Comparative genomic analysis was performed using the circoletto program in order to determine the sequence similarity between the selected four Pseudomonas phage genomes and vB_PseuP-SA22. Each quadrant represents an individual genome, and the ribbons connecting genomes represent local alignments produced by BLAST (Figure 7). The high sequence similarity is represented by red, followed by orange and blue colors. The results revealed that all four BLAST sequences displayed high-level sequence similarity to the query phage sequence (vB_PseuP-SA22). ## 3.10. Biofilm Production In the first experiment, the test tube ring assay technique was used to assess the biofilm production capacity of P. aeruginosa strain B10. In this assay, the overnight culture of planktonic bacteria was gently removed, and the remaining biofilm (Supplementary Figure S4A,B) was stained using crystal violet and thus visualized, as shown in Supplementary Figure S4C. The three polystyrene test tubes inoculated with P. aeruginosa strain B10 produced a white clump on the inner wall of the test tube, which was stained violet. These results indicated that the organism was positive for biofilm production. Similarly, in the second experiment, biofilm production was assessed and quantified using a microtiter plate assay. The round violet rings on the inner wall and the bottom of the microtiter plate were indicative of biofilm production. ## 3.11. Biofilm Reduction Assay The anti-biofilm activity of vB_PseuP-SA22 was assessed in 96-well plates, as described in Materials and Methods (Figure 7A). The results revealed that the anti-biofilm activity of vB_PseuP-SA22 was noticeable at an MOI of 0.1. The total biomass of biofilm showed a significant reduction ($p \leq 0.001$) at 4, 8, and 12 h. The viable bacterial cell count inside the biofilm was also significantly reduced ($p \leq 0.001$) throughout the incubation period, resulting in 2.25, 1.25, 0.34, and 0 log10 CFU/mL at 0, 4, 8, and 12 h, respectively (Figure 8B). Scanning electron microscopic images also confirmed the total disruption of pre-formed biofilms at 12 h of treatment with phage vB_PseuP-SA22, as shown in Figure 8C. ## 4. Discussion At present, nosocomial pathogens such as P. aeruginosa pose a serious public health threat worldwide due to their resistance to a variety of antimicrobial agents [66]. According to a study conducted by the World Health Organization in 2016, CRPA was ranked second among 20 antimicrobial-resistant bacterial species for which new antibacterials are urgently required [9]. To date, scientists and clinicians all over the globe have worked intensively to find promising alternative antimicrobial approaches (e.g., phage therapy) and combinatorial therapies [67,68]. Phage therapy against Pseudomonas spp. has considerably improved over the past decade [69]. Nevertheless, little is known regarding CRPA-specific phages [32]. In this study, we isolated a phage with potential lytic ability against CRPA from wastewater. vB_PseuP-SA22 has an icosahedral head and short, non-contractile tail, which is typical of podophages. Similar reports have indicated that multiple phages active against MDR P. aeruginosa were isolated from wastewater in different geographical locations [31,70]. Wastewater, in general, comprises a wide variety of micro-organisms due to contamination from fecal and hospital wastes [71], and wastewater is a good source of phages against multiple antibiotic-resistant bacterial strains [72]. The whole-genome sequence of the vB_PseuP-SA22 revealed that its genome is a 45,458 bp long double-strand DNA sequence with 56 ORFs. The genome does not carry any harmful genes, such as those linked with lysogeny, antibiotic resistance, toxins, or other factors associated with the virulence of the host bacterium. This suggests that vB_PseuP-SA22 can be considered as a virulent phage, a potential therapeutic agent against CRPA. The lysis cassette of vB_PseuP-SA22 encodes for holin (encoded by ORF35). Holin is a small phage-encoding protein that incorporates into the cell membrane, forms large holes and transports endolysin across the membrane [73]. Phages likely lyse cells to release their progeny through the holin–endolysin lytic system, which is common within almost all dsDNA phages [74]. The vB_PseuP-SA22 genome encodes for three tRNAs. There is evidence suggesting that lytic phages contain a greater number of tRNAs than temperate phages [75]. Most temperate phages (e.g., E. coli phage P4, P22, or Lambda) lack tRNA genes. Phages play a major role in horizontal gene transfer within bacteria. tRNA genes, however, are crucial housekeeping genes that are expected to be least susceptible to lateral gene transfer [76]. When these genes are deleted, rates of protein synthesis and burst sizes are lowered. Yet, why some phages possess tRNAs remains a mystery [77]. The stability of phages in a wide range of thermal and pH conditions is known to be a very crucial factor for the optimal replication of the viral particle in the host bacteria [78]. According to this study, vB_PseuP-SA22 displayed stability in a pH range of 4.0 to 12. Extreme basic or acidic pH could considerably influence the infectivity of most phages, and researchers have suggested that these situations cause the denaturation of phage proteins and, subsequently, loss of their viability [79]. Previous works have verified that most tailed phages are stable in a pH range of 5.0 to 9.0 [80,81], similar to the results obtained in this study. A spot assay was employed to determine the host range of the isolated phage. However, the clear zone created in the tested bacterial strains sometimes is associated with bacteriocins, which might be present in the prepared suspension [82]. Hence, the spot assay result was further confirmed by plaque assay. In this line, vB_PseuP-SA22 was found to be lytic for a broad range of bacterial strains tested. The bacterial killing assay indicated that the highest growth rate was recorded at an MOI of 0.1. On the other hand, normal growth was observed without phage (control), and the cell concentration kept increasing steadily throughout the incubation period. Bacterial reduction assay is one of the principal factors in determining the applicability of newly isolated phages to be used in phage therapy. The lytic kinetics showed a radical decline in bacterial growth from 4 to 8 h post-infection. However, except for 0.1 MOI, a slight increase in bacterial growth was noticed at all the other MOIs. According to previous reports, this result may be due to the development of phage-resistant mutants following infection; this characteristic is regarded as a drawback of phage therapy [83]. The biofilm reduction assay indicated that administration of vB_PseuP-SA22 led to a significant reduction ($p \leq 0.001$) in the biofilm biomass. In this assay, the growth of the host bacteria found in the biofilm was also significantly reduced ($p \leq 0.001$). Guo et al. have reported that two Myoviridae phages, vB_PaeM_SCUT-S1 (S1) and vB_PaeM_SCUT-S2 (S2), inhibited the growth of the P. aeruginosa strain PAO1 at low MOI levels, and showed good performance both in eradicating pre-formed biofilms as well as in preventing biofilm formation [84]. Similarly, in a study by Vukotic et al. [ 85], the A. baumannii phage ISTD produced 2 and 3.5 log reductions in a biofilm-associated bacterial cell and planktonic cell count, respectively [85]. In another study, five P. aeruginosa phages isolated from domestic sewage showed good performance regarding the degradation of biofilms formed in the endotracheal tubes [86]. A high biofilm reduction potential has been reported by Yuan et al. when using the anti-P. aeruginosa phage vB_PaeM_LS1 at 8 h post-infection [87]. Latz et al. have reported that three phages—phiKZ-like, PB1-like, and LUZ24-like—suppressed the biofilm as well as the planktonic form of MDR P. aeruginosa [30]. Several studies have indicated that the antibiofilm activity of phages is mainly associated with the enzymes that they encode. Polysaccharide depolymerases are phage-encoded polysaccharide-degrading enzymes that are highly specific and mainly associated with the tail structure of the virions, such as tail spikes, tail fibers, tail tubular, or base plate proteins [88]. They have a specific binding site with respect to capsular polysaccharides, lipopolysaccharides, or exopolysaccharides of the host bacteria. According to a recent report by Knecht et al., the biofilms of P. aeruginosa can be destabilized by alginate-specific tail spike proteins [15]. ## 5. Conclusions In conclusion, we successfully isolated a lytic bacteriophage targeting CRPA from samples collected from a wastewater treatment plant in Jeddah, Saudi Arabia. vB_PseuP-SA22 presented a wide host range and remained active in a wide range of temperatures and pH conditions. Its high burst size, a lack of toxic, virulent, and/or antibiotic-resistant genes in its genome, together with its antibiofilm activity against the tested host strain, are all crucial features of vB_PseuP-SA22, demonstrating its promise as an alternative therapeutic agent in treating infections associated with multidrug-resistant P. aeruginosa infections. Therefore, further clinical studies and other comprehensive in vivo examinations are necessary to investigate the therapeutic properties of the isolated phage against MDR P. aeruginosa. In addition, the combined effect of vB_PseuP-SA22 with other lytic bacteriophages may also be assessed in order to optimize its use. ## References 1. 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--- title: Effects of Different Combinations of Sodium Butyrate, Medium-Chain Fatty Acids and Omega-3 Polyunsaturated Fatty Acids on the Reproductive Performance of Sows and Biochemical Parameters, Oxidative Status and Intestinal Health of Their Offspring authors: - Caiyun You - Qingqing Xu - Jinchao Chen - Yetong Xu - Jiaman Pang - Xie Peng - Zhiru Tang - Weizhong Sun - Zhihong Sun journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044250 doi: 10.3390/ani13061093 license: CC BY 4.0 --- # Effects of Different Combinations of Sodium Butyrate, Medium-Chain Fatty Acids and Omega-3 Polyunsaturated Fatty Acids on the Reproductive Performance of Sows and Biochemical Parameters, Oxidative Status and Intestinal Health of Their Offspring ## Abstract ### Simple Summary Dietary supplementation with fatty acids benefits the high productivity of sows and plays an essential role in piglet growth. Considering that the mechanisms of the fatty acid types on animal physiology differ, combined supplementation may have additive effects. Therefore, we investigated the effects of different combinations of fatty acids with different chain lengths on the reproductive performances of sows and on the antioxidant capacity, immune function, and intestinal health of their offspring. Taken together, the dietary supplementation of sows with different combinations of SB, MCFAs, and omega-3 PUFAs to the sows during late gestation and lactation can efficiently improve the growth performance, immune function, antioxidant capability, and intestinal microbiota and decrease the incidence of diarrhea in the suckling piglets. Additionally, dietary SMP supplementation has better effects on piglet intestinal health and is likely through gut microorganism alterations. ### Abstract The aim of the study was to investigate the comparative effects of different combinations of sodium butyrate (SB), medium-chain fatty acids (MCFAs), and omega-3 polyunsaturated fatty acids (n-3 PUFAs) on the reproductive performances of sows, as well as on the biochemical parameters, oxidative statuses, and intestinal health of the sucking piglets. A total of 30 sows were randomly allocated to five treatments: [1] control diet (CON); [2] CON with 1 g/kg of coated SB and 7.75 g/kg of coated MCFAs (SM); [3] CON with 1 g/kg of coated SB and 68.2 g/kg of coated n-3 PUFAs (SP); [4] CON with 7.75 g/kg of coated MCFAs and 68.2 g/kg of coated n-3 PUFAs (MP); [5] CON with 1 g/kg of coated SB, 7.75 g/kg of coated MCFAs and 68.2 g/kg of coated n-3 PUFA (SMP). The results showed that sows fed the SP, MP, and SMP diets had shorter weaning-to-estrus intervals than those fed the CON diet ($p \leq 0.01$). The piglets in the SM, SP, and MP groups showed higher increases in the plasma catalase and glutathione peroxidase activities than those of the CON group ($p \leq 0.01$). The diarrhea incidence of piglets in the SM, SP and SMP groups was lower than that of piglets in the CON group ($p \leq 0.01$). Additionally, the addition of SM, SP, MP, and SMP to the sow diets increased the contents of immunoglobulin A, immunoglobulin G, fat, and proteins in the colostrum ($p \leq 0.01$), as well as the plasma total superoxide dismutase activities ($p \leq 0.01$) in the suckling piglets, whereas it decreased the mRNA expressions of tumor necrosis factor-α, interleukin-1β, and toll-like receptor 4 in the jejunum mucosa of the piglets. The relative abundances of Prevotella, Coprococcus, and Blautia in the colonic digesta of the piglets were increased in the SM group ($p \leq 0.05$), and the relative abundances of Faecalibacterium increased in the SMP group ($p \leq 0.05$), compared with the CON group. The relative abundances of Collinsella, Blautia, and Bulleidia in the MP group were higher than those in the CON group ($p \leq 0.05$). Collectively, dietary combinations of fatty acids with different chain lengths have positive effects on the growth performances and intestinal health of suckling piglets. ## 1. Introduction The reproductive performances of high-yielding sows and the growth performances of piglets are the two most important aspects that influence the economic efficiency of the modern pig breeding industry. With the development of breeding techniques, the reproductive performances of sows have been improved. However, highly prolific sows often suffer from a range of issues, including insufficient nutrient intake, excessive weight loss, longer weaning-to-estrus intervals (WEI), shortened service lives, and metabolic disorders, which result in the retarded growth of their piglets [1,2,3,4]. Additionally, in late pregnancy, the fetal growth rate dramatically accelerates [5]. Diets that contain supplemental fatty acids have been particularly effective at improving the body conditions of sows and the birth weights and growth of suckling piglets [6,7,8]. Therefore, meeting the nutrient requirements of prolific sows is a vital consideration for the development of pig farming. Fatty acids have biological functions, such as the regulation of metabolic disorders, the intestinal barrier, and the immune function of animals [7,9,10]. Fatty acids are categorized according to the length of the carbon chain and the degree of saturation. Short-chain fatty acids (SCFAs), and especially butyrate (SB), which is a major source of energy for colonic epithelial cells [9], can improve the immune function of piglets through the nuclear factor-kappa B (NF-κB) signal pathway [11,12]. In addition, appropriate doses of butyrate can alleviate diarrhea symptoms and reduce the intestinal permeability to maintain intestinal health [13]. The majority of MCFAs are carried straight to the liver via the portal vein, granting a rapid energy supply, which is vital for piglets [7]. In addition, MCFAs decrease intestinal colonization by opportunistic pathogens and modulate the colonic microbiota of piglets [14,15]. n-3 PUFAs are essential for embryonic and fetal development [16]. In addition, n-3 PUFAs also play an important role in shortening the weaning-to-estrus interval (WEI) and enhancing the immune function in sows [16,17,18]. Previous studies have shown that dietary single fatty acid supplementation could shorten the WEIs of sows and improve the intestinal health and growth performances of suckling piglets [19,20]. However, studies on the effects of dietary combinations of SB, MCFAs, and n-3 PUFA on prolific sows during late gestation and lactation are still lacking. Therefore, the purpose of this study was to investigate the effects of different combinations of SB, MCFAs, and n-3 PUFAs on the reproductive performances of sows and on the biochemical parameters, oxidative status, and intestinal health of their offspring during late gestation and lactation. We speculated that the combination of fatty acids would have additive effects on the reproductive performances of the sows and the growth performances of their offspring. ## 2. Materials and Methods All animal operations were carried out in compliance with protocols approved by the Animal Ethics Committee of Southwestern University (Chongqing, China). The behavior and health of the experimental animals were continuously monitored during the trial period, and no negative impacts were observed. More precisely, there were no clinical problems that could have necessitated pharmaceutical treatment for pathologies after the investigation began, and all the animals were deemed suitable for the study. The experimental animals were disposed of safely following the Experimental Animal Handling Procedure of Southwest University (Ethics Approval Code: IACUC-20210120-03). ## 2.1. Animals, Materials, and Feeding Management A total of 30 third-parity sows (Landrace × Large White hybrid; 200 ± 15 kg) were used in this study. From mating to d 109 of gestation, the sows were kept in individual stainless-steel cages (0.60 × 2.15 m) in the gestation house, and on approximately d 110 of gestation, they were transferred to the farrowing stalls (1.20 × 2.15 m) in a thoroughly sterilized farrowing house with iron fencing and plastic flooring. The SB, MCFAs, and omega-3 PUFAs in this study were supplied by Xingao Agribusiness Development Co., Ltd. (Xiamen, Fujian, China), with purities of $98\%$, $70\%$, and $20\%$, respectively. The primary active constituents of the n-3 PUFAs were docosahexaenoic acid (DHA), α-linolenic acid (ALA), and eicosapentaenoic acid (EPA). Additionally, the sows were fed twice a day at 08:00 and 16:00, with 2.5–3.0 kg of feed per day, which was restricted in late gestation based on the body condition, while 2 kg was fed on d 1–2 of lactation, with an increase of 0.5 kg per day from d 3 to d 7 of lactation, and an increase of 0.8 kg per day from d 8 to d 14 of lactation, with no further increase thereafter. All sows were allowed to consume water at any time during the study. Heaters and exhaust fans kept the room at a comfortable temperature (from 20 to 25 °C). ## 2.2. Diets and Experimental Design The sows were randomly allocated to five treatments (six replicate pens per treatment and one sow per replicate) in a completely randomized experimental design. The sows were fed a basal diet (control, CON), a basal diet supplemented with 1 g/kg of coated SB and 7.75 g/kg of coated MCFAs (SM), a basal diet supplemented with 1 g/kg of coated SB and 68.2 g/kg of coated n-3 PUFAs (SP), a basal diet supplemented with 7.75 g/kg of coated MCFAs and 68.2 g/kg of coated n-3 PUFAs (MP), and a basal diet supplemented with 1 g/kg of coated SB, 7.75 g/kg of coated MCFAs, and 68.2 g/kg of coated n-3 PUFAs (SMP). The dosages were chosen based on the company’s recommended dosages. The piglets were housed in farrowing stalls, with one litter per pen. After the piglets were born, they were manually attached to the nipples to guarantee that they received breast milk for growth and development. This study shared the control group with Chen et al. [ 20], and the sows in this trial and Chen’s sows were kept in the same barn. The trial started on d 85 of gestation and ended with the weaning of the piglets (d 21 of lactation). From d 85 to d 110 of gestation, the test sows were fed given the gestation diet, followed by the lactation diet from d 110 of gestation and throughout weaning. The nutritional content of the baseline diet met or surpassed the nutritional recommendations of the National Research Council [2012] [21]. The dietary ingredients and nutritional levels for the sows throughout gestation and lactation are shown in Table 1. The gestation diet (approximately 100 g) and lactation diet (approximately 100 g) were collected. Then, the feed samples were analyzed for the crude protein (CP), crude ash (Ash), dry matter (DM), ether extract (EE), calcium (Ca), crude fiber (CF), available phosphorus (AP), and total phosphorus (Total P), according to the procedures followed by the standard of the AOAC [2000] [22]. ## 2.3.1. Reproductive Performances of Sows During the animal experiment, the feed wastage was recorded every day after the meal to calculate the average daily feed intake (ADFI). The individual neonatal weight of the born alive was weighed within 12 h of delivery. The stillborn and mummified fetuses were not weighed, and the mummified fetuses were counted as stillbirths (piglets that died before birth). We measured the numbers of born alive, stillborn, mummified fetuses, and total born, the litter weights, birth weights, and the weaning weights of the piglets, and the WEIs of the sows. The total litter size included mummified fetuses, born alive, and stillborn. The born-alive rate was calculated as the number of born alive/total born × $100\%$. ## 2.3.2. Growth Performances of Piglets and Diarrhea Incidence Piglets were cross-fostered after altering the litter sizes of the sows within the same treatment within 24 h of farrowing. Piglets were weaned on d 21 of lactation, the number of weaned piglets was counted, and the piglets were weighed to determine the ADGs and weaning survival rates. The diarrhea severity was determined by daily observation of the piglet feces, as previously described [23,24]. The formula for calculating the diarrhea incidence was as follows: the diarrhea rate (%) = Σ[(diarrhea days in piglets × number of diarrhea piglets)]/(total number of piglets × 21) × $100\%$. ## 2.3.3. Sample Collection The colostrum (about 40 mL) was manually collected after the alcohol sterilization of the sow teats within 2 h of the first piglet’s birth. For each repetition, piglets that met the average weight were randomly selected for blood and slaughter sampling. On d 22 of lactation, one piglet per pen was randomly selected for the collection of blood samples from the anterior vein. Then, the blood samples were centrifuged (4 °C, 3000× g, 15 min) and the plasma was stored at −80 °C for subsequent analysis. Next, the piglets were humanely killed after anesthesia by intravenous injection with sodium pentobarbital (50 mg/kg BW). Tissue samples from the liver and middle jejunum were taken, flushed with $0.9\%$ saline, and deposited in a $4\%$ formaldehyde solution for morphological examination. The colonic digesta (approximately 10 g) was collected in sterile tubes for the microbiota analysis. After that, jejunal mucosa samples were carefully scraped off using a sterile glass slide, flash-frozen in liquid nitrogen and were maintained at −80 °C for subsequent analysis. ## 2.4.1. Colostrum Composition Analysis One colostrum sample (approximately 20 mL) diluted 3 times with purified water was determined in triplicate for milk fat, milk protein, lactose and solids-not-fat (SNF) using a FOSS Multifunctional Dairy Analyzer (MilkoScan TM FT120, Foss Electric A/S, Hillerød, Denmark). The other colostrum sample (approximately 20 mL) was centrifuged (4 °C, 3000× g, 20 min), and the supernatant was aspirated and kept at −80 °C. The colostrum supernatant was thawed at room temperature and utilized to determine the concentrations of immunoglobulin A (IgA), immunoglobulin G (IgG), and immunoglobulin M (IgM) using swine reagent ELISA kits provided by the Nanjing Jiancheng Bioengineering Institute (Nanjing, Jiangsu, China). The intra- and inter-assay CVs for these ELISA kits were both less than $9.0\%$. ## 2.4.2. Blood Biochemical Parameters The total antioxidant capacity (T-AOC); (Code: A015-2-1), glutathione peroxidase (GSH-Px); (Code: A005-1-1), malondialdehyde (MDA); (Code: A003-1-2), total superoxide dismutase (T-SOD); (Code: A001-1-1) and catalase (CAT); (Code: A007-1-1) in the plasma of the pigs were analyzed by using commercial assay kits (Nanjing Jiancheng Bioengineering Institute, Nanjing, Jiangsu, China). The concentrations of total protein (TP); (Code: A045-1-1) and albumin (ALB); (Code: A028-2-1) in the plasma of the pigs were determined using a por-cine-specific commercial kit with microplate test methods and an enzyme-labeled instrument (Thermo Electron Corporation; Rochester, NY, USA). The plasma concentrations of high-density lipoprotein cholesterol (HDL-C); (Code: A112-1-1), glucose (GLU); (Code: F006-1-1), total cholesterol (TC); (Code: A111-1-1), urinary nitrogen, and triglycerides (TG); (Code: A110-1-1) were determined using colorimetric method diagnostic kits. In accordance with the manufacturer guidelines, all protocols were strictly carried out, ensuring the highest level of safety and accuracy. ## 2.4.3. Intestinal Morphology Jejunum tissues were collected from weaning piglets and fixed in $4\%$ formalin for the analysis of the intestinal morphology. In short, the villus height (VH) and crypt depth (CD) of the jejunum were measured using an Axio Scope A1 microscope (Zeiss, Oberkochen, Germany) with 40× combined magnification. The VH was calculated by measuring the distance between the top of the villus and the villus-crypt junction, and the CD was calculated by measuring the distance between the villus-crypt junction and the bottom of the crypt. The averages of the measurements (at least 10 normative measurements) were used for the statistical analysis, and all intestinal mucosal morphometric analyses were executed by the same operator. ## 2.4.4. Quantitative Real-Time PCR The relative expressions of the genes related to the factors involved in the regulation of inflammation in the jejunal mucosa of the piglets were determined. Total RNA was isolated from frozen jejunal mucosa samples by using SteadyPure Uni-versal RNA Extraction Kits II (Code: AG21022; Accurate Biotechnology (Hunan) Co., Ltd., Changsha, China). The specific RNA extraction procedure was performed using the manufacturer’s recommendations. The concentration of total RNA was measured with a NanoDrop-ND2000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA), and the reverse transcription was performed with the qualified RNA samples using AMV First Strand cDNA Synthesis Kits provided by Sangon (Shanghai, China), according to the manufacturer’s instructions. Real-time PCR analysis was performed to quantify the claudin-1 (CLDN 1), interleukin-6 (IL-6), tumor necrosis factor-α (TNF-α), occludin (OCLN), interleukin-1β (IL-1β), zonula occludens-1 (ZO-1), interleukin-10 (IL-10), toll-like receptor 4 (TLR-4), NF-κB, myeloid differentiation factor 88 (MγD88), and glyceraldehyde-3-phosphate (GAPDH) mRNA levels in the jejunal mucosa. The primer sequences for all the target genes and predicted product sizes are shown in Table 2. The real-time PCR analysis was conducted using the SYBR Green approach combined with an ABI 7900 Sequence Detection System. The following thermal cycling parameters were used: initial denaturation at 94 °C for 30 s, followed by 40 cycles at 94 °C for 5 s, annealing temperature for 20 s, and extension at 72 °C for 20 s. Moreover, the melting curve analysis was used to ensure that the PCR products remained specific and pure. A standard curve was generated using LightCycler software and the amplification of serially diluted cDNA, and the quantification of the target gene expression was calculated using the 2−ΔΔCT method based on the standard curve with the GAPDH gene as the reference gene [25]. ## 2.4.5. 16S rRNA Gene Sequencing and Microbiota Analysis Total DNA from the digesta of the colon was extracted using Power Fecal DNA Isolation Kits (Mobio, Carlsbad, CA, USA) following the manufacturer’s instructions. Briefly, the 16S rDNA gene was presented in the genome of all bacteria and was highly conserved. Microbial profiling was performed on an Illumina HiSeq2500 platform (Novogene, Beijing, China) by the PCR amplification of a segment of a highly variable region sequence (V3 region) following 600 amplification cycles. Then, the raw data sequences for the 16S rRNA gene were collected and filtered with the software tools FLASH and QIIME. UPARSE was used to assess the valid sequences and establish the practical classification units (OTUs). Moreover, singletons and OTUs below $0.005\%$ were eliminated. Subsequently, the species-level classification was determined by the taxonomic alignment of high-quality sequences with the National Center for Biotechnology Information (NCBI) nucleotide database (ver. 2.20) at a $90\%$ confidence threshold. The alpha diversity index (Shannon, ACE, Chao1, and Simpson) and β-diversity (Bray Curtis) analyses were calculated with the QIIME software tool. ## 2.5. Statistical Analysis According to the post hoc power analysis for the ADFI of the sows, ADGs of the piglets, and IgA in the colostrum and plasma in this study, the calculated statistical power was > 0.90; thus, 6 pigs per treatment were enough to provide sufficient statistical power (α < 0.05; β = 0.80). All data analyses were performed using ANOVA analysis, with the dietary treatments used as the fixed factor. Diarrhea rate data assessments were translated using the arcsine square root transformation for subsequent statistics. Data were subjected to Duncan’s test method using SPSS 19.0 software (SPSS Inc., Chicago, IL, USA), and they are presented as means and standard errors of means (SEMs) unless otherwise noted. The histograms were created using GraphPad Prism 8. ( GraphPad Prism Inc., San Diego, CA, USA). Statistical significance was identified when $p \leq 0.05$, and trends were considered when 0.05 < p ≤ 0.10. ## 3.1. Reproductive Performances of Sows As shown in Table 3, there were no significant changes in the number of born alive, stillborn and total born among the dietary treatments ($p \leq 0.05$). Compared with the CON group, the ADFI of the sows was significantly increased in the SM and SMP groups ($p \leq 0.01$). Moreover, the sows fed the SP, MP, and SMP diets showed significantly shorter WEIs compared with those fed the CON diet ($p \leq 0.01$). ## 3.2. Growth Performances of Piglets As shown in Table 3, there were no differences in the survival rates of the suckling piglets among the dietary treatments ($p \leq 0.05$). The final BWs of the piglets in the SMP group were significantly higher than those of the piglets in the CON, SM, and MP groups ($p \leq 0.01$). The ADGs of the piglets in the SMP group were higher than those of the suckling piglets in the CON and MP groups ($p \leq 0.01$). Of note, the suckling piglets in the SMP group showed higher final BWs and ADGs than the other groups. Moreover, the diarrhea incidence of the suckling piglets in the CON group was higher than those of the piglets in the SM, SP, and SMP groups ($p \leq 0.01$). ## 3.3. Colostrum Composition of Sows As shown in Table 4, compared with the sows fed the control diet, the dietary addition of SM, SP, MP, and SMP increased the concentrations of fat and protein in the colostrum ($p \leq 0.01$). The SNF concentrations in the colostrum of the sows in the SM and MP groups were higher than those of the sows in the CON, SP, and SMP groups ($p \leq 0.01$). In addition, the concentrations of IgA, IgG, and IgM in the colostrum of the SM, SP, and SMP groups were higher than those of the sows in the CON group ($p \leq 0.01$). ## 3.4. Plasma Biochemical Index of Suckling Piglets As shown in Table 5, the plasma TP, FFA, and HDL contents of the suckling piglets were significantly increased in the SM, SP, MP, and SMP groups compared with those of the piglets in the CON group ($p \leq 0.01$). In addition, the TG and TC contents were decreased in the plasma of the suckling piglets in the SM, MP, and SMP groups ($p \leq 0.01$) compared with those in the plasma of the piglets in the CON group. For the immunoglobulin levels, the piglets in the SM group showed higher IgA concentrations in their plasma than the other groups ($p \leq 0.001$). Compared with the CON group, the SM, SP, MP, and SMP groups showed significantly increased IgG concentrations in the plasma of the piglets ($p \leq 0.01$). ## 3.5. Plasma Antioxidant Capacity of Suckling Piglets As shown in Table 6, the piglets in the SM, SP, MP, and SMP groups showed significantly increased plasma T-SOD and T-AOC activities in comparison with those in the CON group ($p \leq 0.01$). The piglets in the SM, SP, and MP groups had higher plasma CAT and GSH-Px activities than those in the CON group ($p \leq 0.01$), and the piglets in the MP group had the highest plasma GSH-Px activity among the five groups. However, the content of plasma MDA was higher in the MP group than in the CON group ($p \leq 0.01$), with no significant difference in the SM, SP, and SMP groups ($p \leq 0.05$). ## 3.6. Intestinal Morphology of Sucking Piglets As shown in Figure 1B, there was no significant difference in the VH of the jejunum among the five groups ($p \leq 0.05$). The CD of the jejunum in the SM, SP, MP, and SMP groups was significantly lower than that of the jejunum in the CON group ($p \leq 0.01$, Figure 1C). The VH/CD ratio of the jejunum in the SM, SP, MP, and SMP groups was significantly increased compared with that of the CON group ($p \leq 0.01$, Figure 1D). Moreover, the piglets in the SMP group showed a higher VH/CD ratio in the jejunum mucosa than those in the other groups ($p \leq 0.01$). ## 3.7. mRNA Expressions of Intestinal Tight Junction Protein and Inflammatory Cytokines of Suckling Piglets Compared with the CON group, the piglets in the SM, SP, MP, and SMP groups showed significantly upregulated mRNA expressions of CLDN-1 and ZO-1 in the jejunal mucosa ($p \leq 0.01$, Figure 2A), and the piglets in the SMP group had significantly upregulated mRNA expressions of OCLN ($p \leq 0.01$). There was no significant difference in the mRNA expressions of IL-6 among the five groups ($p \leq 0.05$, Figure 2B). However, the mRNA expressions of TNF-α, IL-1β, and TLR4 of the jejunum were significantly downregulated in the SM, SP, MP, and SMP groups compared with those in the CON group ($p \leq 0.01$, Figure 2B,C), and the mRNA expressions of NF-κB in the jejunal mucosa were downregulated in the SM, SP, and SMP groups compared with those in the CON group ($p \leq 0.01$, Figure 2C). ## 3.8. Intestinal Microbial Flora in Colonic Digesta The microbial flora in the colonic digesta was analyzed. As shown in Figure 3, a total of 222 OTUs were shared among the five treatment groups (Figure 3A). The piglets in the CON, SM, SP, MP, and SMP groups had 167, 317, 189, 164, and 205 unique OTUs, respectively (Figure 3A). However, there was no significant difference in the α-diversity (Shannon, Chao1, ACE, and Simpson indexes) of the colonic digesta in the suckling piglets among the five groups ($p \leq 0.05$, Figure 3B). The SM, SP, and MP groups resulted in significant changes in the beta diversity of the colonic microbiota, as shown by the NMDS based on the UniFrac distances (Figure 3C). Dietary supplementation with SMP was associated with increased relative abundances of Faecalibacterium at the genus level (Figure 3F, LDA score >2). Additionally, the piglets in the MP group were associated with increased relative abundances of Collinsella, while the piglets in the SM group were associated with increased relative abundances of Catenbacterium, Coprococcus, and Bulleidia at the genus level (Figure 3F, LDA score >4). At the phylum level, the relative abundances of Bacteroidetes were increased in the SMP group ($p \leq 0.05$, Figure 4A), while the relative abundances of Firmicute in the SM, MP, and SMP groups were significantly lower than those of the CON group ($p \leq 0.05$, Figure 3B). At the genus level, the relative abundances of Prevotella, Coprococcus, and Blautia were increased in the SM group ($p \leq 0.05$, Figure 4C,F,H), and the relative abundances of Blautia and Bulleidia in the MP group were higher than those in the CON group ($p \leq 0.05$, Figure 4F,H). Compared with the piglets in the CON group, the piglets in the SMP group had increased relative abundances of Faecalibacterium ($p \leq 0.05$, Figure 4E). ## 4. Discussion The ADFI of the sows and the ADGs of the piglets were considered the principal limitation factors for the growth performances of the neonatal pigs. In the present study, the dietary addition of SMP led to the highest ADG (236 g in the SMP group vs. 186 g in the CON group) and final BWs of the piglets (6.48 kg in the SMP group vs. 5.36 kg in the CON group), which agree with Jin et al. [ 26], who reported that the addition of fish oil could produce positive effects in the ADGs of piglets. Gebhardt et al. [ 27] found similar results: MCFAs improved the growth performance of nursery piglets by increasing the ADG, ADFI, and feed conversion ratio in a linear dose-dependent manner. Conversely, in some studies, researchers found no effect on the ADGs of piglets using $0.2\%$ or even lower fatty acid products [27,28], which indicates that the ADGs were affected by the dietary fatty acid contents. It has been documented that SB has a distinct cheese flavor, which regulates appetite and food intake [10,29]. Our results demonstrated that the dietary addition of SM and SMP significantly increased the ADFI of the sows, whereas the dietary addition of SP and MP obtained the reverse results. Hanczakowska et al. [ 30] found similar results: a mixture of SCFAs and MCFAs produced better results on the ADFI. Additionally, Smit et al. [ 31] indicate that n-3 PUFAs have a higher energy density and boost overall energy intake, which allows sows to eat less. Thus, a mixture of SB and MCFAs might have potential additive effects to mitigate the negative effect of n-3 PUFAs on the ADFI. In addition, diarrhea is the critical factor that causes the retardation of growth performances and increased mortality of piglets [32]. Feng et al. [ 13] and Lerner et al. [ 33] indicated that SB and MCFAs have the potential to replace antibiotics to control pathogenic bacteria, while n-3 PUFAs have a positive immunomodulatory effect in the gut [34,35]. In this study, the diarrhea incidence of the suckling piglets in the SM, SP, and SMP groups was lower than that in the CON group. Similar results were reported by Chen et al. [ 20]. The reason might be that SB can provide conditions for the growth of beneficial intestinal bacteria to reduce diarrhea incidence [19]. Intriguingly, Li et al. [ 36] indicate that organic acid combined with MCFAs showed a better reduction in the diarrhea incidence and growth-promoting effects that were comparable to those of antibiotics. It is noteworthy that supplementing the diets of the sows with SMP had the obvious and unexpected effect of decreasing the diarrhea incidence of the nursing piglets, which indicates that a blend of SB, MCFAS, and n-3 PUFAs may have a synergistic effect, although the mechanism still needs further study. The composition and intake of the colostrum are crucial factors that affect the early weight gain, immune function, and survival of neonatal pigs with limited energy reserves [37]. Previous studies have shown a positive association between the content of fat in the milk and the piglet BW, and maternal fat supplementation could improve the piglet weaning weight [38,39]. Consistently, dietary supplementation with fish oil has increased the concentrations of fat in the colostrum of sows [40]. In this study, compared with the CON diet, sows fed the addition of SM, SP, MP, and SMP diets showed increased concentrations of fat and protein in the colostrum, which is in agreement with previous studies. In addition, the immunoglobulins in the colostrum are the only source of passive immunity for neonatal piglets. The colostral IgA and IgG concentrations are major factors that influence the passive immune protection. Jin et al. [ 26] indicated that dietary fish oil supplementation increased the IgG and IgM concentrations in the colostrum of suckling pigs, which improved their immune function. Similarly, a diet supplemented with SB increased the concentration of IgA in the colostrum [12]. Our results indicated that the concentrations of IgA and IgG in the colostrum of the SM, SP, and SMP groups were higher than those of the sows in the CON group. Furthermore, similar results were seen in the piglet plasma, which indicated that fatty acids played a positive role in regulating the immune status and providing health benefits. Additionally, the addition of SMP to the sows’ diets increased the IgA and IgM concentrations in the colostrum compared with those of the sows fed the MP diets, with no significant changes observed for the SM, SP, or SMP additions. SB and omega-3 PUFAs exert multiple beneficial effects, including immunomodulatory effects. He et al. [ 12] also observed that the IgA concentrations in the colostrum increased in SB-treated gilts. A possible explanation is that the blends of SB, MCFAs, and omega-3 PUFAs fed to the sows had additive effects. Collectively, dietary supplementation with fatty acids could enhance the growth performances of piglets by improving the colostrum composition. The plasma biochemical parameters can reflect the nutritional status of the organism, and they can be influenced by changes in internal and external factors. The contents of TP and BUN are used as an index for protein utilization and metabolism. In this study, the piglets in the fatty acid-supplemented groups showed increased contents of plasma TP, which was partially due to the increment in the plasma globulin content. Meanwhile, the addition of fatty acids with different chain lengths to the sow diets increased the contents of BUN in the piglets, which indicated increased nitrogen metabolism. Moreover, previous studies on rats and pigs have shown the beneficial effects of fatty acids on lipid metabolism [41,42]. It is well documented that the TC and TG contents reflect the synthesis and metabolism of lipids in the organism and are associated with diseases linked to dyslipidemia [43]. Allyson et al. [ 44] reported that HDL cholesterol has a strong transport function that delivers cholesterol from peripheral cells to the liver cells. Similarly, Yu et al. [ 41] showed that the addition of SCFAs to the diet promotes lipid catabolism. In this study, the piglets in the SM, MP, and SMP groups showed increased HDL-cholesterol levels and lower TG and TC contents, which suggest that fatty acids might reduce cholesterol deposition in the blood. GSH-Px, SOD, and CAT are considered to be important endogenous antioxidant enzymes that scavenge endogenous free radicals produced by the body, maintain the body’s oxidative balance, and play an important role in the oxidative and antioxidative status [45]. In this study, the piglets in the SM, SP, and MP groups exhibited increases in the activities of T-AOC CAT, GSH-Px, and T-SOD in the plasma. Famurewa et al. [ 46] also found that the dietary addition of coconut oil relieved oxidative stress in a dose-dependent manner by significantly increasing the antioxidant enzyme activities (SOD, CAT, and GSH-Px). Similarly, Nguyen et al. [ 47] reported the benefits of a diet rich in n-3 PUFAs in stimulating antioxidant enzyme activities to reduce excess ROS production. MDA is considered to be the main product of lipid oxidation, and it is a commonly used indicator of lipid peroxidation [47]. Li et al. [ 48] showed that the addition of MCFAs to the diet linearly reduced the plasma MDA concentration. Unexpectedly, in this study, the dietary addition of a combination of mixed MCFAs and n-3 PUFAs increased the plasma MDA levels. The discrepancies between studies might be due to the different doses of n-3 PUFAs and the lengths of time that the n-3 PUFAs were supplied. Diets rich in n-3 PUFAs may undergo peroxidation, which leads to free radical-dependent cellular damage, as evidenced by elevated plasma MDA levels [49,50]. These results indicate that the fatty acid alleviation in the oxidative stress statuses of piglets might be related to the improvement in cholesterol metabolism. Tight junction proteins play an important role in intestinal barrier function. It has been reported that OCLN and ZO-1, which are tight junction proteins, are vital in the regulation of intestinal permeability [51]. Feng et al. [ 13] indicated that SB significantly increased the intestinal ZO-1 and OCLN expressions in vivo and in vitro. A prior study has demonstrated that different fatty acid treatments were beneficial to the intestinal epithelial barrier integrity and intestinal barrier function [20]. In the current study, we observed that the piglets in the SM, SP, MP, and SMP groups had significantly upregulated mRNA expressions of CLDN-1 and ZO-1 in the jejunal mucosa, which contributed to the alleviation of diarrhea in the suckling piglets. The piglets in the SMP group showed higher mRNA expressions of CLDN 1, ZO-1, and OCLN than those in the other groups, which implies that these three fatty acids may have a synergistic effect in strengthening the intestinal barrier function. In addition, the intestinal mucosa morphology is an evaluation of the nutrient digestion and absorption ability, which has a direct impact on the nutrient usage efficiency [23]. It is well-known that SB can reduce some of the negative effects of the intestinal mucosal morphology by providing the preferred energy [10]. Keyser et al. [ 52] indicated that MCFA supplementation restored the villus height in postweaning piglets with LPS challenges. In addition, n-3 PUFAs may repair the gut damage induced by oxidative stress and enhance the intestinal morphology in piglets [34]. These combined findings indicate that fatty acid supplementation could slightly improve intestinal development by enhancing the barrier integrity and intestinal morphology. Fatty acids play a major role in the inflammatory response of intestinal mucosa [25,53]. Many studies indicate that n-3 PUFAs can alleviate the inflammatory status in animals [17,35]. Carlson et al. indicated that medium-chain triglycerides reduced the mRNA expressions of IL-6 and TNF-α in mice and alleviated the inflammatory response [54]. Similarly, Kuang et al. [ 55] demonstrated that the addition of mixing MCFAs with SCFAs to the base diet significantly reduced the mRNA expressions of TNF-α and IL-1β. In the present study, the mRNA expressions of the inflammatory factors (TLR4, IL-1β, MγD88, TNF-α, and NF-κB) in the jejunal mucosa were reduced, while the mRNA expression of the anti-inflammatory factor (IL-10) was increased. Researchers have hypothesized that fatty acids of different chain lengths likely attenuate the inflammatory response through the NF-κB and TLR4 signaling pathways [56,57]. Fatty acids inhibit inflammatory factors by regulating the MγD88-dependent route. In addition, the receptor for TLR4 upregulated the MγD88 expression, which can lead to the production and release of inflammatory factors, inducing an immune response in the intestinal mucosa [58,59,60]. Of note, the mRNA expressions of IL-1β, TLR4, and TNF-α in the SM, SP, MP, and SMP groups were lower than those of the CON group, while the mRNA expressions of IL-10 had an opposite result in the SM, SP, and SMP groups. Butyric acid may be related to an inhibitor of a histone deacetylase and result in better anti-inflammatory effects [9]. Intestinal microbes play important roles in the host health and performance, and they can profoundly impact the host nutrient metabolism, intestinal development, and immunological functions [61]. Researchers have extensively demonstrated that fatty acids can modulate the abundance and composition of intestinal microbes. Firmicutes, Bacteroidota, Proteobacteria, and Actinobacteriota predominated in the colonic contents of the suckling piglets, which is consistent with previous studies [19,62]. Researchers have reported that Bacteroidetes was able to significantly reduce the diarrhea incidence [63]. In the present study, supplementing the sow diets with SMP significantly increased the relative abundance of Bacteroidetes in the colonic digesta of the suckling piglets, which may partly explain the reduction in the diarrhea incidence. At the genus level, the high abundance of bacteria provides an opportunity to understand how microbiota metabolites affect the host physiology. Prevotella belongs to Bacteroidetes, while Faecalibacterium, Blautia, Bulleidia, and Coprococcus belong to Firmicutes, in which Prevotella and Coprococcus are mostly involved in complex polysaccharide metabolism [64]. Prevotella, Faecalibacterium, Blautia, and Coprococcus can produce high levels of SCFAs, mainly including propionate, butyrate, and acetic acid [64,65,66]. Butyric acid has been identified as a major energy source for colonic epithelial cells [67]. In this study, we found that the piglets in the SM group had higher relative abundances of Coprococcus than those in the CON group, which indicates the potential for the increased intestinal availability of butyrate. Similarly, a reduced diarrhea incidence has been proven to be one of the strategies by which Prevotella improves the intestinal immunity and promotes animal growth [68,69]. Our results indicated that the relative abundances of Prevotella in the SM group were higher than those in the other groups, which partially agrees with Li et al., who observed that the combination of SCFAs and MCFAs increased the relative abundances of Prevotella [36]. We speculated that SB, along with MCFA supplementation, might modulate the gut microbiota composition and benefit the host’s health. Moreover, it has been reported that Faecalibacterium is an anti-inflammatory intestinal commensal microbe that can suppress the TLR4/NF-κB signaling pathway in intestinal epithelial cells. Importantly, we found that the relative abundances of Faecalibacterium were particularly increased in the colonic digesta of the piglets when the sows were fed the SMP diet, and it might play an anti-inflammatory role and could promote intestinal development. ## 5. Conclusions In conclusion, our results indicate that diets supplemented with different combinations of SB, MCFAs, and omega-3 PUFAs during late gestation and lactation can efficiently improve the growth performance, immune function, antioxidant capability, and intestinal microbiota, as well as decrease the incidence of diarrhea, in suckling piglets. Additionally, dietary SMP supplementation had better effects on piglet intestinal health and probably through gut microorganism alterations. 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--- title: PDZK1-Interacting Protein 1(PDZKIP1) Inhibits Goat Subcutaneous Preadipocyte Differentiation through Promoting Autophagy authors: - Dingshuang Chen - Yanyan Li - Tingting Hu - Chengsi Gong - Guangyu Lu - Xiaotong Ma - Yong Wang - Youli Wang - Yaqiu Lin journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044287 doi: 10.3390/ani13061046 license: CC BY 4.0 --- # PDZK1-Interacting Protein 1(PDZKIP1) Inhibits Goat Subcutaneous Preadipocyte Differentiation through Promoting Autophagy ## Abstract ### Simple Summary PDZK1-interacting protein 1 (PDZK1IP1) is a membrane-associated non-glycosylated protein and is involved in development and tumorigenesis. However, the role of PDZK1IP1 in goat subcutaneous preadipocyte differentiation is unknown. In this work, we found that PDZK1IP1 acts as a regulator of adipogenesis, and inhibits goat subcutaneous preadipocyte differentiation by promoting autophagy. The results will help to better understand the biological functions of PDZK1IP1 in goat subcutaneous preadipocytes and improve studies of its molecular mechanism. ### Abstract PDZK1IP1 is highly expressed in tumor tissue and has been identified as a tumor biomarker. However, the role of PDZK1IP1 in goat subcutaneous preadipocyte differentiation remains largely unknown. The molecular mechanism of autophagy in regulating the differentiation of goat subcutaneous preadipocytes has not been clarified yet. In our study, PDZK1IP1 gain of function and loss of function were performed to reveal its functions in preadipocyte differentiation and autophagy. Our results showed that the overexpression of PDZK1IP1 inhibited the differentiation of goat subcutaneous preadipocytes, whereas it promoted autophagy. Consistently, the knockdown of PDZK1IP1 demonstrated the opposite tendency. Next, we investigated whether PDZK1IP1 inhibited the differentiation of goat preadipocytes by regulating autophagy. We found that inhibiting autophagy can rescue the PDZK1IP1-induced differentiation restraint in goat subcutaneous preadipocytes. In conclusion, PDZK1IP1 acts as a regulator of adipogenesis, and inhibits goat subcutaneous preadipocyte differentiation through promoting autophagy. Our results will contribute to further understanding the role and mechanism of PDZK1IP1 in controlling adipogenesis. ## 1. Introduction Adipose tissue is of high significance for numerous mammals and plays a critical role in regulating lipid homeostasis and the energy balance in the body [1,2,3]. Adipose tissue in mammals can be classified into visceral fat (VAT), subcutaneous fat (SAT) and intramuscular fat (IMF) according to its location. In different sites of the body, these adipose tissue types have different biological functions. Subcutaneous fat (SAT) and intramuscular fat (IMF) in humans are associated with multiple metabolic dysfunctions, such as obesity and diabetes [4]. Visceral fat (VAT), compared with subcutaneous fat (SAT) and intramuscular fat (IMF), is viewed to be the more adverse adipose tissue depot and has been linked to dyslipidemia, cardiovascular diseases and insulin resistance [5,6,7]. However, subcutaneous fat (SAT) and intramuscular fat (IMF) in farm animals have drawn increased attention, because they are an important factor that impacts meat quality [8]; for example, high intramuscular fat content could increase meat tenderness, flavor and juiciness to improve meat quality. However, increased subcutaneous fat content can reduce the lean meat ratio of the carcass, reducing the economic value of the meat [9,10]. Therefore, shedding light on the lipid metabolism mechanism is crucial for human diseases and agricultural economy. Autophagy is an essential self-protection mechanism that maintains cellular homeostasis by eliminating excess or damaged organelles, proteins and macromolecules [11]. It has been shown that autophagy is involved in the differentiation of several cell types, including preadipocytes. In 3T3-L1 preadipocytes, knockdown of autophagy-related gene 7 (Atg7), which is a key autophagy gene, will decrease the protein levels of preadipocyte differentiation markers and inhibit lipid accumulation. An adipocyte-specific ATG7-knockout mice model displayed a lean phenotype and decreased white adipose tissue (WAT) mass [12,13]. In addition, autophagy is upregulated in the adipose tissue of human obese patients [14,15]. The above studies provide strong evidence that autophagy plays a vital role in preadipocyte differentiation. PDZK1-interacting protein 1 (PDZK1IP1), a 17 kDa insoluble and non-glycosylated protein, is also termed MAP17, SPAP and DD96 [16,17]. It was initially identified as an epithelium-specific molecule and later found to be highly expressed in breast, cervical, ovarian and prostate tumors [18,19]. Moreover, there is evidence that PDZK1IP1 has a widespread impact on tumor cell biological functions, including proliferation, apoptosis, migration, invasion and so on [20,21,22]. This shows that PDZK1IP1 is highly associated with cancer progression. An increasing number of reports regarding the functions and influences of PDZK1IP1 on multiple cancers have been released, and in depth mechanistic studies are also ongoing. However, the regulating effects of PDZK1IP1 in regulating lipid deposition in preadipocytes have not been reported. In our previous study, we successfully cloned the goat PDZK1P1 gene sequence and demonstrated that PDZK1IP1 overexpression could promote goat subcutaneous preadipocyte proliferation [23]. In addition, it is worth noting that bortezomib can induce autophagy in breast cancer cells, while overexpressed PDZK1IP1 can inhibit autophagy in these cells [24]. This finding indicates that PDZK1IP1 could regulate autophagy. As mentioned previously, autophagy is involved in the differentiation of preadipocytes. Therefore, we wish to determine whether PDZK1IP1 affects the autophagy and differentiation of subcutaneous preadipocytes of goats, and whether autophagy regulated by PDZK1IP1 is correlated with adipocyte differentiation. Thus, the current study sought to investigate the underlying roles and potential mechanisms of PDZK1IP1 in goat subcutaneous preadipocyte lipid accumulation. Our study focused on the effects of PDZK1IP1 on goat subcutaneous preadipocyte differentiation via in vitro adipocyte models. ## 2.1. Animals and Tissue Collection In this study, animal samples, which were from Jianzhou Daer goats (Capra hicus), were approved by Sichuan Jianzhou Dageda Aminal Husbandry Co., Ltd. (Jianyang, Sichuan, China). The body weight of goats was approximately 50 kg. In addition, these goats were given ad libitum access to the same diet and water before slaughter. A total of three Jianzhou Daer goats (Capra hicus), male, one year old, were slaughtered. Tissue samples including heart, liver, spleen, lungs, kidney, longissimus dorsi, biceps femoris, arm triceps, abdominal fat and subcutaneous fatty tissue were harvested and immediately frozen in liquid nitrogen. The slaughter procedure was conducted at the College of Animal Science and Veterinary, Southwest Minzu University. All experimental procedures were approved by the Institutional Animal Care and Use Committee of Southwest Minzu University (Chengdu, Sichuan, China) with approval No. 2020086, 2020. ## 2.2. Cell Isolation and Cell Culture Goat subcutaneous preadipocytes were isolated and cultured, as previously reported [25]. Briefly, in our previous study, subcutaneous adipose tissue was collected under sterile conditions from the backs of 7-day-old goats ($$n = 3$$, male) and was cut with scissors into small pieces. The minced tissue was digested with collagenase type I at 37 °C for 1 h. After digestion, these samples were filtered (75-μm filter) and centrifuged (2000 r/min for 5 min). Following this, red blood cell lysis was performed to remove red blood cells, and the cell suspensions were centrifuged (1500 r/min for 5 min) again. Then, the cell precipitates were re-suspended in DMEM/F12 (Hyclone, Logan, UT, USA) containing $10\%$ FBS (Gemini, Calabasas, CA, USA) and $1\%$ biresistance (Gemini, Calabasas, CA, USA). The cell suspension was seeded on 60-mm dishes and cultured in the incubator (37 °C, $5\%$ CO2). ## 2.3. Cell Transfection Goat subcutaneous preadipocytes were seeded in 6-well or 24-well plates and transfected using TurboFect Transfection Reagent (Thermo, Waltham, MA, USA) according to the manufacturer’s instructions. For transfection in 6-well plates, the transfection mixture comprising 2 μg plasmid, 6 μL of transfection reagent and 400 μL of Opti-MEM (Gibco, Calabasas, CA, USA) was added to each well. Likewise, transfection mixture including 6 μL siRNA (20 µM), 6μL of transfection reagent and 400 μL of Opti-MEM (Gibco, Calabasas, CA, USA) was added to each well. The PDZK1IP1-overexpressing plasmid vector (PDZK1IP1) was previously constructed and saved in our lab. The Si-PDZK1IP1 directed against PDZK1IP1 was purchased from GnenPharma (GnenPharma, Shanghai, China). The sequences of Si-PDZK1IP1 and the Si negative control (si-NC) were as follows:Si-PDZK1IP1:forward strand: 5′-GAGAAUGCCUAUGAGAACATT−3′.reverse strand: 5′-UGUUCUCAUAGGCAUUCUCTT−3′.Si-NC:forward strand: 5′-CAAUCGCCUUUGCUGUCAATT−3′.reverse strand: 5′-ACGUGACACGUUCGGAGAATT−3′. ## 2.4. Induced Differentiation of Goat Subcutaneous Preadipocytes Goat subcutaneous preadipocytes in the logarithmic growth phase were harvested for differentiation induction. Goat subcutaneous preadipocytes were seeded on 6-well plates at the density of 8 × 104 cells/well. At 24 h after transfection, medium was replaced with adipocyte-induction medium (DMEM/F12 medium containing $10\%$ FBS, $1\%$ penicillin and streptomycin and 50 µmol/L oleic acid) and the cells were cultured for an additional 48 h. Then, the cells were harvested 48 h after the induction of differentiation and used in the subsequent experiments. ## 2.5. Oil Red O and Bodipy Staining For Oil Red O staining and Bodipy staining, cells were seeded in 24-well plates at a density of 1 × 104 cells per well. Then, 24 h after transfection, goat subcutaneous preadipocytes were induced in complete medium with oleic acid (50 µM) for up to 48 h. The 24-well plate was removed, the supernatant was discarded, and 1 mL pre-chilled PBS was added to wash cells in each well. At room temperature, goat subcutaneous adipocytes were fixed with $4\%$ paraformaldehyde for 15 min and then washed with pre-chilled PBS twice. Next, cells were stained using Oil Red O or Bodipy working solution for 20 min, and then washed twice in pre-chilled PBS. Lastly, goat subcutaneous adipocytes were observed and photographed under an Olympus IX-73 microscope (Tokyo, Japan). Staining was quantified by adding $100\%$ isopropanol into each of the 24 wells to dissolve Oil Red O and measuring the absorbance at 490 nm. ## 2.6. AO and MDC Staining Detection of autophagy was performed by using an AO staining kit (Solarbio, Beijing, China) and MDC staining kit (Solarbio, Beijing, China) according to the manufacturer’s instructions. In brief, goat subcutaneous preadipocytes were transfected with plasmid or Si-RNA for 48 h and then were fixed with $4\%$ paraformaldehyde for 15 min. After fixation, AO working solution (1 mg/mL) and MDC working solution were used at room temperature to stain the cells for 30 min. The staining then was observed under a fluorescence microscope and photographed (magnification: 400×). ## 2.7. Western Blotting The total proteins of goat subcutaneous preadipocytes were extracted via RIPA cell lysis buffer on ice for 30 min, followed by centrifugation at 12,000 rpm for 10 min at 4 °C. The total protein in each sample was detected by using the BCA Protein Assay Kit (Biosharp, Shanghai, China), and denatured protein (20 µg/lane) from each sample was subjected to $12\%$ SDS-PAGE. The electrophoresed proteins were subsequently transferred to a 0.22 µm PVDF membrane with $5\%$ nonfat milk at room temperature for 2 h. Then, the membranes were incubated with anti-LC3 (1:500, Wanleibio, Jilin, China, bs−5713R), anti-p62 (1:500, Wanleibio, Jilin, China, bs−5713R) and anti-β-actin (1:5000, Abways, Shanghai, China, AB0035) at 4 °C overnight. After washing, the membranes were washed in TBST buffer and horseradish peroxidase-labeled secondary antibodies (1:5000, Abways, Shanghai, China, AB0102) were incubated for 2 h. Finally, we used ECL (Bio-Rad, Hercules, CA, USA) to detect target proteins. ## 2.8. Real-Time Quantitative PCR (qRT-PCR) Analysis Total RNA was isolated from goat subcutaneous preadipocytes with an RNAiso Plus reagent (Takara, Dalian, China), according to the manufacturer’s instructions. Then, 1 μg total RNA was reverse-transcribed using the RevertAid First Strand cDNA Synthesis Kit (Thermo, Waltham, MA, USA) and real-time PCR (qRT-PCR) was subsequently performed using the SYBR Green Premix Ex Taq Kit (TaKaRa, Japan) on the BioRad Real-Time PCR System. All of the primer sequence information in the study is presented in Table 1 and all gene expression analyses were calculated using the (2−△△ct) method [26]. ## 2.9. Statistical Analysis All data were analyzed using the GraphPad Prism 9 software. For comparisons between two groups or multiple groups, Student’s two-tailed t-test or multiple comparison test was performed to evaluate the significant differences. In every figure, significance is represented with asterisks (* $p \leq 0.05$; $p \leq 0.01$). The number of biological repeats in all bars was three. ## 3.1. The Expression Level of PDZKIP1 Was Downregulated during the Differentiation of Goat Subcutaneous Preadipocytes To probe the role of PDZK1IP1 in goat subcutaneous preadipocytes, we first examined its expression in various goat tissue samples. The results of qRT-PCR analysis showed that PDZK1IP1 mRNA was expressed in all the tested tissue samples. The expression level of PDZK1IP1 was higher in the kidneys and lower in abdominal fat compared to the expression level in the heart. In addition, PDZK1IP1 was also expressed in subcutaneous adipose tissue (Figure 1A). Because subcutaneous fat deposition is crucial for meat quality, we wished to investigate whether PDZK1IP1 is involved in the regulation of subcutaneous fat deposition. Subsequently, we isolated goat subcutaneous preadipocytes and induced differentiation with 50 µmol/L oleic acid for 4 days. Goat subcutaneous preadipocyte differentiation was evaluated by Oil Red O staining, and the results showed that both the size and the number of lipid droplets were increased as the induction time was prolonged (Figure 1B,C). At the same time, we measured the expression of three differentiation marker genes, PPARγ, C/EBPα and C/EBPβ, by qRT-PCR. We found that the expression of these three markers was increased during preadipocyte differentiation induction (Figure 1D–F). The above morphological and molecular detection results indicated that goat subcutaneous preadipocytes were successfully isolated. Next, we examined the expression level of PDZK1IP1 during goat subcutaneous preadipocyte differentiation by qRT-PCR. The result demonstrated that the expression of PDZK1IP1 was downregulated in differentiated adipocytes (Figure 1G). These results indicated that PDZK1IP1 may be involved in the process of goat subcutaneous preadipocyte differentiation. ## 3.2. PDZK1IP1 Functioned as a Repressor of Goat Subcutaneous Preadipocyte Differentiation From the above study, we sought to determine whether PDZKIP1 is a regulator of goat subcutaneous preadipocyte differentiation; the PDZK1IP1 expression plasmid (PDZK1IP1) or an empty vector (Vector) was transiently transfected into goat subcutaneous preadipocytes for 48 h. qRT-PCR examination showed that the cells transfected with PDZK1IP1 expression plasmid could significantly increase their PDZK1IP1 expression when compared to the cells transfected with the control vector (Figure 2A). Subsequently, as judged by Oil Red O staining, the semi-quantitative assessment of Oil Red O content and Bodipy staining, the overexpression of PDZK1IP1 can significantly decrease lipid droplet accumulation in goat subcutaneous adipocytes (Figure 2B–D). Additionally, the adipocyte differentiation markers, such as PPARγ, C/EBPα, C/EBPβ and SREBP1, were significantly downregulated in PDZK1IP1 overexpression cells (Figure 2E). Nevertheless, we further confirmed the anti-adipogenic effect of PDZK1IP1 through loss of function studies, in which we hypothesized that PDZK1IP1 knockdown would enhance adipose conversion. We then transfected goat subcutaneous preadipocytes with specific Si-RNA to knock down PDZK1IP1. qRT-PCR was used to detect the expression level of PDZK1IP1 in the subcutaneous preadipocytes of goats. Compared with the control cells, its expression was significantly downregulated (Figure 3A). The loss of function assay found that Si-PDZK1IP1 cells displayed stronger adipogenesis potential, including more lipid droplets formed (Figure 3B–D), and the expression of adipocyte marker genes such as C/EBPα, C/EBPβ and AP2 was upregulated (Figure 3E). Taken together, these data suggested that PDZK1IP1 acts as a repressor to regulate goat subcutaneous preadipocyte differentiation. ## 3.3. PDZK1IP1 Positively Modulates Autophagy Activation in Goat Subcutaneous Preadipocytes Studies have shown that the overexpression of PDZK1IP1 inhibits bortezomib-induced autophagy in breast cancer [24], while the effect of PDZK1IP1 on autophagy regulation in goat subcutaneous preadipocytes has not been reported before. When autophagy is activated, LC3I is converted into LC3II, which is localized to autophagosome membranes and is essential for autophagosome membrane biogenesis. Therefore, LC3II has been recognized as a biomarker of the formation of autophagosomes [27]. In addition to LC3II, p62 (a protein specifically degraded in lysosomes) is another widely used autophagy marker, which is downregulated in the autophagic process [28]. In order to clarify the role of PDZK1IP1 in goat subcutaneous preadipocyte autophagy, the expression levels of two autophagy-related proteins were analyzed firstly by Western blot assay. In our study, the Western blot experiment showed that PDZK1IP1 overexpression significantly increased the conversion of LC3I to LC3II and reduced the expression of p62, indicating that PDZK1IP1-induced autophagic and autophagic flux was unobstructed (Figure 4A, see supplementary materials Figure S1 for all western original images). Then, acridine orange (AO) and monodansylcadaverine (MDC) were used to observe the autophagosome. We found that the number of fluorescent puncta was increased upon PDZK1IP1 overexpression (Figure 4B). Next, a loss of function assay was performed with PDZK1IP1-specific siRNA in goat subcutaneous preadipocytes to investigate the effect of PDZK1IP1 on autophagy. After PDZK1IP1 was knocked down, LC3II accumulation and P62 expression were measured by Western blot to evaluate autophagy initiation and autophagy flux. Compared to the control cells, knockdown of PDZK1IP1 significantly decreased the LC3-II/I ratio and increased the p62 level, indicating that Si-PDZK1IP suppresses autophagy initiation and inhibits autophagic degradation (Figure 5A, see supplementary materials Figure S1 for all western original images). Subsequently, AO and MDC staining also were performed to observe autophagosomes, which indicated that PDZK1IP1 knockdown decreased autophagosome formation compared with the control group (Figure 5B). This confirmed our conjecture. Altogether, our data suggested that PDZK1IP1 promotes autophagy formation in goat subcutaneous preadipocytes. ## 3.4. Inhibition of Autophagy Can Rescue PDZK1IP1-Induced Differentiation Restraint in Goat Subcutaneous Preadipocytes Our study has shown that PDZK1IP1 inhibits the differentiation and promotes the autophagy of goat subcutaneous preadipocytes. Therefore, we considered whether PDZK1IP1 affects goat subcutaneous preadipocyte differentiation through the autophagy pathway and treated PDZK1IP1 overexpression cells and control cells with or without NH4CL during adipogenesis. We found that preventing the degradation of autophagosomes adequately increased the expression levels of LC3II and p62 (Figure 6A, see supplementary materials Figure S1 for all western original images). Meanwhile, compared with the control cells without NH4CL, the number of autophagosomes was significantly increased (Figure 6B,C). These results also showed that NH4CL successfully inhibited autophagy and decreased the difference in autophagy between the PDZK1IP1 overexpression group and the control group. To further validate the role of PDZK1IP1-regulated autophagy in its inhibitry effects on adipogenic differentiation, next, we treated cells with 20 mM NH4CL for 48 h during goat subcutaneous preadipocyte differentiation. The efficiency of PDZK1IP1 overexpression was assessed by qRT-PCR, and results showed that PDZK1IP1 was successfully overexpressed in the cells treated with or without NH4CL (Figure 7A). In addition, we observed that NH4CL treatment could reverse the inhibition of lipid accumulation caused by PDZK1IP1 overexpression (Figure 7B–D). Consistent with the phenotype, the mRNA levels of adipocyte differentiation markers, including PPARγ, C/EBPα and C/EBPβ, but not SREBP1, were remarkably downregulated in PDZK1IP1 overexpression cells, which could be rescued to normal levels by NH4CL treatment (Figure 7E). In summary, these data demonstrate that PDZK1IP1 inhibits preadipocytes’ differentiation by promoting autophagy. ## 4. Discussion In our previous studies, we have successfully cloned PDZK1IP1’s gene sequence and demonstrated that PDZK1IP1 could promote subcutaneous preadipocyte proliferation. However, we still lack knowledge of the effect of PDZK1IP1 on goat subcutaneous preadipocytes’ differentiation. Previously, it was shown that PDZK1IP1 was rarely expressed in normal tissue but highly expressed in tumor tissue, and PDZK1IP1 may be an oncogene that is correlated with cancer development [20,29,30]. Thus, we examined the expression of PDZK1IP1 in multiple goat tissue samples and found it to be expressed in all tested tissue types, including subcutaneous adipose tissue. We hypothesized that PDZK1IP1 may play a certain role in subcutaneous adipose tissue. We then examined the level of PDZK1IP1 expression in goat subcutaneous differentiated adipocytes and found that PDZK1IP1 expression was significantly changed. This forced us to determine whether PDZK1IP1 has an effect on goat subcutaneous preadipocyte differentiation and the specific mechanistic function of PDZK1IP1 in regulating goat subcutaneous preadipocyte differentiation. To finally confirm our conjecture, gain and loss of function experiments were performed in goat subcutaneous preadipocytes in vitro. We found that PDZK1IP1 could inhibit lipid droplet accumulation at the morphological level. At the molecular level, we examined the expression of a total of five adipocyte differentiation markers, namely PPARγ, C/EBPα, C/EBPβ, AP2 and SREBP1. Upon the overexpression of PDZK1IP1, we found that PPARγ, C/EBPα, C/EBPβ and SREBP1 were remarkably downregulated (Figure 2E). However, C/EBPα, C/EBPβ and AP2 were remarkably upregulated upon PDZK1IP1 knockdown (Figure 3E). Interestingly, only C/EBPα and C/EBPβ showed the opposite trend after both overexpression and interference. This suggests that PDZK1IP1 likely influences, directly or indirectly, C/EBPα and C/EBPβ expression to regulate goat subcutaneous preadipocyte differentiation. Moreover, regarding why the expression of PPARγ and SREBP1 was not upregulated after PDZK1IP1 knockdown, we believe that this may be because the regulation of cell signaling networks is complex. Thus, our gain and loss of function experiments indicated that PDZK1IP1 is a repressor of goat subcutaneous preadipocyte differentiation at both the morphological and molecular levels. The effect of PDZK1IP1 on autophagy is rarely reported. One study showed that the overexpression of PDZK1IP1 inhibited bortezomib-induced autophagy of breast cancer [24]. However, the effect of PDZK1IP1 on the autophagy level of goat preadipocytes has not been reported. As is well known, when autophagy is activated, LC3I will be converted into LC3II, which is localized to autophagosome membranes and is an essential step for autophagosome membrane biogenesis. Therefore, LC3II has been recognized as a biomarker of autophagosome formation [27]. Furthermore, p62 (a protein specifically degraded in lysosomes) can also be used as an autophagy marker besides LC3II [28]. In our study, we found that low and strong expression of PDZK1IP1 attenuated and enhanced LC3II transformation in goat subcutaneous preadipocytes, respectively, suggesting that PDZK1IP1 can promote autophagy initiation. However, both reduced autophagosome degradation and enhanced LC3II conversion can lead to LC3II accumulation. We examined the expression levels of p62 in our next study and observed the downregulation of the p62 protein level after the overexpression of PDZK1IP1 (Figure 4A). Nevertheless, the protein level of p62 was upregulated after interference with PDZK1IP1 (Figure 5A). This suggests that the increase in LC3II levels is not mediated by the retardation of autophagosome degradation. In conclusion, our data indicated that PDZK1IP1 promotes autophagy in goat subcutaneous preadipocytes. Autophagy, a process of lysosome-dependent degradation, can be broadly divided into nonselective and selective forms. Since the discovery of selective autophagy, including lipophagy and mitophagy [26,31], the relationship between autophagy and adipose metabolism has become an ever-increasing topic of interest. It was previously demonstrated that autophagy increased in the adipose tissue of both obese mice and obese humans [14,32]. The specific knockdown of ATG7 in adipose tissue led to mutant mice becoming leaner and the white adipose tissue content was lower as compared with wild-type mice. By knocking out ATG7 in 3T3-L1 preadipocytes, ATG7 knockdown cells showed reduced TG deposition and the downregulation of markers of adipocyte differentiation [12,13]. Similarly, the specific knockdown of ATG5, which is a protein necessary for autophagy, in mouse MEFs blocked the differentiation of preadipocytes [33]. Previous work has shown that the differentiation of 3T3-L1 is inversely correlated with autophagy. They found that treatment with the autophagy inducer rapamycin inhibited the differentiation of 3T3-L1 preadipocytes [34]. Moreover, the study of autophagy in the regulation of bovine preadipocyte differentiation has been reported [35]. In the present study, we investigated the functions of PDZK1IP1 in goat subcutaneous preadipocytes and found that PDZK1IP1 inhibited preadipocyte differentiation but promoted autophagy initiation and increased autophagic flux. Thus, we speculated that PDZK1IP1 may regulate the differentiation of goat subcutaneous preadipocytes by affecting autophagy. In the next step, autophagy-specific inhibitor NH4CL was used to inhibit autophagy in goat subcutaneous preadipocytes, and the relationship between autophagy and differentiation was further studied. NH4CL has been considered as an autophagy inhibitor with the ability to increase the lysosomal pH, thus preventing autophagic protein degradation to block autophagic flux [36,37,38,39]. Firstly, we demonstrated that 20 mM NH4CL successfully inhibited autophagy. Then, during goat subcutaneous preadipocytes’ differentiation, we treated these cells with 20 mM NH4CL for 48 h after PDZK1IP1 was overexpressed. We observed that PDZK1IP1 overexpression reduced the lipid accumulation at the morphological level by Oil Red O staining and Bodipy staining, However, there was no significant difference in lipid accumulation after the overexpression of PDZK1IP1 in the 20 mM NH4CL treatment group (Figure 7B–D). Consistent with the phenotype, the mRNA levels of adipocyte differentiation markers, including PPARγ, C/EBPα and C/EBPβ, were remarkably downregulated after PDZK1IP1 was overexpressed, which could be rescued to normal levels by NH4CL treatment (Figure 7E). Similarly, we also observed that only the pattern of C/EBPβ expression was consistent with our speculation. Although the mRNA expression of PPARγ and C/EBPα could be rescued to normal levels by NH4CL treatment, the overall levels were higher than in the control group. The regulation of gene expression is often multifactorial, because gene expression regulation is a complex and dynamic process, which involves multi-level regulation, including transcriptional, post-transcriptional, translational and post-translational events. Alternatively, gene expression also may be regulated by one or more signaling pathways. 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--- title: Estimation, Evaluation and Characterization of Carbapenem Resistance Burden from a Tertiary Care Hospital, Pakistan authors: - Aamir Jamal Gondal - Nakhshab Choudhry - Hina Bukhari - Zainab Rizvi - Shah Jahan - Nighat Yasmin journal: Antibiotics year: 2023 pmcid: PMC10044297 doi: 10.3390/antibiotics12030525 license: CC BY 4.0 --- # Estimation, Evaluation and Characterization of Carbapenem Resistance Burden from a Tertiary Care Hospital, Pakistan ## Abstract Carbapenem resistance has become major concern in healthcare settings globally; therefore, its monitoring is crucial for intervention efforts to halt resistance spread. During May 2019–April 2022, 2170 clinical strains were characterized for antimicrobial susceptibility, resistance genes, replicon and sequence types. Overall, $42.1\%$ isolates were carbapenem-resistant, and significantly associated with Klebsiella pneumoniae (K. pneumoniae) ($$p \leq 0.008$$) and Proteus species ($$p \leq 0.043$$). Carbapenemases were detected in $82.2\%$ of isolates, with blaNDM-1 ($41.1\%$) associated with the ICU ($p \leq 0.001$), cardiology ($$p \leq 0.042$$), pediatric medicine ($$p \leq 0.013$$) and wound samples ($$p \leq 0.041$$); blaOXA-48 ($32.6\%$) was associated with the ICU ($p \leq 0.001$), cardiology ($$p \leq 0.008$$), pediatric medicine ($p \leq 0.001$), general surgery ($$p \leq 0.001$$), general medicine ($$p \leq 0.005$$) and nephrology ($$p \leq 0.020$$); blaKPC-2 ($5.5\%$) was associated with general surgery ($$p \leq 0.029$$); blaNDM-1/blaOXA-48 ($11.4\%$) was associated with general surgery ($p \leq 0.001$), and wound ($$p \leq 0.002$$), urine ($$p \leq 0.003$$) and blood ($$p \leq 0.012$$) samples; blaOXA-48/blaVIM ($3.1\%$) was associated with nephrology ($p \leq 0.001$) and urine samples ($p \leq 0.001$). Other detected carbapenemases were blaVIM ($3.0\%$), blaIMP ($2.7\%$), blaOXA-48/blaIMP ($0.1\%$) and blaVIM/blaIMP ($0.3\%$). Sequence type (ST)147 ($39.7\%$) represented the most common sequence type identified among K. pneumoniae, along with ST11 ($23.0\%$), ST14 ($15.4\%$), ST258 ($10.9\%$) and ST340 ($9.6\%$) while ST405 comprised $34.5\%$ of Escherichia coli (E. coli) isolates followed by ST131 ($21.2\%$), ST101 ($19.7\%$), ST10 ($16.0\%$) and ST69 ($7.4\%$). Plasmid replicon types IncFII, IncA/C, IncN, IncL/M, IncFIIA and IncFIIK were observed. This is first report describing the carbapenem-resistance burden and emergence of blaKPC-2-ST147, blaNDM-1-ST340 and blaNDM-1-ST14 in K. pneumoniae isolates and blaNDM-1-ST69 and blaNDM-1/blaOXA-48-ST69 in E. coli isolates coharboring extended-spectrum beta-lactamases (ESBLs) from Pakistan. ## 1. Introduction Since the glorious discovery of first antibiotic, revolutionary changes occurred in the health care settings that helped in reducing the suffering of mankind by preventing the onset of infectious diseases [1]. However, due to misuse of antibiotics, the mounting rise in antimicrobial resistance (AMR) has posed greater clinical challenges and public health threats with every passing day as accepted by health regulatory systems across continents [2]. In healthcare settings, factors contributing in AMR [3] include easy access and unreasonable consumption of broad-spectrum antibiotics, inadequate guidelines for antibiotics utilization guidelines, lack of audit policies for antimicrobials, transmission of resistant strains from patient to patient and through health care providers, absence of isolation of patients colonized with resistant microbes and sub-optimal infection control measures [4]. At the moment, AMR is considered to be accountable for more than 700,000 deaths per year globally and it is anticipated that this epidemic rise will result in 10 million deaths annually by 2050, which will increase the global economic burden with an estimated cost of USD 100 trillion [5,6]. The AMR situation in Pakistan is worrisome with a recent report suggesting an antimicrobial consumption of $66.7\%$ in hospitals and $62.2\%$ in the community; however, no national surveillance system exists, thereby making it difficult to achieve a clear picture of AMR burden [7,8]. Antibiotic targets are usually preserved across the bacterial species and used for the development of new antibiotics [9]. β-lactams are the largest group of antibiotics that are most regularly prescribed in health care settings considering their safety, effectiveness and wide range of activity against Gram-negative and Gram-positive microorganisms [10]. Antibiotics are classified into several groups depending on their action mechanisms. Over the years, bacteria have developed several sophisticated resistance mechanisms [11]. However, the enzymatic degradation of antibiotics is considered as one of the most widely used resistance mechanisms by bacteria [9]. Many enzymes have been discovered that play a critical role in resistance emergence by degrading and modifying the function of antibiotics such as carbapenemases. Carbapenemase-encoding genes are mainly found on mobile genetic elements, hence contributing to their rapid dissemination across different bacterial species [12]. Clinically relevant core carbapenemases include KPC, NDM, OXA48, VIM and IMP [13]. Notably, carbapenem hydrolyzing enzymes reported among Enterobacterales from Pakistan include blaKPC2/blaNDM-1 [14], blaNDM-1/blaOXA-48 [15], blaKPC2 [16], blaNDM-7, blaVIM and blaIMP [17,18]. Different clinical settings facilitate differently towards the resistance development by the colonization of carbapenem-resistant Enterobacterales (CRE) leading to frequent outbreaks and further exhausting the depleting pool of effective antimicrobials [7]. Recently, an overall $16.5\%$ infection risk was reported among CRE-colonized patients [19], while a $28\%$ infection rate of CRE was reported from Egypt, consisting of $83\%$ Escherichia coli (E. coli) and $17\%$ Klebsiella pneumoniae (K. pneumoniae) [20]. High prevalence of CRE isolated from sink drains of health care facilities were observed recently from Pakistan [21]. Similarly, carbapenemase-producing Enterobacterales originating from kitchen of hematology ward were implicated in resistance transmission [22]. Global reports about nosocomial outbreaks of carbapenemase-producing strains showed its association with different clinical wards such as the VIM-producing Enterobacter cloacae outbreak in association with an ICU from France [23], IMP-6 CPE from Japan [24], KPC-2-producing K. pneumoniae from Greece and China [25,26], OXA-23-carrying carbapenem-resistant Acinetobacter baumannii (A. baumannii) in an ICU ward from China [27] and NDM-1-producing K. pneumoniae associated with an ICU from a Portuguese hospital and France [28,29]. Reports from Pakistan have described increasing carbapenem resistance (CR) rates up to $71\%$ among CRE due to carbapenemases [3,30]. However, there are no data available from Pakistan that comprehensively describes the CR burden in association with clinical setup among Enterobacterales. Carbapenemases usually spread through clonal lineages associated with conjugative plasmids, thus making their dispersal more convenient among nosocomial pathogens [31]. Carbapenemase-encoding genes are found associated with different sequence types such as the rapid spread of blaKPC–3-ST384 K. pneumoniae and blaKPC–2-ST101 among Enterobacterales reported from Spain [32,33,34], blaKPC–2-ST15 K. pneumoniae from China [35], blaNDM-1-ST307 K. pneumoniae from France [36], blaOXA-48-ST399 E. coli from the UK [37], blaNDM-1-ST147 K. pneumoniae from Italy [38], blaNDM-1-ST11 K. pneumoniae from Portugal [29], blaVIM-2-ST121 Pseudomonas aeruginosa (P. aeruginosa) from Netherlands [39] and blaNDM-1-ST11 K. pneumoniae from Pakistan [40]. This heterogeneous clonal background showed its importance for global dissemination of carbapenemases among Enterobacterales. Therefore, it is critical to assess the exposure risk and genetic profile of CRE in health care settings through surveillance to devise prevention strategies. The current study was designed to assess and characterize the CR burden in terms of antibiotic resistance profile, prevalence of antibiotic resistance genes, genetic diversity and clonality from Pakistan. ## 2.1. Phenotypic Identification and Distribution of Bacterial Strains During the study period, a total of 2170 clinical strains were collected from Mayo hospital, Lahore, Pakistan. The most prevalent species (spp.) among clinical strains were K. pneumoniae ($$n = 668$$, $30.8\%$) and E. coli ($$n = 544$$, $25.1\%$), while other genera were Pseudomonas ($$n = 384$$, $17.6\%$), Proteus ($$n = 175$$, $8.1\%$), Acinetobacter ($$n = 163$$, $7.5\%$), Citrobacter ($$n = 106$$, $5.0\%$), Morganella ($$n = 55$$, $2.5\%$), Providencia ($$n = 48$$, $2.2\%$) and Burkholderia ($$n = 27$$, $1.2\%$). Gender-wise categorization showed that clinical specimens were mainly obtained from males ($$n = 1288$$, $59.4\%$). The distribution of strains showed that the predominant origins of the collected specimens were general surgery units ($\frac{608}{2170}$, $28.0\%$), ICUs ($\frac{412}{2170}$, $19.1\%$) and general medicine units ($\frac{360}{2170}$, $16.6\%$) with wound samples ($\frac{587}{2170}$, $27.0\%$), pus samples ($\frac{473}{2170}$, $21.7\%$), blood samples ($\frac{261}{2170}$, $12.0\%$) and urine samples ($\frac{204}{2170}$, $9.4\%$) representing the main specimen types. The frequency of identified species, obtained from different specimen types and clinical wards is given in Table 1. It was observed that wound and blood samples were significantly associated with E. coli ($p \leq 0.001$), Pseudomonas spp. ( $p \leq 0.005$) and Citrobacter spp. ( $$p \leq 0.020$$); pus samples with K. pneumoniae ($$p \leq 0.0003$$) and Acinetobacter spp. ( $$p \leq 0.023$$); and urine samples with K. pneumoniae ($$p \leq 0.0003$$), E. coli ($p \leq 0.0001$) and Acinetobacter spp. ( $$p \leq 0.020$$). Furthermore, the ICU was significantly associated with K. pneumoniae ($$p \leq 0.04$$), E. coli ($p \leq 0.0001$), Pseudomonas spp. ( $$p \leq 0.0002$$) and Proteus spp. ( $$p \leq 0.013$$); the nephrology ward with K. pneumoniae ($$p \leq 0.02$$) and E. coli ($$p \leq 0.0001$$); the pediatric medicine ward with E. coli ($$p \leq 0.0008$$), Pseudomonas spp. ( $$p \leq 0.0001$$), Acinetobacter spp. ( $$p \leq 0.019$$) and Citrobacter spp. ( $$p \leq 0.019$$); and the general medicine unit with Acinetobacter spp. ( $$p \leq 0.008$$). ## 2.2. Antimicrobial Susceptibility Trend The antimicrobials used for susceptibility profiling of different species were selected as per criteria given by Magiorakos et al. [ 41]. Resistance against β-lactam combination agents, fluoroquinolones, aminoglycosides and trimethoprim/sulfamethoxazole was observed with higher susceptibility against tigecycline and polymyxin B. The following resistance rates of the antimicrobials were observed; cefazolin ($\frac{1101}{1294}$, $85.1\%$), cefepime ($\frac{1808}{2170}$, $83.3\%$), ceftazidime ($\frac{1779}{2170}$, $82.0\%$), cefuroxime ($\frac{1285}{1575}$, $81.6\%$), cefotaxime ($\frac{1401}{1786}$, $78.4\%$), ceftaroline ($\frac{922}{1212}$, $76.1\%$), ampicillin ($\frac{983}{1294}$, $76.0\%$), cefoxitin ($\frac{1125}{1490}$, $75.5\%$), aztreonam ($\frac{1476}{1994}$, $74.0\%$), ciprofloxacin ($\frac{1572}{2170}$, $72.4\%$), amoxicillin-clavulanic acid ($\frac{1059}{1469}$, $72.1\%$), trimethoprim-sulfamethoxazole ($\frac{904}{1375}$, $65.7\%$), amikacin ($\frac{1072}{2115}$, $50.7\%$), piperacillin-tazobactam ($\frac{895}{2170}$, $41.2\%$), doxycycline ($\frac{485}{1375}$, $35.5\%$), fosfomycin ($\frac{350}{928}$, $37.7\%$), ampicillin-sulbactam ($\frac{63}{163}$, $38.7\%$), polymyxin B ($\frac{91}{1215}$, $7.5\%$) and tigecycline ($\frac{99}{1373}$, $7.2\%$). CR was found in $42.1\%$ ($\frac{913}{2170}$) of isolates and $57.9\%$ ($\frac{1257}{2170}$) were carbapenem susceptible. Higher CR rates were detected among K. pneumoniae ($\frac{309}{913}$, $33.8\%$) and E. coli ($\frac{223}{913}$, $24.4\%$), followed by Pseudomonas spp. ( $\frac{169}{913}$, $18.5\%$), Acinetobacter spp. ( $\frac{67}{913}$, $7.3\%$), Proteus spp. ( $\frac{61}{913}$, $6.9\%$), Citrobacter spp. ( $\frac{45}{913}$, $4.9\%$), Providencia spp. ( $\frac{19}{913}$, $2.1\%$), Morganella spp. ( $\frac{15}{913}$, $1.6\%$) and Burkholderia spp. ( $\frac{5}{913}$, $0.5\%$). CR was significantly associated with K. pneumoniae ($$p \leq 0.008$$) and Proteus spp. ( $$p \leq 0.043$$). The details of antimicrobial susceptibility trends among individual species are given in Table 2. The CR burden was analyzed in clinical wards and specimen types. The prevalence of CR among clinical specimens was higher in wound samples ($\frac{292}{913}$, $32.0\%$), pus samples ($\frac{206}{931}$, $22.6\%$), urine samples ($\frac{103}{913}$, $11.3\%$) and blood samples ($\frac{97}{931}$, $10.6\%$), while the general surgery unit ($\frac{262}{913}$, $28.7\%$), general medicine unit ($\frac{180}{913}$, $19.7\%$) and ICU ($\frac{157}{913}$, $17.2\%$) were the dominant hospital sections involved in the CR spread. It was observed that the occurrence of CR was statistically significant among wound samples ($$p \leq 0.00001$$), urine samples ($$p \leq 0.01$$), tissue samples ($$p \leq 0.00001$$) and tip cell samples ($$p \leq 0.037$$). Additionally, the general medicine unit ($$p \leq 0.0008$$) and oncology ward ($$p \leq 0.006$$) were significantly associated with CR. The results are given in Table 3. ## 2.3. Prevalence of Antimicrobial Resistance Genes Carbapenemase production was found in $86.4\%$ ($\frac{789}{913}$) of isolates with K. pneumoniae ($\frac{283}{789}$, $35.9\%$), E. coli ($\frac{199}{789}$, $25.2\%$), Pseudomonas spp. ( $\frac{145}{789}$, $18.4\%$), Proteus spp. ( $\frac{53}{789}$, $6.7\%$), Acinetobacter spp. ( $\frac{49}{789}$, $6.2\%$), Citrobacter spp. ( $\frac{31}{789}$, $3.9\%$), Morganella spp. ( $\frac{12}{789}$, $1.5\%$), Providencia spp. ( $\frac{13}{789}$, $1.6\%$) and Burkholderia spp. ( $\frac{4}{789}$, $0.5\%$). On the other hand, $13.6\%$ ($\frac{124}{913}$) carbapenem-resistant strains were non-carbapenemase-producing, pointing towards the involvement of alternative resistance mechanisms for carbapenem-resistant phenotypes in this study population. Carbapenemase-encoding genes were detected in $82.2\%$ ($\frac{649}{789}$) of carbapenemase-producing isolates with $15.0\%$ ($\frac{97}{649}$) coharbored genes and $85.0\%$ ($\frac{552}{649}$) single genes. The frequency of carbapenemase resistance genes among detected species was $36.0\%$ ($\frac{234}{649}$) K. pneumoniae, $22.0\%$ ($\frac{143}{649}$) E. coli, $20.5\%$ ($\frac{133}{649}$) Pseudomonas spp., $7.2\%$ ($\frac{47}{649}$) Acinetobacter spp., $7.1\%$ ($\frac{46}{649}$) Proteus spp., $4.0\%$ ($\frac{26}{649}$) Citrobacter spp., $1.7\%$ ($\frac{11}{649}$) Providencia spp., $1.0\%$ ($\frac{6}{649}$) Morganella spp. and $0.5\%$ ($\frac{3}{649}$) Burkholderia spp. The detected carbapenemases were blaNDM-1 $41.1\%$ ($\frac{267}{649}$), blaOXA-48 $32.6\%$ ($\frac{212}{649}$), blaKPC-2 $5.5\%$ ($\frac{36}{649}$), blaVIM $3.0\%$ ($\frac{19}{649}$), blaIMP $2.7\%$ ($\frac{18}{649}$), blaNDM-1/blaOXA-48 $11.4\%$ ($\frac{74}{649}$), blaOXA-48/blaVIM $3.1\%$ ($\frac{20}{649}$), blaOXA-48/blaIMP $0.1\%$ ($\frac{1}{649}$) and blaVIM/blaIMP $0.3\%$ ($\frac{2}{649}$). Among carbapenemase gene-positive strains, $14.2\%$ ($\frac{92}{649}$) were XDR and $85.8\%$ ($\frac{557}{649}$) MDR. The results are given in Table 4. The distribution of detected carbapenemases was analyzed in relation to clinical wards and specimens. It was observed that blaKPC-2 was significantly associated with the general surgery unit ($\frac{16}{36}$, $44.4\%$, $$p \leq 0.029$$); blaNDM-1 with wound samples ($\frac{82}{267}$, $30.7\%$, $$p \leq 0.041$$), ICU ($\frac{74}{267}$, $27.7\%$, $p \leq 0.001$), cardiology ward ($\frac{9}{267}$, $3.4\%$, $$p \leq 0.042$$) and pediatric medicine ward ($\frac{9}{267}$, $3.4\%$, $$p \leq 0.013$$); blaOXA-48 with tip cell samples ($\frac{9}{212}$ $4.2\%$, $$p \leq 0.041$$), general surgery unit ($\frac{43}{212}$, $20.3\%$, $$p \leq 0.001$$), ICU ($\frac{23}{212}$, $10.8\%$, $p \leq 0.001$), general medicine unit ($\frac{59}{212}$, $27.8\%$, $$p \leq 0.005$$), nephrology ward ($\frac{3}{212}$, $1.4\%$, $$p \leq 0.020$$), cardiology ward ($\frac{19}{212}$, $9.0\%$, $$p \leq 0.008$$), pediatric medicine ward ($\frac{23}{212}$, $10.8\%$, $p \leq 0.001$) and orthopedic surgery ward ($\frac{13}{212}$, $6.1\%$, $$p \leq 0.007$$); blaVIM with tracheal secretion samples ($\frac{5}{19}$, $26.3\%$, $p \leq 0.001$), tip cell samples ($\frac{2}{19}$, $10.5\%$, $$p \leq 0.021$$) and oncology ward ($\frac{3}{19}$, $15.8\%$, $p \leq 0.001$); blaIMP with pus samples ($\frac{9}{18}$, $50.0\%$, $$p \leq 0.006$$), CV line samples ($\frac{2}{18}$, $11.1\%$, $p \leq 0.001$) and chest medicine ward ($\frac{3}{18}$, $16.7\%$, $$p \leq 0.001$$); blaNDM-1/blaOXA-48 with wound samples ($\frac{38}{74}$, $51.4\%$, $$p \leq 0.002$$), blood samples ($\frac{13}{74}$, $17.6\%$, $$p \leq 0.012$$), urine samples ($\frac{1}{74}$, $1.4\%$, $$p \leq 0.003$$) and general surgery ward ($\frac{36}{74}$, $48.6\%$, $p \leq 0.001$); blaOXA-48/blaVIM with urine samples ($\frac{9}{20}$, $45.0\%$, $p \leq 0.001$) and nephrology ward ($\frac{7}{74}$, $35.0\%$, $p \leq 0.001$). The results are given in Table 5. ESBL-producer strains were $89.9\%$ ($\frac{821}{913}$) of the samples, and ESBL resistance genes were found in $92.4\%$ ($\frac{759}{821}$) of isolates. The prevalence of detected ESBL resistance genes was as follows: blaSHV $53.3\%$ ($\frac{405}{759}$), blaCTX-M $61.8\%$ ($\frac{469}{759}$), blaTEM $39.1\%$ ($\frac{297}{759}$), blaSHV/blaCTX-M $46.7\%$ ($\frac{355}{759}$), blaSHV/blaTEM $22.3\%$ ($\frac{169}{759}$), blaCTX-M/blaTEM $21.3\%$ ($\frac{162}{759}$) and blaSHV/blaCTX-M/blaTEM $18.6\%$ ($\frac{141}{759}$). ## 2.4. Genetic Diversity Analysis Further, the genetic diversity of K. pneumoniae strains harboring blaNDM-1 ($$n = 83$$), blaKPC-2 ($$n = 36$$) and blaNDM-1/blaOXA-48 ($$n = 37$$) and E. coli strains harboring blaNDM-1 ($$n = 68$$) and blaNDM-1/blaOXA-48 ($$n = 13$$) were accessed in terms of clonal lineage and plasmid content. The sequence types identified among K. pneumoniae were ST147 ($39.7\%$, $\frac{62}{156}$), ST258 ($10.9\%$, $\frac{17}{156}$), ST11 ($23.0\%$, $\frac{36}{156}$), ST14 ($15.4\%$, $\frac{24}{156}$) and ST340 ($9.6\%$, $\frac{15}{156}$), and among E. coli were ST131 ($21.2\%$, $\frac{18}{81}$), ST405 ($34.5\%$, $\frac{28}{81}$), ST101 ($19.7\%$, $\frac{16}{81}$), ST69 ($7.4\%$, $\frac{6}{81}$) and ST10 ($16.0\%$, $\frac{13}{81}$). Plasmid replicon types IncFII, IncA/C, IncN, IncL/M, IncFIIA and IncFIIK were observed. The detailed results are given in Table 6. It was observed that different carbapenemases were present on different sequence types, depicting the adaptability of sequence types towards carbapenemases such as blaKPC-2-ST147 ($\frac{16}{156}$, $10.2\%$), blaNDM-1-ST147 ($\frac{35}{156}$, $22.4\%$), blaNDM-1/blaOXA-48-ST147 ($\frac{17}{156}$, $11.1\%$), blaKPC-2-ST258 ($\frac{10}{156}$, $6.4\%$), blaNDM-1/blaOXA-48-ST258 ($\frac{7}{156}$, $4.5\%$), blaNDM-1-ST340 ($\frac{8}{156}$, $5.1\%$), blaKPC-2-ST11 ($\frac{10}{156}$, $6.4\%$), blaNDM-1-ST11 ($\frac{22}{156}$, $14.1\%$), blaNDM-1/blaOXA-48-ST11 ($\frac{13}{156}$, $8.3\%$) and blaNDM-1-ST14 ($\frac{18}{156}$, $11.5\%$) among K. pneumoniae strains, and blaNDM-1-ST405 ($\frac{22}{81}$, $27.2\%$), blaNDM-1/blaOXA-48-ST405 ($\frac{8}{81}$, $9.8\%$), blaNDM-1-ST131 ($\frac{9}{81}$, $11.1\%$), blaNDM-1/blaOXA-48-ST131 ($\frac{1}{81}$, $1.2\%$), blaNDM-1-ST101 ($\frac{16}{81}$, $19.7\%$), blaNDM-1/blaOXA-48-ST101 ($\frac{2}{81}$, $2.5\%$), blaNDM-1-ST69 ($\frac{10}{81}$, $12.3\%$), blaNDM-1/blaOXA-48-ST69 ($\frac{1}{81}$, $1.2\%$), blaNDM-1-ST10 ($\frac{11}{81}$, $13.6\%$) and blaNDM-1/blaOXA-48-ST10 ($\frac{1}{81}$, $1.2\%$) among E. coli strains. ## 3. Discussion Carbapenem resistance is considered as one of the critical threats associated with hospital-acquired infections, especially in developing countries. Therefore, timely surveillance efforts are required to reduce the spread of CRE [42]. The current study was designed to characterize the key determinants for resistance spread in a tertiary care hospital. Our results showed that the patients were infected mostly with K. pneumoniae and E. coli strains while infrequently detected genera were Pseudomonas, Proteus, Acinetobacter, Citrobacter, Morganella, Providencia and Burkholderia. Previous studies from Pakistan showed that K. pneumoniae and E. coli were the most commonly detected pathogens responsible for nosocomial infections [43,44,45]. While the global data suggested the higher prevalence of K. pneumoniae, P. aeruginosa, E. coli and A. baumannii from Tanzania, Algeria, Nepal and Saudi Arabia [46,47,48,49], our study demonstrated $42.1\%$ CR among Enterobacterales. This is higher than prevalences previously reported from Pakistan such as $21.84\%$ [50], $25.5\%$ [51], $9.6\%$ [52] and $6.5\%$ [45]. However, our results are in agreement with the global reports that CRE prevalence is at alarming rates, such as $65.0\%$ from the USA [53], $42.6\%$ from Cuba [54] and $34.3\%$ from China [55]. We observed that K. pneumoniae is the leading CRE pathogen, accounting for $33.8\%$ of the total CR load, followed by E. coli ($24.4\%$) and Pseudomonas spp. ( $18.5\%$). E. coli and K. pneumoniae were considered the prime reason for CRE as evidenced by a number of other studies; E. coli (86.0–$38.24\%$) [17,45,51,56] and K. pneumoniae (60.0–$31.62\%$) [15,45,56,57]). We observed that CR is significantly associated with K. pneumoniae ($$p \leq 0.008$$) and Proteus spp. ( $$p \leq 0.043$$). Similarly, another study reported significant relation of CR with K. pneumoniae ($\frac{800}{1499}$, $53.0\%$, $$p \leq 0.0008$$) [58]. The National AMR action Plan for Pakistan 2017–2018 suggested a rate of $30\%$ CR in K. pneumoniae, while much lower CR rates were reported in P. aeruginosa isolates ($6.5\%$) [8]. However, another study showed higher CR rates among Pseudomonas spp. ( $34.0\%$) but lower rates among E. coli ($7.0\%$) and Klebsiella spp. ( $8.0\%$) [52]. These reports together with our data suggested that the CR trend among Pseudomonas spp. is changing with time and a notable CR increase was observed from Pakistan, as can be seen in the studies showing $24.2\%$ in 2012 [59], $49.5\%$ imipenem resistance in 2015 [60], $81.6\%$ in 2019 [61], $43.2\%$ in 2020 [62] and $66.4\%$ meropenem resistance in 2022 [63]. In contrast to our results of Acinetobacter spp. ( $7.3\%$), data from Pakistan suggested a sharp increase of CR among Acinetobacter spp. from $50\%$ in 2011 to $95.5\%$ in 2015 [64,65,66], $61.89\%$ imipenem resistant Acinetobacter spp. in 2018 and $84.0\%$ in 2022 [56,67]. High CR rates among Acinetobacter spp. and Pseudomonas spp. are alarming in Pakistan as these species exhibit intrinsic resistance to many antibiotics, leaving few therapeutic choices available. Our results strengthen the WHO recommendations for both species as critical pathogens [30,68]. On the other hand, CR among other species observed in our study suggested lower resistance rates, including Proteus spp. ( $6.9\%$), Citrobacter spp. ( $4.9\%$), Providencia spp. ( $2.1\%$), Morganella spp. ( $1.6\%$) and Burkholderia spp. ( $0.5\%$). Our results are in accordance with other studies with results such as Proteus spp. ( $3.0\%$), Citrobacter spp. ( $1.0\%$) [52], Morganella morganii (M. morganii) ($1.5\%$), Proteus mirabilis ($6.5\%$), Citrobacter freundii (C. freundii) ($4.5\%$) [69], Morganella spp. ( $0.5\%$) [70] and C. freundii ($41.6\%$) and M. morganii ($3.0\%$) [71,72]. However, no report is available about CR among Providencia spp. and Burkholderia spp. from Pakistan as per our knowledge. The trend of CR in our study population suggests that infections are mostly treated empirically by using broad-spectrum antimicrobials without proper testing in developing countries, thereby promoting resistant phenotypes. Global reports demonstrated variation in the dissemination of CRE such as $52.0\%$ CRE from Vietnam with K. pneumoniae ($69.0\%$) and E. coli ($59.0\%$) as prevalent species [73], $12.4\%$ CRE from Indonesia [74], $2.9\%$ CRE from Korea [75], $77.8\%$ CRE from India [76], $54.1\%$ CRE from Egypt with CR K. pneumoniae ($53.7\%$) and E. coli ($27.1\%$) [77], $22.0\%$ CRE from Nigeria with CR K. pneumoniae ($35.9\%$), P. aeruginosa ($30.8\%$) [78]. While a European cohort study reported $55.0\%$ ($\frac{944}{1717}$) CRE [79]. Surveillance data by ECDC on AMR showed that CR has increased in Greece with $64.7\%$ presence in K. pneumoniae and $63.9\%$ E. coli [80]. Interestingly, identification of CRE from Japan is still scarce with $0.5\%$ meropenem resistance in K. pneumoniae [24]. Similarly, much lower CR among E. coli ($0.02\%$) and K. pneumoniae ($0.18\%$) reported from Netherlands [81]. On the other hand, variable range of imipenem resistance in P. aeruginosa was observed worldwide, including in China ($33.2\%$), India ($29.6\%$), Japan ($8.0\%$), Italy ($28.5\%$), Turkey ($43.3\%$), Ukraine ($54.7\%$), United States ($21.4\%$) and Kuwait ($44.7\%$) [82]. From Romania, $6.25\%$ Proteus spp. and $45.79\%$ Providencia spp. were carbapenem resistant [83]. In our study, wound ($32.0\%$) and pus ($22.6\%$) were the predominant specimens for CRE isolation, while clinical wards with higher proportions of CRE were general surgery ($28.7\%$), general medicine ($19.7\%$) and ICU ($17.2\%$). However, tracheal aspirate ($25.0\%$), urine ($24.26\%$), pus ($25.53\%$), and surgical units ($51.4\%$), ICU ($65.3\%$), medical units ($43.5\%$), pediatric wards ($71.4\%$) were the previously reported causes of CRE infections in Pakistan [45,52]. Worldwide reports established that ICU-related colonization of CRE is higher, with results such as $86.15\%$, $35.5\%$, $31.0\%$, $24.0\%$ and $12.3\%$, thus favoring the resistance selection process [54,83,84,85,86]. The Greek System for the Surveillance of Antimicrobial Resistance reported that CR increased from <$1\%$ in 2001 to $42\%$ in medical wards and to $72\%$ in ICUs among K. pneumoniae isolates [25]. Furthermore, respiratory-, surgical- and urinary-associated healthcare CRE infections increased from $5\%$ to $25\%$ in developed countries [87,88]. The most frequent source of CRE infection included urinary tract ($36.2\%$), followed by blood ($26.3\%$) and surgical wound ($17.1\%$) [54,84]. Our study described the significant association of wound ($$p \leq 0.00001$$), urine ($$p \leq 0.01$$), tissue ($$p \leq 0.00001$$) and tip cell samples ($$p \leq 0.037$$) with CR, while general medicine units ($$p \leq 0.0008$$) and oncology wards ($$p \leq 0.006$$) remained statistically significant in relation to CR spread. Another study from Pakistan reported association of wound infections with Acinetobacter spp. ( OR = 1.79) and Pseudomonas spp. ( OR = 1.29) [56]. Urine was found to be the most common origin of CRE from the USA ($p \leq 0.0001$) [58] and Egypt ($$p \leq 0.035$$) [20]. Therefore, the current investigation highlights the constant requirement of containment plans in healthcare departments associated with CR to prevent and slow the process of its expansion. Enzyme-mediated CR accounts for 20–$70\%$ of the total AMR burden among Enterobacterales thereby highlighting carbapenemase production as the most common mode of resistance [89]. A total of $86.4\%$ carbapenemase-producing Enterobacterales (CPE) were identified in present study with $35.9\%$ K. pneumoniae, $25.2\%$ E. coli and $18.4\%$ Pseudomonas spp. as main producer species. Other reports from Pakistan supplement our findings that K. pneumoniae and E. coli were major contributors of the total carbapenemase production among Enterobacterales [15,17,40,44,57,90,91]. Another study observed a high proportion of carbapenemase production among Citrobacter spp. ( $66\%$), Acinetobacter spp. ( $53\%$), Pseudomonas spp. ( $51\%$) and Proteus spp. ( $20\%$) [52]. In contrast, our results indicated lower rates among Proteus spp. ( $6.7\%$), Acinetobacter spp. ( $6.2\%$) and Citrobacter spp. ( $3.9\%$). However, we observed carbapenemase production among Morganella spp. ( $1.5\%$), Providencia spp. ( $1.6\%$) and Burkholderia spp. ( $0.5\%$) for the first time from Pakistan. The key contributing carbapenemases involved in the expansion of CPE in the study population are blaNDM-1 and blaOXA-48, confirming the existing data from Pakistan [14,15,17,40,45,57,90,91,92]. We observed a considerable increase in the prevalence of KPC-producing K. pneumoniae ($15.4\%$). It is noteworthy that first KPC was detected from Pakistan in 2016; afterwards, few reports emerged since 2020 describing the 1.8–$17.6\%$ prevalence of blaKPC-2 [14,16,45,93]. Among Pseudomonas spp., we detected blaVIM ($8.3\%$) and blaOXA-48/blaVIM ($6.7\%$), while previously $2.3\%$–$42.3\%$ blaVIM prevalence was described [62,94,95]. We detected blaIMP more frequently in Pseudomonas spp. along with one report in Proteus spp. However, blaVIM and blaIMP were reported in Acinetobacter spp. previously from Pakistan [56,94,95,96]. Another important finding of our study was the emergence of blaNDM-1 ($$n = 2$$), blaOXA-48 ($$n = 3$$) and blaIMP ($$n = 1$$) in Morganella spp., while only report available from Pakistan recorded blaNDM-1 ($$n = 2$$) in M. morganii [69]. Furthermore, this is the first report that detected blaNDM-1 ($$n = 1$$) and blaOXA-48 ($$n = 2$$) in Burkholderia spp. and the coexistence of blaNDM-1/blaOXA-48 ($$n = 2$$) in Providencia spp. We observed a significant association of general surgery units with blaKPC-2 ($$p \leq 0.029$$), blaOXA-48 ($$p \leq 0.001$$) and blaNDM-1/blaOXA-48 ($p \leq 0.001$); ICU with blaNDM-1 ($p \leq 0.001$) and blaOXA-48 ($p \leq 0.001$); cardiology and pediatric medicine wards with blaNDM-1 ($$p \leq 0.042$$, $$p \leq 0.013$$) and blaOXA-48 ($$p \leq 0.008$$, $p \leq 0.001$); general medicine units with blaOXA-48 ($$p \leq 0.005$$); nephrology wards with blaOXA-48 ($$p \leq 0.020$$) and blaOXA-48/blaVIM ($p \leq 0.001$); wound samples with blaNDM-1 ($$p \leq 0.041$$) and blaNDM-1/blaOXA-48 ($$p \leq 0.002$$); urine samples with blaNDM-1/blaOXA-48 ($$p \leq 0.003$$) and blaOXA-48/blaVIM ($p \leq 0.001$); and blood samples with blaNDM-1/blaOXA-48 ($$p \leq 0.012$$). We could not find another association study from Pakistan. The main reason for the emergence of different STs globally is the ability of strains to disseminate carbapenemases through plasmids and their successful adaption to different healthcare environments. Our data revealed that successful high-risk clones of K. pneumoniae and E. coli have emerged in Pakistan, such as blaKPC-2-ST147, blaNDM-1-ST147, blaNDM-1/blaOXA-48-ST147, blaKPC-2-ST258, blaNDM-1/blaOXA-48-ST258, blaNDM-1-ST340, blaKPC-2-ST11, blaNDM-1-ST11, blaNDM-1/blaOXA-48-ST11 and blaNDM-1-ST14 among K. pneumoniae, and blaNDM-1-ST405, blaNDM-1/blaOXA-48-ST405, blaNDM-1-ST131, blaNDM-1/blaOXA-48-ST131, blaNDM-1-ST101, blaNDM-1/blaOXA-48-ST101, blaNDM-1-ST69, blaNDM-1/blaOXA-48-ST69, blaNDM-1-ST10 and blaNDM-1/blaOXA-48-ST10 among E. coli. The previously described STs from Pakistan include blaKPC-2-ST258, blaNDM-1-ST147, blaNDM-1/blaOXA-48-ST147, blaNDM-1-ST11, blaNDM-1/blaOXA-48-ST405, blaNDM-7/blaOXA-48-ST405, blaNDM-1-ST405, blaNDM-7/blaOXA-48-ST131, blaNDM-1/blaOXA-48-ST131, blaNDM-1-ST131, blaNDM-1-ST10, blaNDM-1/blaOXA-48-ST101, blaNDM-1-ST101, blaNDM-1/blaOXA-48-ST648, blaOXA-48-ST231 and blaNDM-1-ST859 [15,16,17,97]. Furthermore, we observed the emergence of blaKPC-2-ST147, blaNDM-1-ST340 and blaNDM-1-ST14 in K. pneumoniae and blaNDM-1-ST69 and blaNDM-1/blaOXA-48-ST69 in E. coli. ## 4. Conclusions In this study, we reported the detailed analysis of carbapenem resistance burden and the emergence of blaKPC-2-ST147, blaNDM-1-ST340 and blaNDM-1-ST14 in K. pneumoniae isolates, and blaNDM-1-ST69 and blaNDM-1/blaOXA-48-ST69 in E. coli isolates coharboring ESBLs from Pakistan. Moreover, we described blaNDM-1 ($$n = 1$$) and blaOXA-48 ($$n = 2$$) in Burkholderia spp. and the coexistence of blaNDM-1/blaOXA-48 ($$n = 2$$) in Providencia spp. for first time in the study population. Our data indicated that the lack of antimicrobial stewardship and misuse augmented by diagnostic difficulties in developing countries are accelerating the evolution and spread of high-risk STs and hyper-efficient plasmids. This situation is miserable, especially in healthcare settings with immense antimicrobial selection pressure, thereby highlighting the expansion of high-risk clones as a resistance reservoir. ## 5. Methodology The clinical strains were collected between May 2019 and April 2022 from the routine diagnostic laboratory, Mayo hospital, Lahore, Pakistan. Mayo hospital is one of the largest hospitals in South East Asia with a 3000 bed capacity. The clinical isolates were processed as given in Figure 1. Clinical specimens were phenotypically characterized by analyzing colony morphology and Grams staining by culturing on MacConkey agar and cysteine lactose electrolyte-deficient media (Oxoid Ltd., Basingstoke, UK) for urine samples. Biochemical characterization was performed by API-20E and API-20NE (BioMerieux, Marcy-IEtoile, France). ## 5.1. Antimicrobial Susceptibility Testing Antimicrobial susceptibility testing was performed by standard Kirby–Bauer disc diffusion method using Mueller–Hinton agar (Oxoid, Ltd., Basingstoke, UK), according to the “Performance Standards for Antimicrobial Disk Susceptibility Tests; CLSI Supplement M100, 30th Edition”. The following antibiotic disks were used: imipenem (10 μg), meropenem (10 μg), cefazolin (30 μg), cefuroxime (30 μg), ceftazidime (30 μg), cefotaxime (30 μg), cefepime (30 μg), cefoxitin (30 μg), ceftaroline (30 μg), ampicillin (10 μg), amoxicillin-clavulanic acid ($\frac{20}{10}$ μg), aztreonam (30 μg), ciprofloxacin (5 μg), trimethoprim-sulfamethoxazole ($\frac{1.25}{23.75}$ μg), tigecycline (15 μg), fosfomycin (50 μg), polymyxin-B (300 U), doxycycline (30 μg), amikacin (10 μg), piperacillin-tazobactam ($\frac{100}{10}$ μg), ampicillin-sulbactam (20 μg) (Oxoid, Ltd., Basingstoke, UK). For polymyxin B, the standard broth microdilution method was used as per CLSI recommendation (MIC breakpoints; intermediate ≤ 2, resistant ≥ 4). For tigecycline, EUCAST breakpoints were used [98]. Quality control strains were E. coli ATCC 25922 and P. aeruginosa ATCC 27853. Carbapenemase-producing strains were identified by using the modified carbapenem inactivation method (mCIM) [99]. Briefly, 1 or 2 colonies of bacterial growth were mixed with 2 mL of tryptone soy broth (TSB media; ThermoFischer Scientific, Waltham, MA, USA). Meropenem antibiotic disc was added into the bacterial suspension under sterile conditions and incubated at 35 ± 2 °C for 4 h. Meanwhile, a suspension of the mCIM indicator organism E. coli ATCC 25922 (carbapenem-sensitive strain) with turbidity equivalent to 0.5 McFarland standard was prepared and inoculated on a Mueller–Hinton agar (Oxoid, UK) plate. The meropenem antibiotic disc from cultured TSB bacterial suspension was transferred to inoculate the MHA plate with indicator strain. Plates were dried for 3–10 min before adding the meropenem antibiotic disc. K. pneumoniae ATCC BAA-1705 strain was used as quality control strain. The plate was incubated for 18 to 24 h at 35 ± 2 °C. ESBL producer strains were identified by CHROMagarTM ESBL media (CHROMagar, Paris, France). ## 5.2. Antimicrobial Resistance Gene Analysis The heat lysis method was used for genomic DNA extraction [100]. In short, 2 to 3 bacterial colonies were mixed with 500 μL sterile dH2O in 1.5 mL microcentrifuge tube. The sample was incubated at 98 °C for 10 min/300 rpm in thermomixer (FischerScientific, Waltham, MA, USA). Sample was centrifuged at 1000 rpm for 10 min and supernatant containing DNA was collected in a new tube. DNA was stored at −80 °C until further processing. Carbapenemase resistance genes (blaKPC-2, blaNDM-1, blaVIM, blaIMP, blaOXA-48) and selected ESBLs (blaSHV, blaTEM and blaCTX-M) were detected by standard PCR. The PCR reaction mixture contained 25 μL of 2 × PCR Master Mix (catalogue # K0171, Thermoscientific, Waltham, MA, USA), 10 μM of each primer, 0.5 ng of DNA and dH2O up to 50 μL in a thermal cycler (Proflex, ABI, Haines City, FL, USA). Amplicons were resolved by agarose gel electrophoresis (1–$1.5\%$). The primer sequences and PCR cycling conditions are given in Table S1. ## 5.3. Allele Identification by Sequencing Sanger’s sequencing method was used for the blaNDM and blaKPC allele identification. BigDye terminator v3.1 kit was used for cycle sequencing as per kit instructions. Briefly, 10 μL PCR reaction mixture contained BigDye terminator 3.1 Ready Reaction Mix 4 μL, forward primer (3.2 pmol) 0.5 μL, purified DNA template (5–20 ng) 2 μL and dH2O 3.5 μL. PCR cycling conditions were 96 °C 1 min, 96 °C 10 s, 50 °C 5 s, 60 °C 2 min (35 cycles). PCR product was purified by using BigDye XTerminator purification kit as per kit instructions and capillary electrophoresis was performed by Genetic Analyzer (ABI-3500, Thermo Fischer, Waltham, MA, USA). Sequencing analysis software v6.1 and basic local alignment tool (BLAST, NCBI) were used for data analysis and interpretation. CLC Sequence Viewer 7 was used for sequence alignment and mutation analysis. ## 5.4. Determination of Genetic Diversity by Multilocus Sequence Typing and Plasmid Replicon Typing K. pneumoniae and E. coli strains harboring blaNDM-1, blaKPC and blaNDM-1/blaOXA-48 were further subjected to multilocus sequence typing (MLST) analysis. For K. pneumoniae, seven housekeeping genes were used [101]: glyceraldehyde-3-phosphate dehydrogenase A gene (gapA), translation initiation factor IF-2 gene (infB), malate dehydrogenase gene (mdh), phosphoglucose isomerase gene (pgi), phosphoporin E gene (phoE), periplasmic energy transducer gene (tonB), beta-subunit of RNA polymerase gene (rpoB). For E. coli, eight housekeeping genes were used: DNA polymerase (dinB), isocitrate dehydrogenase (icdA), p-aminobenzoate synthase (pabB), polymerase PolII (polB), proline permease (putP), tryptophan synthase subunit A (trpA), tryptophan synthase subunit B (trpB) and beta-glucuronidase (uidA) [102]. Sequencing analyses were performed as described above by using primer sequences given in Table S1. 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--- title: Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis authors: - Gerard Muntané - Javier Vázquez-Bourgon - Ester Sada - Lourdes Martorell - Sergi Papiol - Elena Bosch - Arcadi Navarro - Benedicto Crespo-Facorro - Elisabet Vilella journal: European Psychiatry year: 2023 pmcid: PMC10044301 doi: 10.1192/j.eurpsy.2023.9 license: CC BY 4.0 --- # Polygenic risk scores enhance prediction of body mass index increase in individuals with a first episode of psychosis ## Abstract ### Background Individuals with a first episode of psychosis (FEP) show rapid weight gain during the first months of treatment, which is associated with a reduction in general physical health. Although genetics is assumed to be a significant contributor to weight gain, its exact role is unknown. ### Methods We assembled a population-based FEP cohort of 381 individuals that was split into a Training ($$n = 224$$) set and a Validation ($$n = 157$$) set to calculate the polygenic risk score (PRS) in a two-step process. In parallel, we obtained reference genome-wide association studies for body mass index (BMI) and schizophrenia (SCZ) to examine the pleiotropic landscape between the two traits. BMI PRSs were added to linear models that included sociodemographic and clinical variables to predict BMI increase (∆BMI) in the Validation set. ### Results The results confirmed considerable shared genetic susceptibility for the two traits involving 449 near-independent genomic loci. The inclusion of BMI PRSs significantly improved the prediction of ∆BMI at 12 months after the onset of antipsychotic treatment by $49.4\%$ compared to a clinical model. In addition, we demonstrated that the PRS containing pleiotropic information between BMI and SCZ predicted ∆BMI better at 3 ($12.2\%$) and 12 months ($53.2\%$). ### Conclusions We prove for the first time that genetic factors play a key role in determining ∆BMI during the FEP. This finding has important clinical implications for the early identification of individuals most vulnerable to weight gain and highlights the importance of examining genetic pleiotropy in the context of medically important comorbidities for predicting future outcomes. ## Introduction Schizophrenia (SCZ) is a severe disorder with a large burden of morbidity and societal impact. It has a heritability of ~$80\%$, much of which is attributable to common risk alleles [1]. Patients with SCZ often have a large number of comorbid medical conditions during their lifespan [2, 3]. In fact, people with SCZ have a higher mortality rate than the general population, corresponding to a 10–20 year reduction in life expectancy, predominantly due to cardiovascular disease [4, 5]. Antipsychotic (AP) drugs can help reduce the intensity and frequency of psychotic symptoms; however, most of them are obesogenic [6]. There are differences in the weight gain caused by AP drugs, with olanzapine and clozapine presenting a higher risk [7]. Obesity is a modifiable risk factor that reduces the quality of life [8], adherence to treatment [9], and is associated with many adverse health-related outcomes, including cardiovascular disease [10, 11]. The risk of obesity in patients with SCZ is more than four times higher than in the general population [12]. In fact, the first year after the initiation of AP treatment is a critical period in which up to 60–$80\%$ of the total weight gain occurs [13, 14]. Among all factors studied, rapid initial weight gain, AP drug, pretreatment body mass index (BMI), and sex are the best predictors of weight gain and associated metabolic abnormalities [15–17]. However, substantial differences in susceptibility between individuals under AP treatment suggest that weight gain may be partially explained by a mixture of environmental effects and genetic background [18–20]. In support of this view, the heritability of weight gain in monozygotic twins with SCZ has been estimated to be 0.6–0.8 [21] and a few genetic loci have been associated with AP-induced weight gain by genome-wide association studies (GWAS) [22, 23]. Moreover, given its strong polygenic component, SCZ shows extensive genetic overlap with other mental disorders [24, 25], and also with other nonpsychiatric traits [26–28]. In particular, studies have reported a negative overall genetic correlation between BMI and SCZ, which results from a mixture of variants with concordant and discordant effects [28–30]. There is a substantial genetic vulnerability to BMI trajectories in the general population [31, 32]; however, it remains to be established whether prediction models, including the polygenic risk score (PRS) for BMI (PRSBMI), are clinically useful for populations with psychiatric disorders. In this study, we aimed to determine whether common genetic variants for BMI confer a risk of BMI increase at treatment initiation in patients with first episode of psychosis (FEP), and to investigate the role of shared and private BMI variants in BMI increase (∆BMI). To our knowledge, this is the first study to confirm the role of genetics in FEP-associated weight gain, with a significant contribution of shared variants between the two traits, highlighting their relevance and applicability. This information can be used to identify individuals with an increased risk at the very early stages of the disease. ## Study design This study includes individuals with a FEP initially enrolled in the Cantabria Program for Early Intervention in Psychosis (PAFIP, Spain) between 2001 and 2018 [33]. Patients fulfilling inclusion criteria (Supplementary Material) were assigned to three consecutive phases of the PAFIP (PAFIP I, II, and III), including randomized, flexible-dose, and open-label clinical trials and followed up for 12 months. During this period, AP doses were adjusted at the treating psychiatrist’s discretion to target the lowest effective dose [34]. Similarly, AP treatment could be switched based on the observed effectiveness and the patient’s tolerance. For further analysis, the diagnosis was categorized into Schizophrenia, Schizoaffective disorder, Schizophreniform disorder, Brief psychotic disorder, and not otherwise specified psychosis. Written informed consent was obtained from all subjects. The Clinical Research Ethics Committee of Cantabria approved the research protocol. Patients were evaluated for research purposes at three consecutive time points: at baseline, and at 3- and 12-month follow-ups (after the initiation of AP treatment). Sociodemographic characteristics were recorded at baseline (Table 1), while clinical and anthropometric measures were obtained at each time point. Pharmacological treatment prescribed and chlorpromazine equivalents [35] were also recorded at each time point. Patients were grouped based on the primary active drug (Table 2).Table 1.Sociodemographic and clinical characteristics of the sample at baseline. Total ($$n = 381$$) Training set ($$n = 224$$) Validation set ($$n = 157$$)Statistical difference N (%) N (%) N (%)Pearson Chi-squared testSex (females)165 (43.3)98 (43.8)67 (42.7)$$p \leq 0.91$$* Diabetes7 (4.2)7 (4.9)0p = 0.65* Metabolic syndrome14 (4.7)11 (5.7)3 (2.9)$$p \leq 0.42$$* Drug Naïve354 (92.9)202 (90.2)152 (96.8)$$p \leq 0.02$$* Tobacco211 (55.4)121 [54]90 (57.3)$$p \leq 0.59$$* Cannabis153 (40.2)85 (37.9)68 (43.3)$$p \leq 0.34$$* Alcohol192 (50.5)101 (45.1)91 (58.3)$$p \leq 0.01$$* Antidepressants7 (1.8)6 (2.7)1 (0.6)$$p \leq 0.28$$* Stabilizers1 (0.3)1 (0.4)0p = 1* Diagnose after 6-months Schizophrenia188 (49.3)96 (42.9)92 (58.6)$$p \leq 0.02$$Schizophreniform disorder115 (30.2)73 (32.6)42 (26.7)Brief psychotic disorder43 (11.3)32 (14.3)11 [7]Unspecified psychosis28 (7.3)19 (8.5)9 (5.7)Schizoaffective disorder5 (1.3)2 (0.9)3 [2]Delusional disorder2 (0.5)2 (0.9)0Mean ± SDMean ± SDMean ± SDWilcoxon rank sum testAge at admission, years29.5 ± 9.330.7 ± 10.227.8 ± 7.6p = 0.03Weeks previous treatment1.5 ± 1.61.4 ± 1.61.8 ± 1.7p = 0.44DUI, months21.9 ± 37.216.7 ± 31.429 ± 43.1p = 1.6e-04DUP, months13.3 ± 29.412.5 ± 3114.4 ± 27p = 0.04HOMA baseline2.2 ± 2.32.5 ± 2.71.7 ± 1.4p = 1e-03Weight66.2 ± 13.665.6 ± 1467.1 ± 13.1p = 0.12BMI23.2 ± 3.823 ± 423.4 ± 3.5p = 0.12∆BMI36.4 ± 7.66 ± 7.37 ± 8p = 0.21∆BMI1213.9 ± 12.113.6 ± 11.914.3 ± 12.4p = 0.59Abbreviations: DUI, duration of untreated illness; DUP, duration of untreated psychosis.Pearson’s Chi-squared test with Yates’ continuity correction. Table 2.Number of participants in each treatment category at baseline and after 3 and 12 months of follow-up. Baseline $t = 3$ months $t = 12$ monthsDrugTrainingValidationTrainingValidationTrainingValidationAripiprazole101 ($45.1\%$)11 ($7\%$)81 ($38\%$)9 ($5.8\%$)81 ($38.6\%$)9 ($6.3\%$)Clozapine——3 ($1.4\%$)—10 ($4.8\%$)2 ($1.4\%$)Trifluoperazine——1 ($0.5\%$)———Haloperidol—34 ($21.7\%$)—31 ($20.1\%$)—17 ($11.9\%$)Olanzapine1 ($0.4\%$)42 ($26.8\%$)13 ($6.1\%$)45 ($29.2\%$))24 ($11.4\%$)46 ($32.2\%$)Paliperidone——3 ($1.4\%$)—12 ($5.7\%$)—Quetiapine34 ($21.7\%$)15 ($9.6\%$)14 ($6.6\%$)12 ($7.8\%$)13 ($6.2\%$)18 ($12.6\%$)Risperidone49 ($21.9\%$)43 ($27.4\%$)71 ($33.3\%$)49 ($31.8\%$)54 ($25.7\%$)44 ($30.8\%$)Ziprasidone39 ($24.8\%$)12 ($7.6\%$)27 ($12.7\%$)8 ($5.2\%$)15 ($7.1\%$)7 ($4.9\%$)Amisulpride————1 ($0.5\%$)—Total224157213154210143 ## Samples and genotyping For the present study, the PAFIP sample ($$n = 381$$) was divided into two independent patient datasets. Each of these datasets had been genotyped with a different platform. The first dataset (from now on Training set) included 224 patients and was used to determine the p-value threshold for PRSs, while a second sample (from now on Validation set) included 157 patients and was used for replication. In both datasets, DNA was extracted from peripheral lymphocytes, and genotyping was performed using the Affymetrix 6.0 platform (Training set) and the Illumina Infinium PsychArray (Validation set), respectively. Standard quality-control procedures [36] were performed with PLINK 1.9 [37], resulting in 6,910,431 SNPs in the Training set and 6,552,380 SNPs in the Validation set (Supplementary Material). Only those individuals with valid BMI measures were included (Supplementary Table S1). Finally, a PCA was performed on the resulting individuals and the top 10 PCs were kept for further analysis. ## Pleiotropy analyses with GWAS data GWAS summary statistics on BMI were obtained from Pulit et al. [ 38], which comprised association analyses of a total of 806,834 European individuals. For SCZ, we obtained GWAS data on 67,390 patients and 94,015 controls, with $80\%$ being of European ancestry [1] (Supplementary Table S2). GWAS summary statistics were referenced to 9,546,816 SNPs generated from the 1,000 Genomes Project (1KGP). SNPs that were nonbiallelic, without rsIDs, duplicated, or with strand-ambiguous alleles were removed. We also filtered out SNPs with INFO scores < 0.9, those mapping to the extended major histocompatibility complex (MHC, chr6: 25,119,106–33,854,733), SNPs located on chromosomes X, Y, and mitochondria, and SNPs with sample sizes 5 standard deviations away from the mean. Finally, a common set of 1,949,409 SNPs were kept in the two datasets. ORs and betas were transformed into z-scores. We used pleioFDR [25] to identify genetic loci jointly associated with the two phenotypes, as previously described [27] (Supplementary Material). SNPs jointly associated with BMI and SCZ (conj. FDR < 0.05) were mapped to genes with ANNOVAR [39]. Genelists were submitted to KOBAS-I [40] to check for enrichment in biological categories and diseases; and to the GENE2FUNC option implemented in FUMA to check for tissue enrichment [41]. All protein-coding genes were used as background and the Benjamini–Hochberg (BH) method was used for false discovery rate (FDR) correction. To obtain the widest representation of the SNPs in the pleiotropic loci between SCZ and BMI, SNPs that were in r 2 > 0.1, distance < 250 kb, and MAF > 0.001 with each independent SNPs at conj. FDR < 0.05 were recovered with PLINK v1.9 from the CEU population of the 1 kG project. After this process, all the obtained SNPs were classified as pleiotropic SNPs. Nonpleiotropic SNPs were defined as all the SNPs in the original BMI GWAS that were not considered in any pleiotropic locus. ## PRS estimation PRSice 2.3.1.e was used to implement a pipeline in PRS creation [42], using a two-step procedure as previously developed [43]. This avoids sample overlap between the Training and Validation sets and prevents test statistic inflation. First, PRSs were calibrated in the Training set using the GWAS on BMI [38] as base data. Scores were calculated for multiple p-threshold cutoffs (from 5e-08 to 1 with increments of 5e-05) using r 2 = 0.1, 250-kb window, SNPs with INFO > 0.9, and excluding the MHC. Then, the optimal p-value (p optimal) threshold was determined as the one with the highest prediction for each phenotype. Sex, age, and the 10 first PCs were included as covariates (when evaluating ∆BMI, BMI0 was also included as a covariate). Second, SNPs below each obtained p optimal threshold were identified and carried forward for PRS computation in individuals of the Validation set. ## Statistical analyses First, Mann–Whitney and Kruskal–Wallis tests were used to assess differences in ∆BMI at 12 months (∆BMI12) based on clinical characteristics, use of antidepressants, and the AP drug described. Chi-squared tests were performed to evaluate differences between the Training and Validation sets. To elucidate whether the PRSBMI were associated with the BMI in our dataset we carried out multiple linear regressions in the Validation set. We designed our analysis in three phases, first, we constructed linear models with the observed BMI12, ∆BMI3, and ∆BMI12 as the dependent variables, which combined only clinical and demographic variables. These models were referred to as Clinical models. They included as independent variables the 10 first PCs (to adjust for population stratification), sex, age, AP dose and the type of AP drug (at the corresponding time point), the diagnosis, tobacco smoking, and cannabis use. When the ∆BMI were evaluated, BMI0 was also included as a covariable. We also performed Clinical models with the whole dataset to determine each variable’s contribution in the BMI. Second, to examine the predictive ability of including genetic factors in the Validation set, the PRSBMI values obtained were subsequently incorporated into the previous models. Thus, we produced a series of absolute risk models with the observed BMI (or ∆BMI) as the dependent variable, and the covariates in the Clinical model plus the PRSBMI as independent variables, named PRS models. Third, we divided the genome-wide significant SNPs ($p \leq 5$e-08) from the BMI GWAS into pleiotropic and nonpleiotropic, using information from the pleioFDR analysis, to build additional PRS models which independently included PRS derived from pleiotropic (PRSpleio) and nonpleiotropic (PRSnonpleio) loci. In these cases, only SNPs that belonged to each category were included. Finally, we used the ANOVA() function to compare whether PRS models were significantly different from the Clinical models. All the analyses were performed in R 3.6.0 [44]. Since all the BMI measures were highly interrelated, a Bonferroni correction was considered too restrictive for the linear regression models. Instead, the alpha level was corrected by estimating the effective number of tests [45]. In our study, the resulting significance threshold was set at $$p \leq 0.027.$$ ## Characteristics of the whole dataset A total of 381 patients were included in the entire dataset. Among them, 188 ($49.3\%$) were diagnosed with schizophrenia, 115 ($30.2\%$) with schizophreniform disorder, 43 ($11.3\%$) with brief psychotic disorder, 28 ($7.4\%$) with not otherwise specified psychosis, 5 ($1.3\%$) with schizoaffective disorder, and 2 ($0.5\%$) with delusional disorder (Table 1). At the end of the study, $89.2\%$ of the patients had all BMI measures. None of the participants was initially treated with clozapine, which is indicated in treatment-resistant SCZ [46] (Table 2). A total of 354 patients ($92.9\%$) were AP-naïve. The other 27 patients ($7.1\%$) had been treated with AP prior to their inclusion in the study, although during a short period of time (mean 10.4 days, SD: 11.2 days; median 5 days; range: 1–42 days). In the dataset, the mean ∆BMI3 and ∆BMI12 were 6.4 and $13.9\%$, respectively (Supplementary Figure S1). Most of the individuals displayed a positive ∆BMI12 ($$n = 312$$, $91.8\%$), while 25 individuals ($7.4\%$) showed a negative ∆BMI12, and 3 individuals (<$0.1\%$) had the same BMI0 and BMI12. In total, 108 patients in the Training set ($48.2\%$) and 94 in the Validation set ($59.9\%$) remained with the same AP drug during the study (Supplementary Figure S2). Both ∆BMI3 and ∆BMI12 were negatively correlated with BMI0 ($$p \leq 5.56$$e-10 and $$p \leq 7.07$$e-07) and positively correlated with each other (Supplementary Figure S3). In the whole sample, all APs were associated, on average, with a positive ∆BMI (Supplementary Figure S4). There were differences in ∆BMI12 between AP drugs among the individuals that did not switch AP drug during the study period ($$p \leq 1.9$$e-03), being olanzapine associated with a higher ∆BMI12. There were also differences in tobacco smokers versus nonsmokers ($$p \leq 0.01$$) and in cannabis users versus nonusers ($$p \leq 2.5$$e-03). However, ∆BMI12 was not associated with diabetes, metabolic syndrome, alcohol use, being AP drug naïve, or not having been prescribed antidepressants and mood stabilizers at baseline (Supplementary Figure S5). To reduce the number of variables in the models, only significant factors were incorporated into the subsequent prediction models. ## Pleiotropy between SCZ and BMI A total of 486 independent SNPs belonging to 449 near-independent genomic loci (r 2 < 0.1) were identified as being jointly associated with SCZ and BMI at conj. FDR < 0.05 (Figure 1A,B and Supplementary Table S3). Among them, 169 independent SNPs ($35\%$) showed concordant relationships, where the alleles that confer risk for SCZ, also increase BMI. In addition, 317 independent SNPs ($65\%$) showed discordant links, in which the allele that increased SCZ-risk, had a BMI reduction effect (Figure 1C). This represented an approximate twofold ratio between discordant (antagonistic) and concordant (agonistic) pleiotropic variants that were conserved across significance thresholds (Supplementary Figure S6). Neither of the two groups of variants differed in their effect sizes in SCZ ($$p \leq 0.45$$) or BMI ($$p \leq 0.17$$), and nor in their minor allele frequency ($$p \leq 0.72$$).Figure 1.Pleiotropic variants between SCZ and BMI. ( A) Manhattan plot showing independent (r 2 < 0.1) loci associated with both SCZ and BMI, as defined by conjunction false discovery rates (conj. FDR) after excluding SNPs in the MHC region. The dashed black line represents the conj. FDR threshold of 0.05. ( B) Conditional Q–Q plots of nominal versus empirical (−log10) p-values (corrected for inflation) of BMI as a function of significance with SCZ, at the level of $p \leq 10$−1 (red line), $p \leq 10$−2 (yellow line), and $p \leq 10$−3 (purple line), respectively. The blue line indicates the standard enrichment of BMI including all SNPs, irrespective of their association with the secondary trait. The gray dashed line indicates the null distribution of p-values. ( C) Pleiotropy plot for independent SNPs with conj. FDR < 0.05 ($$n = 486$$) between SCZ and BMI. The conj. FDR values and the direction of the effects (z-scores) of the minor alleles are plotted for BMI (x-axis) against SCZ (y-axis). Graph regions whose effects are consistent with a positive correlation between the two traits are shaded in yellow. ( C) The ratio between discordant and concordant pleiotropic variants across different conj. FDR thresholds. The set of SNPs shared between SCZ and BMI included 9,262 SNPs corresponding to 889 genes in total, enriched in Alcoholism (FDR = 5,15e-15), DNA methylation (FDR = 4,99e-14), DNA damage (FDR = 7,52e-13), cAMP signaling pathway (FDR = 2,45e-09), axon guidance (FDR = 1,06e-07), dopaminergic synapse (FDR = 2,99e-05), insulin resistance (FDR = 6,57e-03), and nervous system diseases (FDR = 7,58e-05), among others (Supplementary Table S4 and Supplementary Figure S7). These* genes were differentially regulated in the brain; and were especially up-regulated in the frontal cortex, anterior cingulate cortex, putamen, amygdala, nucleus accumbens, and hippocampus (Supplementary Figure S8). A complete list of pathways enriched in both agonistic and antagonistic loci can be found in Supplementary Tables S5 and S6. ## Risk models for BMI and ∆BMI First, the Clinical models for BMI12 and ∆BMI12 were evaluated in the entire dataset. Variables associated with a higher BMI12 were male sex ($$p \leq 7.4$$e-05), being on haloperidol ($$p \leq 1.6$$e-04), olanzapine ($$p \leq 6.1$$e-03), risperidone ($$p \leq 6.8$$e-03), and quetiapine treatment ($$p \leq 0.03$$), and not consuming cannabis ($$p \leq 0.01$$, Supplementary Table S7). Among them, the major contributors to BMI12 were sex ($4.5\%$), AP treatment ($9.1\%$) and not being a cannabis user ($1.7\%$, Figure 2A). On the other hand, variables associated with higher ∆BMI12 were reduced age ($$p \leq 0.01$$), low BMI0 ($$p \leq 2.4$$e-04), and AP treatment with paliperidone ($$p \leq 8.2$$e-03) and clozapine ($$p \leq 8.8$$e-03) at the 3-month follow-up (Supplementary Table S7). BMI0 contributed $3.8\%$ to the total variance of ∆BMI12, AP treatment $8\%$, and age $1.8\%$ (Figure 2B). When ∆BMI3 was included, it was the major contributor, accounting for $27.1\%$ of ∆BMI12 variance (Figure 2C).Figure 2.∆BMI variance explained in the whole dataset. Bar plots showing the variance explained by each covariate in the Clinical models for (A) BMI12, (B) ∆BMI12, and (C) ∆BMI12 including ∆BMI3 in the model. AP, antipsychotic drug; CPZ, equivalent doses of chlorpromazine. * p-value < 0.05; p-value < 0.01; p–value < 0.001. To assess the performance of adding PRS in predicting BMI, the whole dataset was split into a Training set and a Validation set. The p optimal was brought forward to calculate PRSBMI in the Validation set and the scores were included into the PRS models for comparison against the Clinical models. For BMI12 the p optimal was established at 0.15 (Supplementary Table S8). The inclusion of the PRSBMI improved the Clinical model for BMI12 by $76\%$ ($$p \leq 7.7$$e-04, Adj. R 2CLIN = 0.10, Adj. R 2PRS = 0.18, Figure 3 and Supplementary Table S9).Figure 3. Clinical versus PRS models in BMI. Barplots showing the Adj. R 2 in BMI by the Clinical model (CLIN) and the PRS models computed using all SNPs from BMI GWAS (PRSBMI), pleiotropic SNPs (PRSPleio), and nonpleiotropic SNPs (PRSNonpleio) in the Validation dataset ($$n = 157$$). The barplot shows predictions of BMI12, ΔBMI3, and ΔBMI12 in the x-axis. Covariates included in the Clinical model were the first 10 PC, age, sex, AP drug prescribed, chlorpromazine equivalent doses, diagnose, tobacco smoking, and cannabis use. Each PRS model was compared to the performance of the corresponding Clinical model. Asterisks represent significantly improved models compared to the Clinical models (ANOVA). p-value < 0.05; *p–value < 0.001. Next, we studied the inclusion of PRSBMI for predicting ∆BMI3 and ∆BMI12. The p optimal thresholds in the Training set were estimated at 2.2e-03 and 0.13, respectively (Supplementary Table S8). The percentage increase in the PRS models was $69.4\%$ in ∆BMI12 ($$p \leq 0.02$$, Adj. R 2CLIN = 0.05; Adj. R 2PRS = 0.08), but not significant in ∆BMI3 ($$p \leq 0.39$$, Adj. R 2CLIN = 0.18; Adj. R 2PRS = 0.18, Supplementary Table S10). Given the large extent of genetic pleiotropy between SCZ and BMI, we investigated whether these variants played a specific role in determining BMI and ∆BMI in our cohort. We obtained two PRSs based on pleiotropic (PRSpleio) and nonpleiotropic SNPs (PRSnonpleio, Supplementary Figure S9). The PRSpleio was not a significant predictor in determining absolute BMI12 ($$p \leq 0.26$$), as confirmed by the comparison of the PRS model including the PRSpleio (Adj. R 2Pleio = 0.11) compared to the Clinical model (Adj. R 2CLIN = 0.10, Figure 3). Instead, the PRSnonpleio showed an improvement in BMI12 higher than that of PRSBMI: $110.6\%$ (Adj. R 2Nonpleio = 0.22, $$p \leq 5.3$$e-05). In contrast, including PRSpleio improved the ∆BMI12 model by $98\%$ ($$p \leq 0.01$$, Adj. R 2CLIN = 0.05, Adj. R 2Pleio = 0.10, Figure 3), as well as the ∆BMI3 model by $12.2\%$ ($$p \leq 0.04$$, Adj. R 2CLIN = 0.18, Adj. R 2Pleio = 0.20), indicating the role of the pleiotropic variants in weight increase. PRSnonpleio was not relevant for predicting ∆BMI3 ($$p \leq 0.28$$); however, it significantly improved ∆BMI12 by $93.9\%$ ($$p \leq 0.01$$, Adj. R 2Nonpleio = 0.10). Finally, we classified the pleiotropic variants into agonistic and antagonistic to construct both agonistic PRSs (PRSago) and antagonistic PRSs (PRSantag). In predicting both ∆BMI3 and ∆BMI12, the PRSantag outperformed the PRSago (Supplementary Figure S10). ## Discussion Understanding the genetic vulnerability associated with increased BMI can be used to predict the risk of ∆BMI prior to treatment initiation so that personalized risk-based treatments can be implemented. In this study, we demonstrate for the first time, the replicable effects of a PRSBMI in predicting ∆BMI in FEP, with a prominent role of variants shared between SCZ and BMI. In our study, BMI0 was inversely associated with ∆BMI3 and ∆BMI12, indicating that individuals with lower BMI0 were more likely to have higher ∆BMI during the first year of treatment [47], which is in line with a previous study on an extended FEP cohort [48]. In the analyses of patients who did not switch AP, greater BMI gain was observed in patients treated with atypical AP, in accordance with previous studies [49, 50]. However, the effects varied greatly within medications, and interactions with underlying individual characteristics and genetic factors may be relevant [51]. Previous studies have proposed that the weight gain in psychosis is associated with the altered expression of genes related to both obesity and BMI [52], which suggests that there is a genetic overlap between these two medical conditions. Here, we confirm and extend with further data the shared genetic architecture between BMI and SCZ, involving pathways such as alcoholism (alcohol use disorder), DNA damage, DNA methylation, insulin resistance, and dopaminergic and glutamatergic synapses [28], which are promising mechanisms for understanding weight gain in FEP. Alcohol intake can be a contributing factor to weight gain, probably by effects on central neurotransmitter systems to increase appetite [53], however, it has to be highlighted that alcohol use disorder was among the exclusion criteria for entering the PAFIP program. Specific DNA methylation signatures have been widely described to play a role in obesity and weight loss in humans [54–56]. There is also evidence pointing to the relevance of the glutamatergic system as a promising strategy to treat obesity [57]. In addition, weight gain associated with clozapine may be linked to antagonism of the histaminergic H1 receptors, increasing the risk of insulin resistance and type 2 diabetes [58]. The pleiotropic variants between the two traits belong to genes that are up-regulated in the frontal and anterior cingulate cortices, the putamen, amygdala, nucleus accumbens, and hippocampus, which suggests that these areas play a relevant role in weight increase associated with SCZ. For instance, dopamine has been considered to be the target responsible for the efficacy of AP and also to be involved in feeding behavior, and the accumbens is considered to be the brain area with an increased release of dopamine [59]. In fact, nucleus accumbens microstructure can be used to predict weight increase in children [60]. Similarly, hippocampus size has been discovered to be a predictor for change in BMI in FEP [61]. A recent study in FEP found no association between psychopathological PRS with metabolic progression, including BMI [62]. However, in the present study, we demonstrate the role of underlying genetics in both BMI and ∆BMI in FEP patients. Including PRSBMI ostensibly improved the prediction of BMI at 12 months of treatment. This is not surprising, as the role of genetics in BMI in the general population is already known [31]; however, this is the first time it is also validated in FEP individuals under AP treatment. Notably, we report for the first time, that the inclusion of PRSBMI also improved the prediction of mid-term ∆BMI, which may have important consequences in identifying FEP patients at high risk for weight gain. Strikingly, including PRSpleio improved both risk models of ∆BMI12 and ∆BMI3, although containing a much lower number of SNPs. Remarkably, ∆BMI12 was strongly predicted by ∆BMI3 (explaining almost $30\%$ of its variance). This result emphasizes that clinicians should focus on the early weeks of treatment to prevent long-term weight gain [63], also reinforcing the benefits of including pleiotropy information for early detection of BMI increase in FEP. In contrast, the genetic architecture of BMI not shared with SCZ (PRSnopleio) played a pivotal role in controlling BMI. Our results are in line with recent data showing that incorporating pleiotropic information improves prediction by capturing biological mechanisms shared between traits [64, 65]. Moreover, opening the door to applying it to the study of comorbidity between a priori independent traits. Our results also indicate that antagonistic variants (those that have opposite directions between BMI and SCZ) play a greater role in ∆BMI. However, it could be simply a matter of statistical power as more SNPs were recovered in this category. This study has one main strength: it is based on independent longitudinal and prospective cohorts of well-characterized drug-naïve FEP patients in which different types of AP medication were considered. However, our work has some limitations; first, we could not control for well-known factors that contribute to BMI changes, such as diet and physical activity [66, 67]. Secondly, the interpretation of the results could also be skewed by the fact that FEP patients often switch AP treatment during follow-up, and are sometimes treated with a secondary AP, which have a possible impact on BMI [68]. It is noteworthy that the two datasets do not have the same distribution of AP treatments. Although this may imply a bias when the results between AP drugs are compared, it also reinforces the role of genetics in ∆BMI given its replicability in sets of individuals with different treatments. Also, the fact that GWAS of SCZ contained a $20\%$ non-European sample may have slightly biased the calculation of pleiotropic regions and, in turn, the pleiotropic PRS. Finally, the predictive power we obtain with a small population (N < 200) is relatively low (Adj. R 2 ~ 0.1–0.2), but is fully comparable to that of other studies with much larger cohorts [32, 69–71]. Further research with larger cohorts and including populations of non-European ancestry is needed to better understand this relationship and to develop effective interventions to address the issue of weight gain in people with FEP. In summary, our findings highlight that genetics is an important factor in determining the BMI trajectory in patients with FEP, paving the way for its inclusion in the clinical routine in order to identify individuals at higher risk, and to optimize individualized prevention programs to improve patients’ quality of life. In turn, our results lay the groundwork for addressing the prediction of comorbid trajectories in other diseases using a similar approach. ## Data Availability Statement The datasets used in the current study are available from the authors upon request. ## Author Contribution GM conceived and designed the study. GM and JV-B collected the data. GM conducted the analyses with support from ES and JV-B. GM drafted the manuscript and all co-authors provided critical suggestions. All authors contributed to the interpretation of the findings, read and accepted the final version of the manuscript for submission. ## Financial Support This work was supported by the Catalan Agency of Research and Universities (AGAUR, 2017SGR-00444, PI: E.V.). G.M. is supported by Instituto de Salud Carlos III (PI$\frac{18}{00514}$ and PI$\frac{21}{00612}$). The Santander (PAFIP) cohort was funded by the following grants: Instituto de Salud Carlos III (FIS$\frac{00}{3095}$, PI020499, PI050427, PI060507), Plan Nacional de Drogas Research (2005-Orden sco/$\frac{3246}{2004}$), SENY Fundatio Research [2005-0308007], Fundacion Marques de Valdecilla (A/$\frac{02}{07}$, API$\frac{07}{011}$), and MINECO/FEDER (SAF2016-76046-R, SAF2013-46292-R). J.V.-B. is supported by funding from Instituto de Investigación Valdecilla (INT/A$\frac{21}{10}$, INT/A$\frac{20}{04}$). A.N. is supported by funding from AEI-PGC2018-BI00 (FEDER/UE) (MINECO/FEDER, UE), “Unidad de Excelencia María de Maeztu,” funded by the AEI (CEX2018-000792-M), Secretaria d’Universitats i Recerca, and the CERCA Program of the Departament d’Economia i Coneixement de la Generalitat de Catalunya (GRC 2017 SGR 880). ## Conflict of Interest B.C.-F. has received honoraria (advisory board and educational lectures) and travel expenses from Takeda, Menarini, Angelini, Teva, Otsuka, Lundbeck, and Johnson & Johnson. He has also received unrestricted research grants from Lundbeck. J.V.-B. has received honoraria for his participation as a consultant and/or a speaker at educational events from Janssen-Cilag and Lundbeck. The rest of the authors report no biomedical financial interests or potential conflicts of interest. ## References 1. Trubetskoy V, Pardiñas AF, Qi T, Panagiotaropoulou G, Awasthi S, Bigdeli TB. Mapping genomic loci implicates genes and synaptic biology in schizophrenia*. 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--- title: Cheminformatics Bioprospection of Sunflower Seeds’ Oils against Quorum Sensing System of Pseudomonas aeruginosa authors: - Nosipho Wendy S’thebe - Jamiu Olaseni Aribisala - Saheed Sabiu journal: Antibiotics year: 2023 pmcid: PMC10044302 doi: 10.3390/antibiotics12030504 license: CC BY 4.0 --- # Cheminformatics Bioprospection of Sunflower Seeds’ Oils against Quorum Sensing System of Pseudomonas aeruginosa ## Abstract Clinically significant pathogens such as Pseudomonas aeruginosa evade the effects of antibiotics using quorum sensing (QS) systems, making antimicrobial resistance (AMR) a persistent and potentially fatal global health issue. Hence, QS has been identified as a novel therapeutic target for identifying novel drug candidates against P. aeruginosa, and plant-derived products, including essential oils, have been demonstrated as effective QS modulators. This study assessed the antipathogenic efficacy of essential oils from two sunflower cultivars (AGSUN 5102 CLP and AGSUN 5106 CLP) against P. aeruginosa ATCC 27853 in vitro and in silico. At the sub-inhibitory concentrations, both AGSUN 5102 CLP ($62.61\%$) and AGSUN 5106 CLP ($59.23\%$) competed favorably with cinnamaldehyde ($60.74\%$) and azithromycin ($65.15\%$) in suppressing the expression of QS-controlled virulence phenotypes and biofilm formation in P. aeruginosa. A further probe into the mechanism of anti-QS action of the oils over a 100-ns simulation period against Las QS system revealed that phylloquinone (−66.42 ± 4.63 kcal/mol), linoleic acid (−53.14 ± 3.53 kcal/mol), and oleic acid (−52.02 ± 3.91 kcal/mol) had the best affinity and structural compactness as potential modulators of LasR compared to cinnamaldehyde (−16.95 ± 1.75 kcal/mol) and azithromycin (−32.08 ± 10.54 kcal/mol). These results suggest that the identified compounds, especially phylloquinone, could be a possible LasR modulator and may represent a novel therapeutic alternative against infections caused by P. aeruginosa. As a result, phylloquinone could be further studied as a QS modulator and perhaps find utility in developing new therapeutics. ## 1. Introduction Biofilm formation in Pseudomonas aeruginosa is believed to be responsible for $65\%$ of P. aeruginosa-related deaths and poses a significant threat to individuals with cystic fibrosis (CF) due to antibiotic resistance [1]. Therefore, blocking biofilm formation in P. aeruginosa could help reduce mortality and curb antibiotic resistance. A biofilm consists of bacterial cells embedded in a matrix of extracellular polymeric substances (EPS) composed of lipids, macromolecules, DNA, exopolysaccharides, and proteins [2], rendering the organisms therein impermeable to antibiotics. Accordingly, the planktonic P. aeruginosa is 1000 times less resistant to antibiotics than biofilm-embedded P. aeruginosa [3,4,5]. Biofilm formation also allows P. aeruginosa to evade recognition by the host immune system [3], and this is coordinated by its quorum sensing (QS) system, which plays a crucial role in the formation, secretion, processing, and recognition of autoinducer (AL), a cell-to-cell communication molecule implicated in biofilm formation [6,7,8]. At specific density, as measured by the ambient AL concentration, the transcription of bacterial genes in the community becomes synchronized, allowing the organisms to act cooperatively. Thus, virulence factor secretion, swimming motility, secondary metabolite synthesis, biofilm development, and antibiotic resistance in the organism community are all controlled by ALs [9]. Consequently, disruption of P. aeruginosa QS-mediated signaling disrupts communication in the organism community, reducing pathogenicity and ultimately eradicating the organism from the host [10,11,12]. Pseudomonas aeruginosa has three main QS systems: LasI-LasR, RhlI-RhlR, and PQS-MvfR, all of which contribute to the formation of mature and differentiated biofilms [13,14,15]. However, the Las system regulates the expression of the other two systems with its autoinducer 3O-C12-HSL; thus, targeting the Las QS system could help prevent the expression of other QS systems [14,15,16]. In this regard, the molecular docking approach could help in the in silico screening of metabolites against the Las QS system due to its ability to identify lead metabolites from a library of compounds. Interestingly, the technique has been employed in the recent screening of metabolites against a bacterial QS system [17] and, thus, could serve as a less expensive approach to understanding compounds’ interactions with the QS system. However, due to the limitations of virtual screening relating to the lack of subsequent refinement (rescoring) and assessment of binding pose stability [18,19], further refinement and assessment of thermodynamic information are often encouraged using molecular mechanics/GB surface approach and molecular dynamics (MD) simulations [18,19], and in this study, these approaches were employed. Several synthetic compounds such as azithromycin, erythromycin, levamisole, propranolol, and chloroquine have been shown to have anti-QS activities in the search for QS inhibitors via drug repurposing [20]. However, the side effects of these drugs and the increase in the frequency of P. aeruginosa strains with increased virulence following their use [11,21] have prompted researchers to investigate alternative QS inhibitors, such as plant-derived compounds. Many plants, such as Pisum sativum seedlings, Citrus reticulata, and Syzygium aromaticum, have been recently studied as potent QS modulators [11,14]. Interestingly, Helianthus annuus (sunflower) and its oil have been found to have nutritional and therapeutic relevance as anti-inflammatory, antimalarial, antiasthmatic, antioxidant, antitumor, and antibacterial agents [22]. However, despite the antibacterial significance of H. annuus and its derived products, there is a paucity of information on its antipathogenic activity through QS inhibition to date. Hence, this study evaluated the anti-QS activity of two sunflower seed oils using both in vitro and in silico techniques. In the in vitro assay, a biomonitor strain, Chromobacterium violaceum, was employed to understand and easily detect QS inhibition in P. aeruginosa due to C. violaceum’s ability to produce the purple pigment violacein [23]. Thus, in addition to identifying active Las modulators of P. aeruginosa, metabolites of sunflower seed oil were screened in silico against the main QS regulator in C. violaceum, the CviR QS system [23]. Identifying lead compounds from plant-derived products, such as oils, against important QS regulators of P. aeruginosa and C. violaceum could help to identify active anti-QS modulators that could fast-track novel drug discovery and development and contribute toward the increased commercial importance of plants. ## 2.1. Plant Collection, Preparation, and Oil Extraction The two hybrids (AGSUN 5102 CLP and AGSUN 5106 CLP) of sunflower seeds used in this study were collected from the Agricultural Research Council, Potchefstroom, South Africa. The seeds were dehusked, washed, and oven-dried overnight at 30 °C to constant weight. Initially, the dried seeds were powdered using a mortar and pestle before 10 g of each was subjected to Soxhlet (Merck Sigma-Aldrich, Johannesburg, South Africa) extraction (50–55 °C) using 100 mL of n-hexane adopting a standard method [24]. After extraction, the remaining n-hexane was evaporated using a Heidolph Hei-VAP core rotary evaporator (ProfiLab24, Landsberger, Berlin) maintained at 40 °C, leaving only the extracted oil in the flask. The oil extracted was stored in an air-tight container and covered with aluminum foil to prevent auto-oxidation due to sunlight exposure. The oils were stored at 4 °C for further analysis. For the in vitro assays, stock solutions of oils were re-diluted to the required concentrations using $2\%$ dimethyl sulfoxide (DMSO). ## 2.2.1. Bacterial Strains and Growth Conditions For this study, P. aeruginosa ATCC 27853 and C. violaceum ATCC 12472 were collected from American Type Culture Collection (ATCC). Mueller–Hinton (MH) and Luria–Bertani (LB) media were prepared using distilled water. P. aeruginosa was subcultured into a fresh MH agar plate and incubated at 37 °C, while C. violaceum was subcultured into an LB agar plate and incubated at 30 °C for 24 h. Before each assay, the 24 h-old bacterial strains of P. aeruginosa and C. violaceum were re-cultured by inoculating single colonies in MH and LB broths and incubated with shaking maintained at 120 rpm at 37 °C and 30 °C, respectively. The overnight cultures were assessed for an OD600 nm of 0.8 to achieve 0.5 McFarland standard equivalent [17]. ## 2.2.2. Microdilution Assay for Antibacterial Activity The minimum inhibitory concentration (MIC) of the oils and those of the reference standards (azithromycin and cinnamaldehyde) were determined using the broth dilution method [17,25]. Briefly, using 96-well microtiter plates (Merck Sigma-Aldrich, Johannesburg, South Africa), 100 µL of Mueller–Hinton broth (MHB) was transferred into each well, after which 200 µL two-fold serial dilutions of the antibacterial agents (oils (concentration ranging from 0.72 to 367.2 mg/mL) and positive controls (azithromycin and cinnamaldehyde (0.031–4 mg/mL)) and $2\%$ DMSO (negative controls) was transferred into the wells. This was followed by the addition of 100 µL of standardized bacterial inoculum (1.33 × 108 CFU/well), and each treatment was performed in triplicates. After 24 h of incubation at 37 °C, 40 µL of P–iodonitrotetrazolium (INT, 0.2 mg/mL) was added and incubated for another 45 min. Bacterial growth inhibition (clear wells with no color change) was observed and recorded visually. The MIC of the oils was recorded as the lowest concentration that inhibited bacterial growth with no visible growth [26]. ## Qualitative Anti-Quorum Sensing Assay For this assay, a well diffusion method was performed according to Chenia [27] to detect the AQS activity of the oils. In brief, C. violaceum was grown in a sterile LB broth for 24 h at 30 °C, and the bacterial suspension was adjusted to an OD600 nm of 0.8. The standardized culture was evenly swabbed with a sterile swab across the sterile agar plate surface. The wells were inoculated with 100 µL of oils at varying concentrations of 91.8–11.48 mg/mL (MIC to $\frac{1}{8}$ MIC) in triplicates, with reference standards of cinnamaldehyde at 3.75–0.47 mg/mL (MIC to $\frac{1}{8}$ MIC) and azithromycin at 0.25–0.031 mg/mL (MIC to $\frac{1}{8}$ MIC) (positive QS inhibitors) and negative control of $2\%$ DMSO and incubated at 30 °C for 24 h. The plates were examined for violacein production, with the loss of purple violacein pigment (opaque zone) surrounding the wells indicating AQS and clear and transparent zones indicating bactericidal activity. As described by Cosa et al. [ 28], the diameter of the clear/halo inhibition zones was interpreted as follows: Susceptible (S) ≥ 15 mm, Intermediate (I) = 11–14 mm, and Resistant ≤ 10 mm. ## Quantitative Anti-Quorum Sensing Assay The oils were tested for the quantitative AQS activity at various concentrations ranging from MIC to $\frac{1}{8}$ MIC against the C. violaceum using the microdilution method described by Pauw and Eloff [24]. Cinnamaldehyde (MIC: 3.75–$\frac{1}{8}$ MIC: 0.47 mg/mL) and azithromycin (MIC: 0.25–$\frac{1}{8}$ MIC: 0.031 mg/mL) served as reference standards. Before incubation, the absorbance was measured at OD600 nm (to check the bacterium’s viability and growth) and OD485 nm (violacein production). The 96-well plate was then incubated (120 rpm) at 30 °C for 24 h. After incubation, absorbance was measured again at OD420 nm. Subsequently, the plate was dried in a drying oven at 50 °C for 24 h. After drying, 150 µL of $100\%$ DMSO was used to re-suspend the dried contents in each well, mixed thoroughly, and placed in the shaking incubator at 30 °C, 120 rpm for 1 h to confirm that the essential oils inhibited quorum sensing without affecting bacterial growth activities. Thereafter, absorbance was measured at an OD485 nm to determine the concentration of violacein. The analysis was performed in triplicates. Equation [1] was used to calculate the percentage (%) of inhibition [25]. [ 1]Percentage %inhibition=OD control−OD testOD control×100 Control: Controls (negative (C. violaceum treated with $2\%$ DMSO), positive (cinnamaldehyde and azithromycin)), Test: Oil extracts (AGSUN 5102 CLP and AGSUN 5106 CLP). ## 2.2.4. Inhibition of Cell Attachment The oils and standards (azithromycin and cinnamaldehyde) were used to test for inhibition of cell attachment (anti-adhesion) against P. aeruginosa. The method used was slightly modified from that of Famuyide et al. [ 29]. A Sigma® 96-well microtiter plate (Merck Sigma-Aldrich, Johannesburg, South Africa) was used where a 100 µL of standardized bacterial suspension (OD600 nm = 0.8), 100 µL of MHB, and 100 µL of oil were added to the wells at various concentrations (MIC to $\frac{1}{8}$ MIC) in triplicates. The reference standards (azithromycin and cinnamaldehyde) and negative control ($2\%$ DMSO) were also added into their respective wells. The blank wells contained 200 µL of sterile MHB, and all the treatments were thereafter incubated at 37 °C for 24 h [17]. The modified crystal violet (CV) assay was used to assess biofilm biomass. To remove planktonic cells and media, the 96-well microtiter plates containing formed biofilm were washed three times with sterile distilled water. The plate was then oven-dried for 45 min at 60 °C. After drying, $1\%$ CV solution was used to stain the remaining biofilm in the dark for 15 min. To remove any unabsorbed stains, the wells were washed three times with sterile distilled water. A semi-quantitative assessment of biofilm formation was performed by de-staining the wells with 125 µL of $95\%$ ethanol. Subsequently, 100 µL of the de-staining solution was transferred to a new plate, and the absorbance (OD585 nm) was measured with a SpectraMax® paradigm multimode microplate reader (Molecular Devices, Separations, South Africa) and the inhibitory effect of the oils and standards were calculated [17]. The following criterion for interpreting results was used: values between 0 and $100\%$ were interpreted as inhibitory activity, and it was further broken down as follows: ≥$50\%$ (high activity) and values between 0 and $49\%$ (low activity) [29]. ## 2.2.5. Inhibition of Biofilm Development A total of 100 µL of standardized bacterial suspension and 100 µL of MHB were added to a 96-well microtitre plate (Merck Sigma-Aldrich, Johannesburg, South Africa) for biofilm development bioassays and incubated at 37 °C for 8 h. Thereafter, 100 µL of the oils and reference standards (azithromycin and cinnamaldehyde) were added to the wells at various concentrations (MIC to $\frac{1}{8}$ MIC) in triplicates and incubated for another 24 h. Subsequently, the CV staining protocol was carried out as described in Section 2.2.4, the inhibitory effect of the test oils and standards was determined, and the results obtained were interpreted as previously reported [29]. ## 2.2.6. Confocal Laser Scanning Microscopy (CLSM) Confocal laser scanning microscopy was used to evaluate the viability of biofilms, as earlier reported [30]. Pseudomonas aeruginosa’s biofilm was grown on glass pieces (1 × 1 cm) positioned in 24-well polystyrene incubated at 37 °C for 8 h. Following an 8-h incubation period, the preformed biofilm was supplemented with the MIC of the oils and standards (azithromycin and cinnamaldehyde) in triplicates and incubated for another 24 h. The adherent biofilm was delicately washed with deionized water before smearing with a live/dead backlight viability kit comprised of Syto 9 fluorescence and propidium iodide (PI) and incubated in the dark for 15 min. The plate was washed once more after staining. SYTO 9 fluorescence was identified using a Zeiss LSM 510 (Carl Zeiss Microscopy, Jena, Germany) confocal laser-scanning microscope with excitation at 488 nm, and the emission was collected with a 500–530 bandpass filter. ## 2.2.7. Pyocyanin Assay The pyocyanin assay was carried out using a slightly modified method of Bhattacharya et al. [ 31]. In a nutshell, an overnight P. aeruginosa culture was diluted to an OD600 nm of 0.8. Following that, the oils and standards (azithromycin and cinnamaldehyde) were added in various concentrations (MIC to $\frac{1}{8}$ MIC) in triplicates, along with the standardized culture in King’s A broth, and incubated overnight at 37 °C. A 1.5 mL volume of overnight culture was centrifuged at 3000× g for 10 min. Thereafter, 1 mL of the supernatant was transferred into new centrifuge tubes (pre-cooled in ice), allowed to chill, and 100 µL chloroform was added while still on ice. Then, 300 µL of 0.2 M hydrochloric acid was added and vigorously mixed with a vortex mixer (EINS Sci E-VM-A, Biotechnology Hub Africa, Hatfield, South Africa). The pyocyanin-containing chloroform layer was collected and transferred to a Sigma® 96-well microtiter plate (Merck Sigma-Aldrich, Johannesburg, South Africa). The absorbance was measured using a SpectraMax® paradigm microtiter plate reader (Molecular Devices, Separations, South Africa) at 520 nm. To obtain the mean value, the experiments were repeated three times. The concentration of pyocyanin was calculated by multiplying the OD at 520 nm by 17.072 (the molar extinction coefficient). Pyocyanin production was compared to that of untreated cells, which was used as a control [17]. ## 2.2.8. Swarming and Swimming Motility Assays The swarming assay medium consisted of nutrient broth ($0.8\%$, w/v) supplemented with glucose ($3\%$, w/v) and $0.5\%$ (w/v) agar. The standardized bacterium, P. aeruginosa (2 µL), was spotted on swarming media supplemented with or without the oils on agar plates. Oils were tested at various concentrations (MIC to $\frac{1}{8}$ MIC) in triplicates, with positive (azithromycin and cinnamaldehyde) and negative ($2\%$ DMSO) controls, respectively. The plates were incubated at 37 °C for 24 h, and thereafter, the zone diameters (mm) were measured and compared to the negative and reference standards to assess swarming motility. To obtain the mean value, the experiments were carried out in triplicate [17]. The swimming assay was carried out using swimming media as previously described [32] with minor modifications using $1\%$ tryptone, $0.5\%$ NaCl, and $0.5\%$ agar. The plates were inoculated with 2 µL of P. aeruginosa (OD600 nm of 0.8) and either the oils or the controls (azithromycin and cinnamaldehyde) at various concentrations (MIC-$\frac{1}{8}$ MIC) in triplicates and incubated at 37 °C for 24 h. The turbid zone diameter (mm) was then measured by comparing the observation with the negative control ($2\%$ DMSO). ## 2.3.1. Gas Chromatography-Mass Spectrophotometry (GC-MS) Analysis of the Oils The chemical composition of the oils from the seeds (AGSUN 5102 CLP and AGSUN 5106 CLP) was determined using a GC-MS Shimadzu QP 2100 SE (Shimadzu Corporation, Tokyo, Japan) equipped with an Inert Cap 5 MS/NP capillary (30 m × 0.25 mm × 0.25 µm: GL Sciences, Tokyo, Japan) capillary column. The temperature of the column oven was initially held at 80 °C, then increased at a rate of 5 °C/min to 200 °C with a hold period of 2 min, and then increased to 280 °C with a hold time of 10°C/min. The temperature of the GC injector was set to 270 °C. A continuous flow rate of 1 mL/min of helium was used as a carrier gas. In full scan mode, electron ionization (EI) mass spectra were acquired throughout the range of m/z 40–550 at 70 eV ionization voltages. The temperatures of the ion source and transfer line were set to 200 and 250 °C, respectively. By comparing the retention times and mass spectra of the components to those of the National Institute of Standards and Technology (NIST) 05 mass spectral database, the components of the oils were identified [33]. ## 2.3.2. High-Performance Liquid Chromatography (HPLC) Analysis of the Oils for Phylloquinone Identification The phylloquinone in the oils was identified using an HPLC system comprised of a D-6000 Merck Hitachi Interface, LC-Solution Software, an AS-4000A Intelligent Autosampler (Merck, Vienna, Austria) equipped with a Rheodyne 7125 injection valve and a 50 µL loop (Cotati, California), an L–6000 Merck Hitachi pump, and an L-7400 LaChrom UV Detector from Merck Hitachi. A LiChroCart® RP-18 Lichrosphere®, 5 µm, 250–4.6 mm (Merck) column dry-packed with zinc powder was used, with a 4 × 4 mm guard column and RF-10AXL fluorescence detector. The extracted phylloquinone fraction from the oil sample was dissolved in 200 μL of mobile phase, and 50 μL was injected. The separation was in reverse phase with a LiChrospher RP-18 5 μm end-capped LiChroCART 250–4.6 column, with a pre-column from Merck and a mobile phase consisting of dichloromethane/methanol (10:90 v/v) with the addition of 5 mL of methanol solution with zinc chloride (1.37 g), sodium acetate (0.41 g), and acetic acid (0.30 g) per liter of mobile phase and was pumped at a flow rate 1.00 mL/min with isocratic elution. The post-column reduction (20 × 4.0 mm id) was filled manually with zinc dust p.a. grade from Merck with particles < 45 μ and kept in a furnace (Shimadzu-CTO-6A) at 40 °C with fluorescence detector excitation 243 nm and emission 430 nm. Phylloquinone peaks were identified by comparing their retention times to those of standards. Concentrations were calculated from peak areas determined using a Jasco ChromNav Chromatography Data System (Jasco Corporation) [34]. ## Proteins Retrieval, Prepping, and Active Site Identification The X-ray crystal structures of the Las protein from P. aeruginosa (LasR: 2UV0, LasA: 3IX7, LasB: 3DBK, LasI: 1RO5, ToxA: IXK9, AprA: 1KAP) and the CviR QS protein from C. violaceum (CviR: 3QP1, VioA: 6G2P) were downloaded from the RCSB Protein Data Bank (https://www/rcsb.org/pdb) (accessed on 16 May 2022). The proteins were saved in pdb format using the UCSF Chimera 1.15 software package after optimization via the removal of all non-standard residues, non-essential water molecules, and other heteroatoms (co-crystallized ligands) attached to the protein. The active site coordinates were identified via Discovery Studio 2021. They were validated by optimally superimposing docked ligands against the reference pocket containing the native ligand in the experimental co-crystallized LasR structure. The Root Mean Square Deviation (RMSD) value (0.5 Å) obtained between the docked ligands and the native inhibitor orientation confirmed the approach used [19,20,21,22,23]. ## Ligand Procurement The standard drugs (azithromycin and cinnamaldehyde) and all phytochemical compounds identified from GC-MS were acquired in sdf format from PubChem (www.pubchem.com) (accessed on 16 May 2022). ## Docking Subsequent to the optimization of the proteins and ligands, the python prescription (PyRx-0.8) software coupled with AutoDock vina was used for docking. The phytoconstituents of the oils and standard (azithromycin and cinnamaldehyde) were docked into the active sites of each of the Las proteins (P. aeruginosa) and CviR QS proteins (C. violaceum) by selecting the amino residues present in a 3-D structure of a co-crystallized protein downloaded from the protein data bank. The best binding pose with the highest binding affinity was saved in pdb format, and each final complex was visualized in Discovery Studio 2021 for the interactions formed between the ligands and Las protein and CviR. The standards and the phytoconstituents with the best interaction and docking scores against the most vulnerable Las protein (Las protein in which the phytoconstituents had best docking scores) from P. aeruginosa and the most vulnerable CviR protein from C. violaceum were then subjected to MD simulation [35]. ## 2.4.2. Molecular Dynamics Simulation The MD simulation was carried out on the Center for High Performance and Computing’s system, using AMBER 18 software to run the FF18SD variation of the amber force field. The TIP3P water molecules, Na+, and Cl− counter ions were included to neutralize the system using the ANTECHAMBER atomic partial charges assigned to the ligands, and the non-bond interactions cut-off value was adjusted to 8 Å using the ANTECHAMBER atomic partial charges assigned to the ligands. The system started with 2000 minimization steps, was constrained with 500 kcal/mol potential for both solutes on another 1000 steps with the help of the steepest descent approach, and was then followed by conjugate gradients of 1000 steps. Then, using the conjugate gradient technique, the entire minimization stage was carried out in 1000 unrestricted steps. To maintain a constant volume of water and atoms, the following MDS stage involved 50 ps of heating from absolute zero (0 K) to 300 K. The solutes were then exposed to collision frequency and possible harmonic constraint (10 kcal/mol) (1.0 ps). The system was then brought to equilibrium by applying 500 ps while maintaining a constant heating temperature of 300 K. Other factors, such as several atoms and pressure (at 1 bar), were kept constant to simulate an isobaric–isothermal ensemble (NPT) [35]. While each MD simulation took 100 ns on average and contracting H+ by simulation using the SHAKE algorithm using the SPFP precision model, the step size was reduced for the bond 2 fs. NPT, constant pressure, and the simulations agreed [35]. Temperature (300 K), pressure coupling constant (2 ps), randomized seeding, and a Langevin thermostat number of collisions (1.0 ps) [35]. The CPPTRAJ program was used to process the trajectories for the analyses of the post-MD simulation parameters such as root–mean square deviation (RMSD), root–mean square fluctuation (RMSF), and radius of gyration (RoG), while the Leap Module Shake algorithm was used to reduce the expansion of chemical bonds involving hydrogen atoms. The molecular Mechanics/GB Surface Area technique (MM/GBSA) was utilized to compute the binding free energy for each ligand–protein combination over a period of 100 ns simulation [36]. Based on Ylilauri and Pentikäinen [37], the free binding energy (G) was calculated using the Molecular Mechanics/GB Surface Area Method (MM/ GBSA). This was performed to estimate the systems’ binding affinities. In a 100-ns trajectory, the average of G across 100,000 photos was found. ## 2.4.3. Pharmacokinetic Properties The SwissADME server (http://www.swissadme.ch/) (accessed on 21 July 2022) was used to estimate the pharmacokinetic parameters (absorption, distribution, metabolism, and excretion (ADME) features and drug-likeness) of the top three compounds of the oils and the controls. ## 2.5. Statistical Analysis Except otherwise stated, the in silico and in vitro results were expressed as the mean ± standard error of the mean (SEM) and in percentages (%), and statistical analyses were carried out using the Graph Pad Prism version 5.0 and one-way analysis of variance (non-parametric tests) to determine the significant difference ($p \leq 0.05$) between the treatment means. ## 3.1.1. Minimum Inhibitory Concentration A color change was observed after adding INT. Pink coloration indicated no inhibition, while clear zones indicated significant inhibition. This was performed to determine the MICs of the oils against planktonic growth of P. aeruginosa ATCC 27853, and the MICs obtained for both oils were 91.80 mg/mL each, while those of azithromycin and cinnamaldehyde were 0.25 mg/mL and 3.75 mg/mL, respectively (Table 1). The negative control, $2\%$ DMSO, showed no inhibitory effect (Table 1). An exponential growth was seen in P. aeruginosa (Figure 1) and C. violaceum (Figure 2) treated with $2\%$ DMSO throughout the incubation period, with a total bacterial count of 4.53 × 108 CFU/mL and 4.65 × 108 CFU/mL, respectively, after 36 h. However, while the lag phase (before 15 h for both organisms) of the oil- and reference standards-treated cells was comparable to the negative control ($2\%$ DMSO-treated cells), a moderate decrease in bacterial counts was observed for both organisms after 15 h, with the most profound effect observed with azithromycin (1.1 × 108 CFU/mL for both organisms) after 36 h. The total bacterial count observed for AGSUN 5102 CLP-treated cells was comparable to the cells treated with cinnamaldehyde, which were significantly different from cells treated with AGSUN 5106 CLP at p ≤ 0.05 for both organisms (Figure 1 and Figure 2). ## 3.1.2. Anti-Quorum Sensing (Qualitative and Quantitative) Treatments with the oils caused loss of the purple violacein pigment of C. violaceum that QS mediates (opaque halo) around the agar wells following the addition of essential oils with zones of between 1.00 and 2.33 mm in diameter at 91.80 mg/mL (MIC) with AGSUN 5102 CLP having the highest zone of inhibition (Table 2 and Figure S1d,e from Supplementary Materials). On the other hand, azithromycin and cinnamaldehyde elicited AQS activity with distinct zones of inhibition of 13.33 mm and 5.33 mm at 0.25 mg/mL and 3.75 mg/mL (MIC), respectively. However, $2\%$ DMSO (negative control) showed no discernible zones of inhibition (Table 2 and Figure S1a–c from Supplementary Materials). Moreover, at concentrations lesser than the MIC, no zones of inhibition were observed. However, a concentration-dependent AQS activity was quantitatively observed after treatment with essential oils and reference standards. At MIC, AGSUN 5102 CLP and AGSUN 5106 CLP showed $72.38\%$ and $62.96\%$ inhibition, respectively, while cinnamaldehyde and azithromycin showed $74.23\%$ and $70.42\%$ inhibition, respectively (Figure 3). ## 3.1.3. Inhibition of Cell Attachment and Biofilm Formation The oils showed favorable competition with the reference standards for biofilm and cell attachment inhibition. It was observed that the inhibitory effect of the oils on P. aeruginosa biofilm formation and cell attachment was concentration-dependent (Figure 4 and Figure 5). At MIC, AGSUN 5102 CLP and AGSUN 5106 CLP had $56.64\%$ and $50.34\%$ inhibitory effect on cell attachment, respectively, while azithromycin and cinnamaldehyde elicited $70.24\%$ and $55.42\%$ inhibitory effect on cell attachment, respectively (Figure 4). Similarly, treatment with AGSUN 5102 CLP and AGSUN 5106 CLP at MIC showed that biofilm formation was reduced by $62\%$ and $59\%$, respectively, while azithromycin and cinnamaldehyde showed $65\%$ and $60\%$ inhibitory effect, respectively. However, an improvement in biofilm formation (−$14.52\%$) was observed after treatment with AGSUN 5106 CLP at $\frac{1}{8}$ MIC (Figure 5). ## 3.1.4. Confocal Laser Scanning Microscopy Upon treatment of P. aeruginosa cells with the oils, a significant reduction in biofilm matrix, thickness, and biomass was observed (Figure 6). In the untreated cells or those treated with negative control ($2\%$ DMSO), dominant biomass and thickness of biofilms were distinctly observed (Figure 6a). However, there was a significant reduction upon supplementation with the oils. Treatment with azithromycin completely inhibited biofilm matrix formation (Figure 6b), whereas the effect produced by treatment with cinnamaldehyde (Figure 6c) was comparable with those of the oils (Figure 6d,e), with the effect observed with AGSUN 5102 CLP being more pronounced in reducing the thickness and biomass of biofilms (Figure 6d). ## 3.1.5. Inhibition of Pyocyanin A concentration-dependent reduction of pyocyanin was observed following treatment with the oils and reference standards. Pyocyanin production was significantly inhibited (p-value ≤ 0.05) following treatment (at MIC), with AGSUN 5102 CLP ($46.46\%$) and AGSUN 5106 CLP ($31.81\%$) competing well with azithromycin ($53.81\%$) and cinnamaldehyde ($43.60\%$) (Figure 7). ## 3.1.6. Inhibition of Swarming and Swimming Motility Treatment with the oils significantly (p ≤ 0.05) decreased swarming motility in the cells compared to the untreated control (Table 3 and Figure S2 from Supplementary Materials) at different doses. The highest inhibition was observed at the MIC (91.80 mg/mL), with AGSUN 5102 CLP (2.0 mm) and AGSUN 5106 CLP (3.0 mm) having activity comparable to the reference standards azithromycin (2.0 mm) and cinnamaldehyde (2.5 mm) (Table 3). As shown in Table 4, treatment with the oils and standards decreased swimming motility at all the investigated concentrations. Most of the reduction was observed at the highest concentrations (MIC) of the oils and reference standards (Table 4 and Figure S3 from Supplementary Materials). Compared to the untreated cells (34.0 mm), treatment with the oils (at MIC) significantly (p ≤ 0.05) reduced motility with zone diameters of 21.0 mm (AGSUN 5102 CLP) and 23.0 mm (AGSUN 5106 CLP) (Figure S3d,e from Supplementary Materials), which was higher than that observed for azithromycin (17.0 mm) but comparable to 22.0 mm observed for cinnamaldehyde (Table 4; Figure S3b,c from Supplementary Materials). ## 3.1.7. Chromatography (GCMS and HPLC) Analysis of the Oil The results of the chromatographic analysis of oils revealed 15 similar constituents between the two cultivars; however, they differed in quantity and abundance (Table 5 and Figure 8a,b). The components identified were mainly fatty acids and vitamins, with linoleic and oleic acids being the most abundant components (Table 5). The full details of the mass-to-charge/ion (m/z) ratios and retention times of the compounds are presented in Table S1. ## 3.2.1. Molecular Docking The docking scores of the 15 constituents from the two oils against Las proteins from P. aeruginosa and CviR from C. violaceum are shown in Table S2 from Supplementary Materials. Of the Las proteins, the constituents had higher negative docking scores (Table S3 from Supplementary Materials) and interactions (Table S4 from Supplementary Materials) against P. aeruginosa LasR. Phylloquinone (−9.4 kcal/mol), linoleic acid (−8.5 kcal/mol), and oleic acid (−8.2 kcal/mol) were the three most promising constituents/metabolites against LasR, judging by their higher interactions and docking scores relative to the reference standards (Azithromycin (−6.4 kcal/mol); cinnamaldehyde (−7.4 kcal/mol)), with phylloquinone being the most promising metabolite (Table 6). Similarly, of the two proteins (CviR and VioA) involved in the CviR QS system, the CviR protein was the most inhibited (Table S5 from Supplementary Materials). The top four metabolites (phylloquinone (−8.6 kcal/mol), linoleic acid (−6.7 kcal/mol), myristic acid (−6.6 kcal/mol), lauric acid (−6.6 kcal/mol)) of the oils against CviR had competitive docking scores (Table S6 from Supplementary Materials) and higher binding interactions (Table S7 from Supplementary Materials) compared to the reference standards (cinnamaldehyde (−7.1 kcal/mol), azithromycin (−5.3 Kcal/mol)), with phylloquinone having the highest docking scores and interactions. The data obtained regarding the validation of the docking protocol with the top three metabolites and the reference standards by optimal superimposition with the native LasR inhibitor yielded an RMSD value of 0.5 Å (Figure 9). ## 3.2.2. Molecular Dynamics Simulation Compared to the standards, the top three compounds had significantly higher total binding free energy values ((−66.42 ± 4.63 kcal/mol (phylloquinone), −53.14 ± 3.53 kcal/mol (linoleic acid), and −52.02 ± 3.91 kcal/mol (oleic acid)) against LasR (Table 7) with phylloquinone having the highest value. The RMSD of the LasR–ligand complexes converged at approximately 15 ns and 45 ns, however after 60 ns, they were substantially stable (Figure 10a), with the least fluctuations at amino residues 15–35, 45–50, and 100–110 (Figure 10b). Lesser fluctuations in RoG (Figure 10c) and SASA (Figure 10d) were seen in LasR + cinnamaldehyde, LasR + phylloquinone, LasR + linoleic acid, and LasR + oleic acid complexes throughout the 100 ns simulation compared to LasR + azithromycin, with pronounced fluctuation in RoG between 70 and 90 ns and SASA between 20 and 85 ns (Figure 10c,d). Figure 10e shows a consistent fluctuation in the pattern of the number of hydrogen bonds produced in LasR, typically between 60 and 100 before and after the binding of the top three compounds of the oils, azithromycin, and cinnamaldehyde. Of the top three metabolites studied, phylloquinone had the lowest average RMSD (1.51 Å), RMSF (1.11 Å), and SASA (8623.64 Å) values compared to the values for azithromycin–LasR. LasR ligand binding generally increases mean RMSD, RMSF, and SASA values but has little effect on mean RoG values, with a negligibly small variance of 0.26 Å between the resulting complexes (Table 8). The top three compounds, alongside azithromycin and cinnamaldehyde, had different natures, bond lengths, and numbers of interactions at the active site of LasR after the 100 ns simulation, which impacted the resulting binding free energy values in this study (Table S10). LasR–cinnamaldehyde complex had a total of 13 bonds, with conventional hydrogen bonds (Trp54), π–π stacked and π–π T-shaped (Trp88 and Phe95), π–alkyl (Ala99), and van der Waals (Tyr58; Asp67; Thr69; Ile86, Pro60; Tyr87; Phe96; Leu104; Tyr50). The complex with azithromycin had 13 bonds, comprising conventional hydrogen bonds (Glu133), carbon–hydrogen bonds (Asp23; Leu24; Gly25; Phe26), unfavorable donor–donor (Arg136), alkyl (Val126 and Val141), and van der Waals (Ser27; Phe137; Ser22; Glu127; Ala128). LasR–phylloquinone complex, on the other hand, had 30 interactions, including conventional hydrogen bonds (Thr109 and Trp54), π–anion (Asp67), π–π stacked (Tyr58 and Trp82), alkyl and π–alkyl (Leu30; Ala121; Leu119; Ile46; Tyr41; Leu34; Ala44; Val70; Cys73; Leu104), and van der Waals (Asp59; Phe45; Gly32; Phe31; Gly20; Thr74; Ala64; Leu33; Thr69; Tyr87; Ser123; Arg55; Ala99; Phe95; Tyr50), when compared to LasR–linoleic acid complex with 23 interactions comprising conventional hydrogen bonds (Tyr87 and Leu104), alkyl and π–alkyl (Arg55; Tyr58; Val70; Trp54; Trp82; Ile46; Tyr41; Ala121; Leu30; Phe95), and van der Waals (Leu122; Gly32; Phe31; Asp67; Ala99; Val105; Phe96; Thr69; Thr109; Tyr50; Ser123). The oleic acid complex formed 29 interactions, including conventional hydrogen bonds (Glu83), carbon–hydrogen bonds (Trp82), alkyl and π–alkyl (Tyr87; Ala99; Trp54; Val70; Tyr58; Ala121; Leu30; Leu104), and van der Waals (Ile86; Pro84; Phe96; Phe95; Tyr50; Ser123; Ala64; Gly120; Tyr41; Leu34; Ala44; Leu33; Gly32; Phe45; Ile46; Phe31; Thr109 Asp67; Thr69) (Table S10). Against CviR of C. violaceum, higher binding free energy values were observed with the top four metabolites relative to the cinnamaldehyde (−16.90 kcal/mol), while in contrast to azithromycin (−32.05 kcal/mol), only phylloquinone (−50.56 kcal/mol) and linoleic acid (−41.17) had higher values, with phylloquinone having the highest value (Table S8 from Supplementary Materials). Interestingly, except for linoleic acid, the binding of CviR caused reduced mean RMSD values, with myristic acid (1.23 Å) having the lowest value (Table S9, Figure S4 from Supplementary Materials). Similarly, with the exception of linoleic acid and azithromycin, ligand binding of CviR caused reduced average RMSF values, with myristic acid (1.04 Å) having the lowest value (Table S9 from Supplementary Materials). The RoG, SASA, and intramolecular hydrogen bonds showed slightly increased values following binding of CviR, with azithromycin–CviR having the highest RoG and SASA values, while myristic acid–CviR had the highest intramolecular hydrogen bonds (Table S9, Figure S4 from Supplementary Materials). The top-ranked metabolites, azithromycin, and cinnamaldehyde all had different natures, bond lengths, and interactions at the CviR active site, which impacted the binding free energy values observed with the target (Figure S10 from Supplementary Materials). During the simulation, a consistent number of interactions at 30 ns, 60 ns, and 100 ns were observed with the top-ranked metabolites, and reference standards against LasR and CviR (Table S10 from Supplementary Materials) and those formed between the metabolite (phylloquinone) (Figure 11a) and reference standard (azithromycin) (Figure 11b) with the highest affinity with LasR are presented in Figure 11. Phylloquinone–LasR complex had a total of 30, 29, and 30 interactions at 30 ns, 60 ns, and 100 ns, respectively (Figure 11a), while azithromycin–LasR complex formed 5, 6, and 13 interactions at 30 ns, 60 ns, and 100 ns, respectively (Figure 11b). Interactions formed between other top-ranked metabolites with LasR and those formed with CviR at 30 ns, 60 ns, and 100 ns are presented in Table S10. ## 3.2.3. Pharmacokinetic Properties The identified top three compounds against LasR in this study did fairly well with the violation of Lipinski’s rule of five, having a molecular weight of less than 500 g/mol (Phylloquinone (450.70 g/mol); Linoleic acid (280.447 g/mol); Oleic acid (282.470 g/mol)), hydrogen bond donors of less than 5 ((Phylloquinone [0]; Linoleic acid [1]; Oleic acid [1]), and hydrogen bond acceptors of less than 10 ((Phylloquinone [2]; Linoleic acid [2]; Oleic acid [2]) (Table 9). Azithromycin, on the other hand, had three violations (Table 9). While azithromycin and phylloquinone had a low gastrointestinal tract (GIT) absorption rate, linoleic acid and oleic acid had higher GIT absorption rates (Table 9). Azithromycin had the lowest bioavailability score, while phylloquinone had a bioavailability score that was comparable to that of cinnamaldehyde. Except for phylloquinone and azithromycin, which were predicted to be poorly soluble, the other top-ranked metabolites are moderately soluble in an aqueous environment (Table 9). ## 4. Discussion There have been numerous studies conducted on sunflower seeds to determine their antibacterial, anticancer, antioxidant, and other health benefits [38,39,40]. However, no information exists on their anti-QS potential to date, hence the motivation for this study. Solvent extraction is the traditional method for obtaining oil from oilseeds, and in this study, n-hexane was employed as a solvent for extraction because of its benefits, including easy recovery, low latent heat of vaporization (330 kJ/kg), non-polarity, and good selectivity to solvents [41]. The MIC of an antibiotic is the lowest concentration at which bacterial growth is prevented [42]. In this study, the two oils (AGSUN 5102 CLP and AGSUN 5106 CLP) had remarkable MIC, which was higher than those obtained for the reference standards (azithromycin and cinnamaldehyde), and the one reported by Liu et al. [ 38] but were significantly lower than the values reported by Benites et al. [ 43]. Interestingly, despite the fact that the oils were not refined as the standards used in this study, the values obtained with the two cultivars fall within the limit reported by Ács et al. [ 44], which is >43.4 mg/mL. Thus, it can be inferred that P. aeruginosa showed considerable susceptibility to the active constituents of the oils from the two investigated cultivars of sunflower seeds. Hence, both oils could be further explored as possible antibacterial agents against the test pathogen in this study. The pathogens were treated with the oils and reference standards for 36 h to understand the growth pattern of P. aeruginosa and C. violaceum. The initial steady growth observed before 15 h for the oils and reference standard-treated cells could be due to the low permeability of the outer membrane of Gram-negative bacteria, which enables them to express or exhibit innate resistance [45]. However, the observed decrease in bacterial growth beyond 15 h in cells treated with the oils and the reference standards could be attributed to the minute penetration of the outer membrane by the administered treatments, which could have initiated a slow release of bacterial cell components that eventually led to cumulative cell death. Ideally, for the oils to act as AQS or potential antipathogenic agents, they only need to show less hindrance to bacterial development, as the speculation of antipathogenic drugs ought not to be bactericidal but to hinder or disrupt the virulence factors [45]. Chromobacterium violaceum ATCC 12472, a well–recognized QSI biomonitor strain that generates the purple pigment known as violacein, was chosen as the QSI biomonitor strain as it is an efficient bacterium to visually detect and quantify pigment suppression by metabolites of the investigated oils in this study. Generally, violacein pigmentation in C. violaceum ATCC 12472, regulated by QS chemical communications, produces a naturally occurring and easily observable phenotype without the need for additional substrates, making it simple to assess how well a substance inhibits QS [46]. However, it is worth noting that this screening technique does not provide information on the precise kinds and quantities of active chemical compounds that are present [47]. In this study, treatment with the oils might have impacted violacein pigment, which could indicate that the pathogen’s QS system had been tampered with, as seen by the sizes of the opaque zones surrounding the wells administered with the oils. However, the small size of the opaque zones following treatments with the oils could indicate that the biomonitor strain exhibited resistant characteristics to the oils, which according to Kowalska–Krochmal and Dudek–Wicher [42], suggest a substantial possibility of therapeutic failure even with increasing doses of the oils. On the other hand, azithromycin, which is a refined drug, demonstrated an intermediate characteristic at MIC towards the biomonitor strain, which was largely anticipated, suggesting a likelihood of therapeutic success by increasing the dosage of the medication. Generally, judging by the results obtained, it could be logically inferred that qualitative violacein inhibitory assay might not be the best method to explore AQS potentials as a therapeutic agent, and this was consistent with the submission of Cosa et al. [ 28]. Hence, the need for quantitative studies to complement qualitative assays in evaluating the AQS potential of an agent. In this study, to further validate the interference of the QSS of C. violaceum by the oils through inhibition of the purple pigment as suggested by Cosa et al. [ 28], a quantitative AQS was performed. The two oils exhibited varying levels of AQS activity against C. violaceum, as confirmed by the significantly reduced production of violacein following treatment by AGSUN 5102 CLP and AGSUN 5106 CLP at both MIC and sub-MIC doses. This observation agrees with a previous study that demonstrated that antipathogenic medications should show their AQS potential with their effectiveness shown at sub-MIC concentrations and upward [45]. In a related investigation, Khan et al. [ 48] noted a remarkable halt in the formation of the violacein pigment in C. violaceum when essential oils of cinnamon, peppermint, and lavender were present. Similarly, according to Noumi et al. [ 49], tea tree oil demonstrated significant inhibition of violacein in C. violaceum ATCC 12472 at MIC and sub-MIC. In this study, like the essential oils, both cinnamaldehyde and azithromycin also had significant AQS activity at all doses. These findings are significant, as they demonstrate that some metabolites of the oils had a structural resemblance to the analogs of signaling molecules (AHL), allowing competitive structural binding between AHL and the oils’ metabolites with the appropriate receptor protein in the organism, thus, affecting the transmission of signal molecules and QS due to essential oils’ metabolites interfering with the C. violaceum CviR-QSS. The results of this study showed that the oils could prevent the development of QS. The current study also aimed to investigate the ability of the oils to get rid of biofilms of P. aeruginosa both in early and mature stages. The oils significantly inhibited cell attachment in a concentration-dependent manner, coherent with what was seen in the quantitative AQS. Both oils favorably competed with the standards in reducing cell attachment. The anti-adhesion activity observed with the oils in this study agrees with the findings of Pejčić et al. [ 50], who observed that the presence of sage and basil essential oils significantly inhibited cell attachment. Since cell attachment is significant in the advancement of infection caused by P. aeruginosa [17], their inhibition during the early stages of biofilm formation by the essential oil suggested their importance as an AQS antimicrobial. As in the inhibition of cell attachment, a similar trend in the inhibition of biofilm development was observed in this study, where both oils competed relatively well with the controls in reducing biofilm formation. Remarkably, the reduction of biofilm formation by both oils was observed at all concentrations except at 18MIC of AGSUN 5106 CLP, where the biofilm formation was enhanced. This observation points to the AQS potency of the essential oils. This phenomenon has also been reported by other researchers [17,51]. However, one explanation for biofilm formation enhancement at 18MIC of AGSUN 5106 CLP could be due to the efflux pump resistance mechanism during the cell attachment stage, where AGSUN 5106 CLP oil might have been expelled from the cells [17]. Another possible reason for biofilm formation enhancement at 18MIC could be because some of the metabolites of the oil were utilized by the bacteria as a source of nourishment and aided in the growth of the bacteria. Examining biofilm structure in relation to the geographic localization of significant biofilm matrix components is possible using microscopy techniques. Confocal laser scanning microscopy (CLSM) is an effective tool for studying biofilms and may be used to quantitatively examine the biofilm matrix and the amount of adhering biomass [52]. Additionally, the spatiotemporal impacts of various nutritional conditions or antibiotic treatments can be observed [53]. In this study, CLSM was used to study the three-dimensional architectural complexity of biofilms in the presence of the oils. The marked reduction in biofilm thickness and biomass following treatment with the oils compared to the $2\%$ DMSO-treated cells could be indicative of the anti-biofilm activity of the oils. Compared to the reference standards, a complete inhibition of biofilm thickness and biomass was observed in the azithromycin-treated cells. This finding corroborates the cell attachment and biofilm formation inhibition assay, as azithromycin was the most effective anti-biofilm agent. However, treatment with the oils, especially AGSUN 5102 CLP, showed activities comparable to cinnamaldehyde-treated cells, where the biofilm thickness and biomass were significantly reduced, which also corroborates the findings of the cell attachment and biofilm inhibition assay. This is a significant finding as it shows that the oils had the ability to turn off the expression of genes responsible for the biofilm matrix formation, thus, reducing the chances of virulence/resistance-forming genes in P. aeruginosa forming. One of the primary factors determining the virulence of P. aeruginosa is pyocyanin, a blue redox-active secondary metabolite that can generate free radicals. Patients with cystic fibrosis frequently experience this substance’s effects, which involve interfering with ion transport and mucus secretion in respiratory epithelial cells [54]. Reactive oxygen species (ROS) produced by pyocyanin significantly impact the development of both acute and chronic respiratory infections and play a crucial role in changing the host immune system [55]. In this study, the observation that the oils significantly reduced the production of pyocyanin in a concentration-dependent manner could be suggestive of their probable anti-pyocyanin effect. At MIC, the fact that both oils reduced pyocyanin in a manner comparable to the reference standards points to their beneficial potential as AQS antimicrobials. This is a significant discovery since pyocyanin synthesis is crucial to the virulence of P. aeruginosa, and the observation noted in this study agrees with the report of Pejčić et al. [ 50], where basil and sage oils significantly decreased pyocyanin production. The potential of P. aeruginosa to colonize various settings through motility influence is another factor supporting its classification as a life-threatening opportunistic pathogen [54]. Motility in P. aeruginosa is regulated by QS, where swimming on a soft surface and swarming on a semisolid surface are made possible by flagella and pili IV [32]. A distinct reduction in the diameter zone following the treatment of P. aeruginosa cells with the oils revealed their remarkable anti-swarming and swimming motility effect relative to the untreated bacterial cells. The anti-swarming and swimming motility effects demonstrated by the oils were comparable to those displayed by the reference standards. Interestingly, this observation is consistent with the report of Pejčić et al. [ 50], who found that the swimming and swarming motility were reduced by sage and basil oils. As the swarming and swimming motility of P. aeruginosa plays a pivotal role in the emergence, development, and upkeep of the biofilm’s structural framework [56], limiting this mobility may lessen the organism’s virulence ability. This is significant as the oils limited the degree of motility in P. aeruginosa in a way that suggested possible interference in the production of virulence factors. Since the mechanism of action of the oils was unknown, they were profiled using chromatographic techniques to analyze the constituents that might be responsible for the AQS potential observed against P. aeruginosa in the in vitro evaluation. The 15 metabolites identified in the oils were essentially fatty acids and vitamins, and the oils could be said to be high linoleic edible oils, as they are rich in polyunsaturated fatty acids (linoleic acids), monounsaturated fatty acids (oleic acid), and others, thus, they are also cholesterol and nutritionally friendly [57]. Emran et al. [ 58] demonstrated in silico docking as a promising method to support findings from in vitro analysis. Upon identifying the constituents, a molecular docking analysis was performed with all the identified constituents to evaluate which of the compounds are active and bind at the catalytic regions of the Las proteins system of P. aeruginosa and the CviR system of C. violaceum. Following molecular docking with all the identified constituents from the oils against all the proteins involved in the Las and CviR systems, the compound(s) with the highest negative binding score and interactions were considered to have the greatest affinity for the respective investigated protein [59]. Additionally, the proteins in each case of the Las and CviR systems that were most receptive to the constituents were taken further for analysis. Specifically, the binding affinities from LasR (Las system) and CviR (CviR) proteins were considered as they had the best affinity for the test metabolites. Phylloquinone having the highest negative score against LasR was an indication of its significant affinity for the protein relative to the other test metabolites. This also holds for linoleic acid and oleic acid as the next-ranked metabolites relative to the reference standards and is suggestive of their higher affinity and interactions against LasR. This observation could be due to the higher number of interactions formed between the metabolites and, most especially, phylloquinone with LasR, which was higher than those formed between the reference standards and LasR. This opinion is consistent with the report of Chen et al. [ 60], where a higher number of interactions and hydrogen bonds of anthocyanins against α–Glucosidase enhanced the affinity of the compound against α–Glucosidase [60]. Furthermore, the top three metabolites interacted with key amino acid residues such as Tyr56, Trp60, Asp73, Ser129 Leu36, Leu40, Tyr47, Val76, and Cys79 in LasR, which is consistent with those reported by Bottomley et al. [ 61] between LasR’s autoinducer (3–oxo–C12–HSL) and LasR at the catalytic site, with phylloquinone interacting more with these amino acids towards LasR. This is indicative that the top three compounds are binding at the catalytic site of LasR and exhibiting similar interactions and traits as LasR’s autoinducer (3–oxo–C12–HSL). This was similarly observed against CviR, with the top-ranked compounds, including phylloquinone, linoleic acid, myristic acid, and lauric acid, having competitive docking scores and interactions relative to the reference standards, with phylloquinone having the best affinity for the receptor, and suggesting phylloquinone as the best inhibitor of the protein. Measuring the ligand’s RMSD from its reference point in the resulting complex following optimum superimposition is one of the most popular methods for assessing the accuracy of docking geometry [62]. Remarkably, confirmatory superimposition analysis of the docking protocols in this study indicates the same binding position with the native inhibitors of LasR and CviR, hence eliminating the selection of any pseudo-positive binding conformations as the greatest energy-minimized posture. However, due to the limitations of molecular docking, which can only be used as a preliminary investigation of a ligand’s affinity for a protein’s binding pocket, an MD simulation over 100 ns was conducted to gain further insight into LasR and CviR protein’s behavior upon the binding of the top-ranked compounds at the catalytic region. This is important to evaluate the residing time of the metabolites and reference standards at the catalytic region by measuring the binding free energy as well as important conformational information regarding the thermodynamic structural stability, flexibility, and compactness of the complexes taken as post-MD simulation indices [63]. The top three compounds from molecular docking were then further taken to MD simulation. The binding free energy estimates the distinction in energy between a complex and its unbound receptor component, and the higher the negative value, the better the affinity of the ligand toward the enzyme [64]. Phylloquinone, linoleic acid, and oleic acid, when bound with LasR at the catalytic region, had higher negative binding free energy values than the reference standards. This finding suggests the top three compounds as promising and better potential inhibitors of LasR, especially phylloquinone which had the highest negative binding free energy value. Moreover, this observation is suggestive of phylloquinone having a higher residence time at the catalytic region of LasR, thus, resulting in an enhancement of activity towards LasR; it also correlates with the results obtained in the molecular docking studies, where phylloquinone was identified as the most promising metabolite. Similarly, against the CviR of C. violaceum, the higher binding free energy values observed with the top four metabolites relative to the cinnamaldehyde point to their advantage as better inhibitors of CviR. While compared to azithromycin, the observation that only phylloquinone and linoleic acid had higher binding free energy values, with phylloquinone having the highest value, demonstrated the benefit of phylloquinone and linoleic acid as an anti-CviR agent, with phylloquinone again having the best inhibitory effect. The RMSD trajectory was analyzed to evaluate the structural stability of the resulting complexes at the catalytic site of LasR over 100 ns and, thus, the stability of a complex structure is shown by its proximity to the unbound structure, which implies a lower RMSD value [64]. As reported by Ramírez and Caballero [65], a desirable and generally acceptable RMSD number should be less than 3 Å. In this study, the complexes converged at around 15 ns and 45 ns; however, after 60 ns, they were all at equilibration and were relatively stable and compact. The LasR deviated from its native conformation upon the binding of ligand compounds accounting for a maximum fluctuation of less than 2.5 Å average RMSD value, suggesting that all the complexes were within the acceptable RMSD limit of less than 3 Å. This observation points to the thermodynamic structural stability of the top-ranked metabolites towards LasR and further enhances their benefit as AQS agents. Interestingly, the phylloquinone–LasR complex, with the highest negative binding free energy, had the lowest mean RMSD value comparable to the unbound LasR, thus, further indicating the advantage of the compound as a potential LasR inhibitor. Similarly, the observation that only linoleic acid among the top-ranked metabolites against CviR caused increased RMSD relative to unbound CviR suggests the thermodynamic structural stability of top-ranked metabolites with CviR. Moreover, all the RMSD values of the top-ranked metabolites and reference standards were less than 3 Å, with myristic acid having the lowest value. The RMSF assesses the fluctuation of the amino residues of LasR and CviR protein and can be related to the stability of the intra- and inter-molecular bonds within the complex, and the lower the fluctuation at the catalytic location, the stronger the binding and affinity of the ligand to the protein [66]. The binding of the investigated metabolites to LasR and CviR led to random fluctuations that arose from possible structural conformation changes. For all the metabolites investigated, the lowest fluctuations at the catalytic region of LasR were generally observed between residues 30 and 70. This indicates that metabolite binding at these residues was stable due to fewer fluctuations. This observation is favorable for the top-ranked metabolites and reference standards as inhibitors of LasR and is consistent with the report of Husain et al. [ 67], where similarly reduced fluctuations in the catalytic region of the protein were observed. This result shows that the three key metabolites from the oils are promising inhibitors of LasR, especially phylloquinone, which is the metabolite that leads to the lowest volatility and flexibility of LasR after binding to the protein, reflecting its increased attractiveness and ability to improve the stability of LasR–amino acid residues. Interestingly, this finding is also coherent with those of the binding free energy. A similar observation was noted for the metabolites and reference standard binding of CviR where, except for linoleic acid and azithromycin, CviR ligand binding caused decreased RMSF. This observation points to the increased attractiveness and ability of the ligands to enhance the stability of CviR amino acid residues. However, in contrast to LasR, where phylloquinone showed the least fluctuation of LasR amino acid residues after its binding, the myristic acid–CviR complex had the least fluctuation. However, phylloquinone–CviR, together with other metabolites and the reference standard binding of CviR, all had an RMSF value that was less than 3 Å, indicating the relatively good fluctuation of the CviR residue after binding of these ligands. The RoG measures how complexes are thermodynamically compact over time, so the lower the value, the more compact the complex is [68]. In this study, the observation that the binding of the top-ranked metabolites and the reference standards to LasR had a negligible effect on the mean RoG values relative to unbound LasR indicates the relative compactness of the complexes. This means that metabolites’ binding had no thermodynamic perturbation effect on LasR. However, it is worth noting the unfolding effect that azithromycin had on LasR between 70 and 80 ns, suggesting lower compactness and stability within this period. As with LasR, the negligible increasing effect on RoG upon CviR binding by the top four metabolites and reference standard might indicate that the thermodynamic geometry of CviR was not perturbed. The SASA is a key thermodynamic stability metric that examines protein folding and surface area changes over the simulation, with larger SASA values indicating an increase in protein volume [66]. The physicochemical properties of the amino acid residues that were rearranged or altered determine the degree of variation in the SASA value [69]. Similar to the RoG results, a higher fluctuation of SASA was observed in the azithromycin-LasR and azithromycin–CviR complex plot over the 100 ns simulation period, suggesting that azithromycin has a larger impact on the surface expansion of LasR and CviR protein. However, the comparable SASA value of the unbound proteins (LasR and CviR) and the top-ranked metabolite complexes shows that the LasR and CviR volume either remains the same or decreases over the course of the simulation, thus suggesting no perturbation in protein following the binding of the top-ranked metabolites and cinnamaldehyde during simulation. In biochemistry, hydrogen bonds are significant interactions as they are crucial for molecular recognition, structural stability, enzyme catalysis, drug partition, and permeability [70,71,72]. Intramolecular hydrogen bonds and distance are important in the stability of a protein structure and, hence, can be assessed to understand the impact of ligand binding on the stability of a protein during simulation [64]. However, the membrane partition and permeability of the medication may be negatively impacted by an excess of hydrogen bond donors or acceptors [73]. In addition to increasing the water desolvation penalty during drug penetration, these polar groups can reduce the attraction for the hydrophobic membrane area [73]. As observed in this study, before and after the top three compounds and reference standards bindings, the stable fluctuation in the pattern of the number of hydrogen bonds produced in LasR and CviR indicates that, following binding to compounds, LasR and CviR thermodynamic entropy was unaffected [19]. In LasR, the decrease in the average number of intramolecular hydrogen bonds created by the top three compounds and controls in complexes with LasR could suggest breakage in some intramolecular hydrogen bonds due to ligand binding. This observation is unlike what was reported by Aribisala and Sabiu [33] and those observed in this study with CviR, where top-ranked metabolites and reference standards binding of CviR caused increased intramolecular hydrogen bonds. The increased intramolecular hydrogen bonds of CviR could be due to the addition of intermolecular hydrogen bonds contributed by ligand binding. However, the high number of intramolecular hydrogen bonds noted with phylloquinone–LasR and phylloquinone–CviR protein corroborates the stability observed with the complex, which implies that there was a high degree of thermodynamic compatibility during the 100 ns simulation duration, which may have caused the higher binding free energy observed with the compound against LasR and CviR. The top-ranked compounds and the reference standards exhibited different bond lengths and numbers of interactions in the active site of LasR and CviR, which were found to have an impact on the free energy of binding observed in this study. The highest number of interactions and hydrogen bonding contacts observed with the phylloquinone–LasR and the phylloquinone–CviR complexes after 100 ns is consistent with the binding free energy observed with the metabolites against the respective target. This observation indicated that a stronger and more stable ligand–protein complex results from more interactions and hydrogen bonding contacts, suggesting greater inhibition in the phylloquinone–LasR and phylloquinone–CviR complexes observed in this study. This conclusion agrees with the observation of Forli et al. [ 74], who showed that the higher the number of interactions, including hydrogen bonding, the higher the inhibitory effect of the inhibitor. Similar observations were noted with the other top-ranked metabolites compared to the reference standards against LasR and CviR, where top-ranked metabolites with higher binding free energy showed a higher number of interactions and hydrogen bonding contacts. In addition to the highest number of hydrogen bonding contacts, phylloquinone–LasR and phylloquinone–CviR complexes had the highest number of important interactions, such as the stacking interaction (stacked), which is one of the most powerful driving forces behind the biological complexation process, and protein folding. Taken together, all of these observations may have contributed to the observed higher affinity and improved stability between phylloquinone and the investigated targets. Since analyzing the number and type of interactions in just one simulation time frame could lead to a false positive conclusion, snapshots at the different time frames of the simulation were taken, and the observation that the consistent number of interactions and some conserved residues exist at 30 ns, 60 ns, and 100 ns during the simulation point to the potential enhanced inhibitory effect on the proteins by the top-ranked metabolites and reference standards during the 100 ns simulation period. To avoid or reduce the tendency of high failure rate during the preclinical and clinical phases of drug development, evaluation of the pharmacokinetics, drug likeliness, synthetic feasibility, and toxicity characteristics of potential therapeutic agents have been identified as a critical step and a prerequisite, and this was undertaken for the top-ranked metabolites in this study. The rule of 5 (RO5), also known as Lipinski’s rule, provides a useful framework to determine whether a tested molecule will be orally accessible and bioavailable [75]. It also affirms that molecules exhibit strong absorption or permeation if they have an octanol/water partition coefficient (log P) < 5, molecular weight (MW) < 500 g/mol, number of hydrogen bond donors (n OH, NH) < 5, and number of hydrogen bond acceptors (n O, N) ≤ 10 [76]. Interestingly, the top-ranked metabolites fulfilling Lipinski’s rule showed their ability to be orally administrable to reach target sites and exert their pharmacological effects, while azithromycin failing the rule suggests its relatively lower tendency to be orally administered and, thus, pinpoints the probable advantage of the top-ranked compounds over azithromycin as drug candidates. However, this does not imply that azithromycin is not a good drug but suggests that it can nonetheless be modified to improve its administration via the oral route, which is mostly preferred [77]. On the other hand, while the low GIT absorption rate of azithromycin and phylloquinone indicates that they are less likely to be absorbed through the GIT, the high GIT absorption rate of linoleic acid and oleic acid indicates their advantage over azithromycin and phylloquinone. Azithromycin, with the lowest bioavailability score, indicates that it is not preferred as an oral drug over cinnamaldehyde, and the top-ranked compounds with higher bioavailability scores mean a high rate of absorption of a drug and the concentration of unchanged drug that reaches the site of action to exert its pharmacological effect. This observation agrees with the findings of Shode et al. [ 78] regarding anti-COVID-19 drug candidates with relatively higher bioavailability scores than the reference standards used. Additionally, phylloquinone and the other top-ranked compounds had bioavailability ratings comparable to cinnamaldehyde, which suggests that they can be used orally and will be bioavailable since cinnamaldehyde is an oral drug. Interestingly, the top-ranked compounds, except phylloquinone, were also water-soluble, suggesting their advantage of being easily transported through the bloodstream compared to azithromycin, which is poorly soluble in water. ## 5. Conclusions The results of this investigation demonstrated the effectiveness of oils in modulating quorum sensing of P. aeruginosa, which subsequently prevented biofilm formation and other virulence factors. Interestingly, the anti-QS potential observed with the oils competed favorably well with conventional drugs used as standard in vitro. Findings from the in silico study revealed linoleic acid, oleic acid, and especially phylloquinone to be mostly responsible for the high anti-QS activity observed with the oils as they were able to bind at the active site of LasR and relatively stable over 100 ns simulation period. Noteworthily, bioprospecting of the oil constituents against the CviR QS system of the biomonitor strain, C. violaceum, revealed, in particular, the ability of phylloquinone and linoleic acid to bind and inactivate the protein, suggesting the broad spectrum AQS capacity of the lead metabolites. Fascinatingly, the top-ranked compounds also passed Lipinski’s rule of five, indicating their drug-likeness properties. Consequently, the top-ranked metabolites of the oils, and most particularly phylloquinone, may function as a potential LasR modulator and constitute a novel therapeutic drug candidate for infections caused by P. aeruginosa. As a result, phylloquinone could be studied further as a QS modulator and perhaps find utility in the development of new therapeutics. ## References 1. Olivares E., Badel-Berchoux S., Provot C., Prévost G., Bernardi T., Jehl F.. Clinical Impact of Antibiotics for the Treatment of Pseudomonas aeruginosa Biofilm Infections. Front. Microbiol. (2020.0) 10 2894. DOI: 10.3389/fmicb.2019.02894 2. Funari R., Shen A.Q.. Detection and Characterization of Bacterial Biofilms and Biofilm-Based Sensors. ACS Sens. (2022.0) 7 347-357. DOI: 10.1021/acssensors.1c02722 3. Tuon F.F., Dantas L.R., Suss P.H., Ribeiro V.S.T.. Pathogenesis of the. Pathogens (2022.0) 11. 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--- title: 'Ethnic differences in response to atypical antipsychotics in patients with schizophrenia: individual patient data meta-analysis of randomised placebo-controlled registration trials submitted to the Dutch Medicines Evaluation Board' authors: - Bram W. C. Storosum - Cedrine Steinz - Sem E. Cohen - Taina Mattila - Wim van den Brink - Kit Roes - Lieuwe de Haan - Damiaan A. J. P. Denys - Jasper B. Zantvoord journal: BJPsych Open year: 2023 pmcid: PMC10044330 doi: 10.1192/bjo.2023.19 license: CC BY 4.0 --- # Ethnic differences in response to atypical antipsychotics in patients with schizophrenia: individual patient data meta-analysis of randomised placebo-controlled registration trials submitted to the Dutch Medicines Evaluation Board ## Body Schizophrenia is a severe mental illness (lifetime prevalence of $0.7\%$), with a huge burden for patients, families and society. Antipsychotic medication is the mainstay of treatment.1 Several clinical and demographic factors have been shown to be negatively associated with treatment response to antipsychotics, including severity of baseline negative symptoms, younger age at onset and the duration of untreated psychosis.2–4 Male patients show a smaller effect size than female patients because of a lower placebo response in female patients.5 To date, little is known about the role of ethnicity on drug treatment in patients with schizophrenia. This is striking because research has shown ethnic differences in the efficacy of other medication. Black patients, for instance, benefit less from beta-blockers and angiotensin-converting enzyme inhibitors than White patients.6 There are also ethnic differences in dosage and prescribing practices.7–10 White patients are more likely to be offered a range of evidence-based treatments for psychosis,11 whereas Black patients have a higher rate of discontinuation of antipsychotic medication,12 are more likely to receive (long-acting) injectable antipsychotics11 and are more susceptible to adverse events such as weight gain and diabetes.13–15 This may be because of ethnic differences in pharmacodynamics or pharmacokinetic differences.16–18 Although ethnic differences in the efficacy of antipsychotics are conceivable through the same pharmacological differences that may explain the higher incidence of adverse events, previous studies have reported conflicting results. Two studies found no significant differences in the efficacy of antipsychotics between White and Black patients with schizophrenia,13,19 whereas one small study (79 White patients and 50 Black patients) showed a lower symptom reduction in White patients compared with Black patients (Positive and Negative Syndrome Scale (PANSS) score reduction of 11.4 v. 28.4, respectively).20 However, none of these studies had a placebo control arm and all were performed as post hoc analyses. ## Abstract ### Background Little is known about the effect of ethnicity on the response to antipsychotic medication in patients with schizophrenia. ### Aims To determine whether ethnicity moderates the response to antipsychotic medication in patients with schizophrenia, and whether this moderation is independent of confounders. ### Method We analysed 18 short-term, placebo-controlled registration trials of atypical antipsychotic medications in patients with schizophrenia ($$n = 3880$$). A two-step, random-effects, individual patient data meta-analysis was applied to establish the moderating effect of ethnicity (White versus Black) on symptom improvement according to the Brief Psychiatric Rating Scale (BPRS) and on response, defined as >$30\%$ BPRS reduction. These analyses were corrected for baseline severity, baseline negative symptoms, age and gender. A conventional meta-analysis was performed to determine the effect size of antipsychotic treatment for each ethnic group separately. ### Results In the complete data-set, $61\%$ of patients were White, $25.6\%$ of patients were Black and $13.4\%$ of patients were of other ethnicities. Ethnicity did not moderate the efficacy of antipsychotic treatment: pooled β-coefficient for the interaction between treatment and ethnic group was −0.582 ($95\%$ CI −2.567 to 1.412) for mean BPRS change, with an odds ratio of 0.875 ($95\%$ CI 0.510–1.499) for response. These results were not modified by confounders. ### Conclusions Atypical antipsychotic medication is equally effective in both Black and White patients with schizophrenia. In registration trials, White and Black patients were overrepresented relative to other ethnic groups, limiting the generalisability of our findings. ## Aims The aim of the current study is to test whether ethnicity moderates the response to antipsychotic medication in patients with schizophrenia, and whether a potential moderating effect is dependent on baseline severity, baseline negative symptoms, age or gender. ## Selection of studies and participants Data were obtained from the double-blind, randomised, placebo-controlled short-term efficacy trials with antipsychotics for the treatment of psychotic episodes in patients with a DSM-III-R or DSM-IV diagnosis of schizophrenia, identified from documentation submitted by pharmaceutical companies to the Dutch regulatory authority for the purpose of marketing authorisation application. These studies ($$n = 22$$, including 5233 patients; 3727 on active medication and 1506 on placebo) were initiated between 1991 and 2004.21 A study period of 6 weeks was chosen for the analysis cut-off point because this is the duration of short-term schizophrenia trials recommended in the European Medicines Agency's Committee for Medicinal Products for Human Use (CHMP) guideline on clinical investigation of medicinal products in the treatment of schizophrenia.22 The Yale University Open Data Access (YODA) Project was consulted for relevant additional individual patient data from high-quality trials, which yielded no additional data (Fig. 1). Fig. 1Interaction of ethnicity × treatment (with main effects) β-coefficients for (a) BPRS change and (b) response. BPRS, Brief Psychiatric Rating Scale. For the current study, the data were compiled with a specific focus on ethnicity. Adults (age 18 years or over) with schizophrenia receiving antipsychotic medications were included as participants and adults with schizophrenia receiving placebo were included as controls. Availability of data on ethnicity was a prerequisite for inclusion. In the database, the following predefined ethnic groups were available: Caucasian, Black, Asian, Oriental, Hispanic, Native American, other. The original individual patient studies were identified for the purpose of collecting manuscripts and corresponding authors were contacted in case of unclarities or missing information (e.g. on the definition of the ethnicity subgroups). In addition, the ethnic subgroups were examined and redefined to the terms American Indian or Alaska Native, Asian, Black, Native Hawaiian or other Pacific Islander, White and some other race, according to current JAMA Network guidelines.23 In the original studies, the term ‘race’ was used, or may have been used interchangeably with ‘ethnicity’. In the current report, however, only the term ethnicity is used because of the controversial implications regarding the term ‘race’ and because the concept of ethnicity encompasses a broader definition.23 To ensure adequately powered statistical analyses, individual studies were excluded if the ethnicity subgroups consisted of fewer than ten patients per study arm (placebo or active medication). The five atypical antipsychotic medications that are currently most frequently prescribed in clinical practice were included. Because of agreements between the Medicines Evaluation Board and the pharmaceutical companies that provided their individual patient data, the names of the compounds cannot be disclosed in this report. To avoid bias from ineffective dosing, only studies with potential effective doses (according to current Summaries of Product Characteristics) of antipsychotic medications were included. We did not perform a formal systematic literature search, because in the design of the current study, only available individual patient data were used. Regarding background information, PubMed, EMBASE and PsycINFO were consulted. Additionally, quoted articles were checked for relevant references. To provide transparent and accurate reporting, the EQUATOR network was consulted for guidelines.24 To determine the risk of bias of the individual studies, the individual patient data-specific extension to the guideline for the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines was followed. A Cochrane Risk of Bias tool was used, and the study protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO; identifier CRD42022327122). ## Outcome measures The main efficacy measure was the Brief Psychiatric Rating Scale (BPRS). The scale consists of 18 items with potential scores ranging from 1 (not present) to 7 (extremely severe), resulting in a total score between 18 and 126. In the original individual patient data, either the BPRS or the PANSS was used for measuring symptoms. When no BPRS data were available, PANSS data were converted into BPRS scores, following the method previously described in the literature.21 When individual BPRS/PANNS item scores were missing, the average of the other subgroup scores of that visit were used. The main outcome measure was the difference between total BPRS scores at baseline and week 6 (defined as BPRS change). As an additional outcome measure, a minimum of $30\%$ reduction of BPRS scores from baseline to week 6 was used (defined as response). The response criterion of $30\%$ is in accordance with the EMA's CHMP guideline.22 When patients dropped out earlier than 6 weeks, the last observation was carried forward to week 6. To examine possible confounding effects, baseline severity, baseline negative symptoms, age and gender were used as covariates in the various statistical models. To compute baseline negative symptoms, three subscale items from the BPRS were used: emotional withdrawal (item 3), motor retardation (item 13) and blunted affect (item 16), as per previous research.21 To provide an impression of the possible influence of the publication date of the individual studies on the results, the studies were ranked in chronological order. ## Statistical analyses A two-step, random-effects, individual patient data meta-analysis was performed. To explore participant-level variations and to control for potential confounders, individual patient data meta-analysis was chosen over study aggregate meta-analysis. Because of existing heterogeneity between studies (e.g. different patient populations, different types of medications and different companies), random-effect instead of fixed-effect models were used.25 First, basic characteristics and outcome measures (BPRS change from baseline and response rates) were calculated. Subsequently, multivariate linear regression analyses were performed with mean BPRS change from baseline as the dependent variable and treatment condition, ethnicity and treatment condition×ethnicity as the independent variables. Similarly, a multivariate logistic regression analysis was performed with response as dependent variable. Thus, in both analyses, the interaction of ethnicity×treatment condition (active medication versus placebo) was added to the main effects (ethnicity and treatment condition) as independent variable as an indicator for a modifier effect of ethnicity on treatment effect. Subsequently, to examine the effect of baseline severity, baseline negative symptoms, age and gender, these variables were cumulatively added as independent variables to the main effects and the interaction of ethnicity by treatment. For these analyses, SPSS version 26 for Windows was used. Subsequently, Comprehensive Meta-Analysis (CMA for Windows, Biostat Inc., USA; https://www.meta-analysis.com/) version 2 software was used to perform random-effect meta-analyses, with outcomes created in the abovementioned linear and logistic regression analyses, i.e. the regression coefficients and odds ratios for the treatment condition×ethnicity interactions. The $95\%$ confidence interval indicates the scope of uncertainty in the effect estimate of the treatment condition×ethnicity interactions, considering heterogeneity between studies. Finally, the treatment effect in the ethnicity subgroups (Black and White) was examined separately. A conventional individual patient data meta-analysis was performed, yielding an overall pooled mean difference in outcomes (BPRS change from baseline and response rates) between participants receiving antipsychotic medication and participants receiving placebo. ## Definitions In the literature, the terms ‘race’ and ‘ethnicity’ are often used interchangeably. According to the Oxford English dictionary, early use of the word ‘race’ was applied to groups of people with obviously distinct physical characteristics such as skin colour. An influential early system dating from 1775 (De Generis Humani Varietati Nativa by J. F. Blumenbach) divided the human species into five races: American, Caucasian, Ethiopian, Malay and Mongolian. This system was based on conformation of the head and skin colour, and assigned the races in qualitative ranking. Because of such early theories and ideologies, the use of the word ‘race’ in reference to specific ethnic groups is avoided in the current literature. Instead, the term ‘ethnicity’ is used for human groups that entertain a subjective belief in their common descent because of similarities of physical type or of customs or both, or because of memories of colonisation and migration. This indicates that the concept of ethnicity encompasses various characteristics, such as genetic profile, culture, migration history, ethnic identity, socioeconomic factors and discrimination. In the current study, the term ‘ethnicity’ will be used rather than ‘race’, even though ‘race’ was used in certain quoted literature and in the databases. Current guidelines of describing specific ethnic groups are followed (e.g. capitalising and using adjectival forms instead of nouns): White and Black are used instead of Caucasian and African American. ## Study population In the original database, seven different ethnic groups were described (Caucasian, Black, Asian, Oriental, Hispanic, Native American and other). Only Black and White groups had enough patients per study arm (n > 10) to perform statistical analyses. The ‘other’ group had patients with mixed ethnicities, rendering them unsuitable to be subdivided into the existing groups. Consequently, four of the 22 eligible studies were excluded from the primary outcome analysis (BPRS change). Of the 18 studies included for analysis, one study had a duration of 4 weeks, 13 studies had a duration of 6 weeks, one study had a duration of 7 weeks and three studies had a duration of 8 weeks. For analyses of response, an additional four studies (out of 18 studies) had to be excluded because there were not enough patients per treatment arm. Based on the Cochrane Risk of Bias Tool, all studies were determined as low risk (see Supplementary Appendix 1 available at https://doi.org/10.1192/bjo.2023.19). Table 1 presents demographic and clinical baseline characteristics for each ethnic group. In total, 3880 patients were included: 1328 Black patients ($34.2\%$) and 2552 White patients ($65.8\%$). There were no relevant baseline differences between groups. For further description of individual studies, please see Supplementary Table 1. Table 1Baseline characteristicsWhiteBlackTotalActive compoundPlaceboTotalActive compoundPlaceboTotaln (%)1818 (46.9)734 (18.9)2552 (65.8)940 (24.2)388 (10.0)1328 (34.2)3880 [100]Age in years, mean (s.d.)38.5 (10.3)38.2 (10.1)38.4 (10.2)38.8 (9.5) 39.2 (10.1) 38.9 (9.7) 38.6 (10.1)Gender, % female25.522.624.723.3 20.6 22.5 23.9Baseline BPRS score, mean (s.d.)55.3 (10.4)55.0 (10.0)55.2 (10.3)54.0 (9.4) 53.0 (8.9) 53.7 (9.2) 54.7 (10.0)Baseline BPRS negative, mean (s.d.)9.7 (3.2)9.5 (3.1)9.6 (3.2)9.5 (3.0) 9.2 (3.0) 9.4 (3.0) 9.6 (3.1)BPRS, Brief Psychiatric Rating Scale. ## Effect size difference for antipsychotic treatment and ethnicity Figure 1 presents the results of the individual patient data meta-analysis of the interaction ethnicity×treatment (without adjustment for confounders). The results show no significant overall treatment condition×ethnicity effect, which indicates that ethnicity does not moderate the efficacy of antipsychotic medication. This is represented by an overall pooled β-coefficient of −0.582 ($95\%$ CI −2.567 to 1.412) for mean BPRS change (Fig. 1(a)) (heterogeneity: $Q = 14.71$, d.f. = 17, I2 = $0.00\%$, τ2 = 0.00) and an overall pooled β-coefficient of −0.134 ($95\%$ CI −0.673 to 0.405) for response (Fig. 1(b)) (heterogeneity: $Q = 19$ 565, I2 = $34\%$, τ2 = 0.34), with the latter translating to an odds ratio of 0.875 ($95\%$ CI 0.510–1.499). Addition of confounders to the model produced similar results. Crude pooled data on outcome measures for both ethnicities can be found in Supplementary Appendix 2. An overview of the individual mean β-coefficients, odds ratios and figures of the effect size differences of the cumulatively added confounders are displayed in Supplementary Appendix 3. ## Conventional meta-analyses for the two ethnicity groups Figure 2 shows the overall pooled effect sizes of BPRS change and response separately for Black and White patients. The effect sizes show a statistically significant beneficial effect of active treatment compared with placebo in both White and Black patients. In White patients, this is represented by an overall pooled mean difference of 0.446 ($95\%$ CI 0.317–0.575) for mean BPRS change (Fig. 2(a)) (heterogeneity: $Q = 33.746$, I2 = $49.62\%$, τ2 = 0.04), and an overall pooled odds ratio of 0.269 ($95\%$ CI 0.177–0.362) for response (Fig. 2(b)) (heterogeneity: $Q = 10.587$, I2 = $0.00\%$, τ2 = 0.00). In Black patients, this is represented by an overall pooled mean difference of 0.360 ($95\%$ CI 0.293–0.481) for BPRS change (Fig. 2(c)) (heterogeneity: $Q = 7511$, I2 = $0.00\%$, τ2 = 0.00), and overall pooled odds ratio of 0.233 ($95\%$ CI 0.109–0.356) for response (Fig. 2(d)) (heterogeneity: $Q = 5915$, I2 = $0.00\%$, τ2 = 0.00). Fig. 2Mean difference in outcome of treatment versus placebo, according to ethnicity: (a) BPRS change for White patients, (b) response for White patients, (c) BPRS change for Black patients and (d) response for Black patients. BPRS, Brief Psychiatric Rating Scale. ## Main findings In these individual patient data meta-analyses we did not find a significant effect of ethnicity in the efficacy of antipsychotics for the treatment of White and Black patients with schizophrenia for symptom improvement (BPRS change score) as well as for response. This finding was independent of baseline severity, baseline negative symptoms, age and gender. Furthermore, our results showed that antipsychotics were effective (separation between active compound and placebo) in both Black and White patients separately. Our data-set included more White patients than Black patients ($65.8\%$ White patients compared with $34.2\%$ Black patients) (Table 1). In the complete data-set without the exclusion of non-relevant studies, $61\%$ of patients were White, $25.6\%$ of patients were Black and only $13.4\%$ of patients were Asian, Oriental, Hispanic, Native American or defined as other ethnicity, indicating that White and Black patients were overrepresented in registration trials when compared with the ethnic distribution of the population of the USA. These findings are in line with results from previous trials showing that minorities are underrepresented in clinical trials.26 The uneven distribution of ethnicity in our large sample of patients with schizophrenia supports the need to include more ethnic diverse populations in future clinical trials, to better represent the clinical population. To the best of our knowledge, our study is the first to investigate the efficacy of antipsychotics in different ethnicity groups in a large sample of placebo-controlled trials with a predefined protocol. One possible explanation for our negative findings is that ethnicity encompasses a broad definition including many factors. When investigating ethnic differences, it is not possible in general to control for all factors that may influence differences between ethnic groups. Moreover, although there are some indications that there is a difference in side-effects between different ethnic groups and that genetic ancestry could influence dopamine receptor availability, a review of 51 studies describing side-effects found limited evidence of ethnic differences in the risk of adverse events.13–16 In addition, genetic profiles vary widely within ethnic groups and there is no proof of an underlying explanation for possible differences in the prevalence of side-effects. Another possible explanation for the lack of ethnic efficacy differences in the current study is that there are clinical disparities in diagnosing schizophrenia in different ethnic groups. More specifically, there is evidence that psychiatrists tend to over-diagnose schizophrenia in Black patients compared with White patients, despite the actual prevalence of schizophrenia being equally distributed over different ethnicities.10,27 Overdiagnosis of schizophrenia in Black patients may result in a smaller observable treatment effect of antipsychotic medication and/or a higher placebo response. ## Strengths and limitations The main strength of our study is the inclusion of a large number of individual patient data. All studies included were double-blind, randomised placebo-controlled trials. This increased the reliability and generalisability of our findings, by quantifying the effect modification and accounting for heterogeneity between studies. However, the study also has limitations. Not all provided studies could be included, which could limit the generalisability of our findings. Second, because the enrolment of included studies was between 1991 and 2004, the newest antipsychotic medications were not examined. However, medications included in the current study are still the most prescribed antipsychotics in current clinical practice.28 In addition, because of agreements with pharmaceutical companies, we were not able to examine the different antipsychotics individually. This may be an important limitation since in antihypertensives, there is an inter-ethnic variance of effectiveness between different medication classes.29 The fact that we could not examine the antipsychotics individually may mask possible ethnic differences for specific antipsychotic medications. Although six different confounders were investigated, not all information about possible relevant confounders was available in the data-set. For example, the duration of untreated psychosis and the age at onset of the disorder are negatively correlated with treatment outcome in schizophrenia 2,3 and these data were not available in our data-set. Moreover, there was no information about the degree of therapy adherence, which has previously been shown to be lower in Black patients and could have resulted in a smaller treatment effect.12 Because of a lack of complete information on the inclusion date of patients, we were not able to examine the inclusion date as a confounder to the model. However, the forest plots were sorted based on publication date of the studies (with ascending date ranging from 1990 to 2004). When viewing these forest plots, there does not seem to be an influence of publication date on the moderating effect of ethnicity. ## Implications Our findings confirm the presence of equal efficacy of antipsychotic medications in both White and Black patients with schizophrenia. Our findings also show that different ethnicities are not evenly distributed in large clinical trials, emphasising the need for more diversity in research, to ensure a more representative distribution of ethnic groups. ## Data availability Restrictions apply to the availability of the data that support the findings of this study. Data are available from the authors with permission from the pharmaceutical companies. ## Author contributions B.W.C.S., J.B.Z. and C.S. conceived of the study. B.W.C.S., C.S., W.v.d. B., L.d. H. and J.B.Z. contributed to the study design. B.W.C.S, C.S., T.M., W.v.d. B. and D.A.J.P.D. contributed to data collection. B.W.C.S., J.B.Z., C.S., S.E.C., W.v.d. B. and K.R. contributed to data analysis. All authors contributed to data interpretation. B.W.C.S. and C.S. wrote the first draft of the manuscript. All authors provided critical feedback on the draft manuscript, and read and approved the manuscript before submission for publication. ## Funding This research received no specific grant from any funding agency, commercial or not-for-profit sectors. ## Declaration of interest None. ## References 1. Hasan A, Falkai P, Wobrock T, Lieberman J, Glenthoj B, Gattaz WF. World Federation of Societies of Biological Psychiatry (WFSBP) guidelines for biological treatment of schizophrenia, part 1: update 2012 on the acute treatment of schizophrenia and the management of treatment resistance. World J Biol Psychiatry (2012) 13 318. PMID: 22834451 2. Kao YC, Liu YP. Effects of age of onset on clinical characteristics in schizophrenia spectrum disorders. BMC Psychiatry (2010) 10 63. PMID: 20718964 3. Marshall M, Lewis S, Lockwood A, Drake R, Jones P, Croudace T. Association between duration of untreated psychosis and outcome in cohorts of first-episode patients: a systematic review. 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--- title: Anti-Biofilm Efficacy of Commonly Used Wound Care Products in In Vitro Settings authors: - Matthew Regulski - Matthew F. Myntti - Garth A. James journal: Antibiotics year: 2023 pmcid: PMC10044339 doi: 10.3390/antibiotics12030536 license: CC BY 4.0 --- # Anti-Biofilm Efficacy of Commonly Used Wound Care Products in In Vitro Settings ## Abstract Considering the prevalence and pathogenicity of biofilms in wounds, this study was designed to evaluate the anti-biofilm capabilities of eight commercially available wound care products using established in vitro assays for biofilms. The products evaluated included dressings with multiple delivery formats for ionic silver including nanocrystalline, gelling fibers, polyurethane (PU) foam, and polymer matrix. Additionally, non-silver-based products including an extracellular polymeric substance (EPS)-dissolving antimicrobial wound gel (BDWG), a collagenase-based debriding ointment and a fish skin-based skin substitute were also evaluated. The products were evaluated on Staphylococcus aureus and Pseudomonas aeruginosa mixed-species biofilms grown using colony drip flow reactor (CDFR) and standard drip flow reactor (DFR) methodologies. Anti-biofilm efficacy was measured by viable plate counts and confocal scanning laser microscopy (CSLM). Four of the eight wound care products tested were efficacious in inhibiting growth of new biofilm when compared with untreated controls. These four products were further evaluated against mature biofilms. BDWG was the only product that achieved greater than 2-log growth reduction (5.88 and 6.58 for S. aureus and P. aeruginosa, respectively) of a mature biofilm. Evaluating both biofilm prevention and mature biofilm disruption capacity is important to a comprehensive understanding of the anti-biofilm efficacy of wound care products. ## 1. Introduction Wound biofilm formation can begin through ubiquitously present, endogenous, or exogenous microbes that attach to the wound surface and proliferate [1]. Under ordinary circumstances, the host immune system can fight bacterial growth from free-floating or planktonic bacteria [1,2,3]. However, in an immunocompromised patient or when bacterial growth is uninhibited, the bacteria multiply and build a complex community protected by a matrix of extracellular polymeric substances (EPS) known as the biofilm matrix [4,5,6]. The presence of biofilm is medically recognized as a leading cause of chronicity, with the Center for Disease Control and Prevention (CDC) estimating that biofilms are responsible for over $65\%$ of all chronic bacterial infections, while the National Institutes of Health (NIH) estimating it at around $80\%$ of microbial infections [7,8]. Polymicrobial biofilms are pervasive in most chronic wounds. Mixed-species bacterial communities encased within the EPS exhibit intrinsic tolerance to antibiotics, antiseptics, and antimicrobials [9,10,11,12]. Biofilms also exhibit varied defense mechanisms against environmental stresses and host immune responses and engender secretion of inflammatory mediators that can impede the natural wound healing cascade while sustaining the biofilm [13,14,15,16,17,18]. Research indicates that the key to treating a biofilm is to break down the protective EPS, a structurally strengthened complex of cross-linked polysaccharide polymers linked with metallic ions and containing microbial and host proteins and nucleic acids [19,20,21,22,23,24,25,26,27,28,29,30,31]. Despite the accepted pathogenicity of biofilms in wounds, there are limited objective studies assessing the efficacy of commonly used wound treatment products on preventing biofilm formation, as well as disrupting mature biofilm. This in vitro study sought to facilitate an understanding of the anti-biofilm efficacy of commonly used wound care products using established assays and techniques for biofilm prevention/inhibition and mature biofilm treatment/disruption [32,33,34,35,36]. Mixed-species biofilms are more common than single-species biofilms in chronic wounds, with S. aureus and P. aeruginosa being the most common [37,38,39,40]. Mixed species biofilms with these two species were thus utilized in this study to simulate the prevention and treatment of polymicrobial biofilm wound infections. Given that antimicrobial silver-based wound dressing products account for almost two-thirds of the antimicrobial wound dressing market with an estimated compound annual growth rate of $12.2\%$ between 2022 and 2029, several advanced silver wound dressing products were included for evaluation [41,42,43]. Additionally, non-silver wound care products with differing mechanisms of action including an EPS disrupting wound gel with antimicrobial activity, an enzymatic collagenase wound debriding product and a fish skin graft product theoretically lacking antimicrobial and EPS disrupting activity were also tested to minimally address the spectrum of wound care products in use. A total of eight commercially available wound care products were tested. All the products tested are either FDA cleared, or FDA approved for use and do not represent an exhaustive list of commercially available wound care products available in the wound care market. ## 2.1. Efficacy of Wound Care Products in Preventing the Formation of New Biofilm in a CDFR Determined by Viable Plate Count The mixed-species biofilm, grown using the CDFR methodology and analyzed by viable plate count methodology indicated that several of the products evaluated were able to impact the formation of biofilm in a statistically significant manner when compared with the untreated control. Figure 1 and Figure 2 demonstrate the recovered colony forming units (CFU) for S. aureus and P. aeruginosa, respectively, from membranes either left untreated (control), treated with saline soaked gauze, or treated with wound care products and treatment efficacy determined by viable plate counts. A Tukey’s t-test of the treatments determined that Nano Ag, BDWG, CMC-Cellulose-$1.7\%$ Ag, and Collagenase were able to impede new biofilm growth of both bacterial species in a statistically significant manner when compared with the untreated control. CMC-$1.2\%$ Ag and Poly-Sheet Metallic Ag were only able to inhibit P. aeruginosa biofilm growth in a statistically significant manner when compared with untreated controls in the mixed-species biofilm prevention assay ($p \leq 0.05$). Saline-soaked gauze, Fish Skin and PU Foam-Ag Salt were comparable to the untreated control, indicating an inability to prevent both S. aureus and P. aeruginosa biofilm growth in this mixed-species in vitro assay. Table 1 presents the log CFU reduction values compared with untreated controls for the mixed-species biofilm prevention assay. Only four of the eight products were able to inhibit growth of S. aureus in the mixed-species biofilm prevention assay with CMC-$1.2\%$ Ag, Fish Skin, PU Foam-Ag Salt, and Poly-Sheet Metallic Ag unable to prevent the biofilm growth of S. aureus in a statistically significant manner compared with the control. Six of the eight products tested were able to inhibit biofilm growth of P. aeruginosa in a statistically significant manner compared with the control. Fish Skin, and PU Foam-Ag Salt were the only products unable to inhibit P. aeruginosa biofilm. ## 2.2. Efficacy of Wound Care Products in Preventing the Formation of New Biofilm in a CDFR Determined by CSLM Imaging To enable for nondestructive, in situ microscopic evaluation of the biofilm matrix and embedded bacteria, the membrane/product biofilm containing pairs from the CDFR assays were evaluated via CSLM. The CSLM assay, which is a multi-step, and technically challenging assay involving staining, cryo-embedding, sectioning, and microscopy, required several experimental runs of CDFR/CSLM to obtain the appropriate images encompassing the cross-sectioned membrane–biofilm–product complex. The nature of some of the products tested also introduced additional difficulties to the CSLM process. For example, Collagenase was hard to slice through when sectioning, resulting in shattering of the gel in several repeated experiments. Once hydrated, CMC-Cellulose-$1.7\%$ Ag experienced cryo-embedding issues due to its thickness. Additionally, in some cases, the applied dressing products such as Poly-Sheet Metallic Ag tended to curl up and separate from the biofilm during sectioning. Figure 3 shows CSLM imaging of cryosections of BacLight™ LIVE/DEAD™ stained membrane/dressing pairs overlaid with transmitted light images. The BacLight kit used to differentiate live and dead cells is composed of two nucleic acid stains: SYTO™9 and propidium iodide. SYTO 9 penetrates all bacterial membranes and stains the cells green, while propidium iodide only penetrates cells with damaged membranes, and the combination of the two stains produces red fluorescing cells. All products were tested for biofilm prevention efficacy in the CDFR with the LIVE/DEAD CSLM assay. Although Poly-Sheet Metallic Ag was evaluated, it is not depicted due to poorly captured images associated with repeated cryo-embedding and sectioning issues. The LIVE/DEAD CSLM images determined that BDWG, Nano Ag and CMC-Cellulose-$1.7\%$ Ag dressings had superior biofilm growth prevention efficacy, as exhibited by the lack or substantially reduced detection of live green fluorescing bacteria within these biofilm cross-sectioned samples. Collagenase and CMC-$1.2\%$ Ag had reduced but detectable green fluorescing bacteria, while the untreated control, gauze, PU Foam-Ag Salt, and Fish Skin treated samples had significant detectable live bacteria in the biofilm. The LIVE/DEAD stained results generally correlated with the viable plate counts in the biofilm prevention CDFR assays. Figure 4 shows CSLM images of cryosections from membrane/dressing pairs after the 24 h treatment period, stained with SYTO™ 9 and Texas Red® conjugated Wheat Germ Agglutinin (WGA). Rather than testing all products with SYTO 9/WGA, representative products with high, medium, and low or no staining in the BacLight LIVE/DEAD assay were evaluated in the SYTO 9/WGA assay. In this assay, SYTO 9 stains both Gram-positive and Gram-negative bacteria nucleic acid (green fluorescence), while WGA binds to poly-N-acetylglucosamine (PNAG) residues present in the typical EPS (red fluorescence). WGA staining in this assay was primarily bacterial cell-associated carbohydrate residues, which accounts for the correlation of SYTO 9 and WGA staining. All the tested treatments had less DNA and carbohydrate compared with the untreated and gauze controls, with BDWG and PU Foam-Ag Salt having the least detected fluorescence among the products tested. The SYTO 9/WGA staining correlated well with both the LIVE/DEAD staining and viable plate counts for majority of the products tested. The exception was the PU Foam-Ag Salt which appeared to show significantly reduced staining with SYTO 9/WGA but ample presence of live bacteria in the viable plate counts and LIVE/DEAD staining, indicating discrepancies between the assays. This discrepancy was attributed to technical staining difficulties with SYTO 9/WGA associated with loss of membrane/product during the CSLM process rather than an inhibitory mode of activity of PU Foam-Ag Salt in the CDFR SYTO 9/WGA assay. ## 2.3. Efficacy of Wound Care Products in Treating Established Mixed-Species DFR Biofilm by Viable Plate Count Biofilm treatment/elimination was evaluated using the DFR model against mature mixed-species biofilms of S. aureus and P. aeruginosa. Only those products that demonstrated efficacy in the biofilm prevention CDFR assay against both bacteria were tested. The exception was Poly-Sheet Metallic Ag which only showed efficacy against P. aeruginosa in the biofilm prevention assay but was tested in the biofilm treatment DFR assays due to the difficulties encountered in evaluating the product in the biofilm prevention CDFR/CSLM/LIVE/DEAD assay. Mixed-species biofilms were grown on hydroxyapatite-coated slides for three days prior to treatment. The treatments were then applied for 24 h. Figure 5 and Figure 6 demonstrate the recovered log CFU/cm2 for S. aureus and P. aeruginosa, respectively from established biofilms either left untreated (control), treated with saline soaked gauze, or treated with wound care products for 24 hrs. Nano Ag and BDWG were efficacious at treating S. aureus entrenched mature biofilm in a statistically significant manner compared with the untreated control. CMC-Cellulose-$1.7\%$ Ag, Poly-Sheet Metallic Ag and Collagenase were ineffective at treating mature biofilm entrenched S. aureus in this in vitro mature biofilm treatment assay. BDWG and Poly-Sheet Metallic Ag were efficacious at treating P. aeruginosa entrenched mature biofilm in a statistically significant manner compared with the untreated control. Nano Ag, CMC-Cellulose-$1.7\%$ Ag, and Collagenase were ineffective at treating mature biofilm entrenched P. aeruginosa in this in vitro mature biofilm treatment assay. Table 2 presents the log CFU reduction values compared with untreated controls for the mixed-species mature biofilm treatment assay. Only Nano Ag and BDWG were able to treat S. aureus in a mature biofilm in a statistically significant manner compared with the control, whereas BDWG and Poly-Sheet Metallic Ag were able to treat P. aeruginosa mature biofilm in a statistically significant manner compared with the control. As shown in Table 2, BDWG was the only product that exhibited greater than 2-log reductions for both microorganisms (5.88 logs for S. aureus and 6.58 logs for P. aeruginosa) and exhibited a statistically significant reduction of viable bacteria in a mature biofilm when compared with untreated control, indicating broad spectrum antimicrobial, and mature biofilm treatment efficacy. ## 3. Discussion Infections caused by antibiotic-resistant bacteria were among the leading causes of death in 2019, with an estimated 4.95 million people dying from illnesses in which antimicrobial resistance (AMR) played a part, and an estimated 1.27 million of those deaths occurring as a direct result of AMR [44]. The presence of biofilm with its inherent AMR characteristics is medically recognized as a leading cause of chronicity of infections, with the Center for Disease Control and Prevention (CDC) estimating that biofilms are responsible for over $65\%$ of all chronic bacterial infections, while the National Institutes of Health (NIH) estimating it at around $80\%$ of microbial infections [7,8]. Bacteria can rapidly form a biofilm on wound surfaces and become tolerant to many traditional antimicrobial treatments through various mechanisms, presenting significant obstacles to clinical intervention of microbial colonization and infection [9,10,11,12,13,14,15,16,17,18]. The AMR develops over time, resulting in mature biofilms that are much more treatment resistant than newly formed biofilms [45,46,47,48]. Most in vitro studies evaluating the efficacy of wound treatment products against biofilms have used single-species biofilms. However, most chronic wound biofilms are polymicrobial, with S. aureus and P. aeruginosa often the most prevalent species [37,39,40]. This study was designed to evaluate both the anti-biofilm preventative and treatment capacity of commonly used wound care products against mixed-species (S. aureus/P. aeruginosa) biofilms in in vitro settings. Wound dressings impregnated with silver are widely used in wound management. To reflect this usage, five commercially available wound dressings with varied forms and concentrations of silver in varied dressing formats were evaluated. Additionally, a Collagenase enzymatic debridement product, an antimicrobial wound gel with EPS disruption capacity (BDWG) and a Fish Skin substitute lacking antimicrobial and anti-biofilm activity were also evaluated with the intent to explore the spectrum of antimicrobial and EPS disruption capacity in commercially available wound care products. Evaluation of the literature has determined that investigations of this combination of wound care products in both a biofilm prevention and mature biofilm treatment mixed-species biofilm CDFR and DFR model, respectively, have not yet been reported. A mixed-species (S. aureus and P. aeruginosa) biofilm prevention assay was evaluated by applying the wound care products to the test surface immediately after mixed-species bacterial inoculation but before the bacteria had time to develop biofilm characteristics. As expected, products lacking antimicrobial agents (Gauze, Fish Skin) behaved as “no-treatment” controls. For S. aureus biofilm inhibition, the silver containing products Nano Ag and CMC-Cellulose-$1.7\%$ Ag and the non-silver-based products Collagenase and BDWG had the most effective results. The silver-based products tested were generally more successful at P. aeruginosa inhibition compared with S. aureus inhibition in the mixed-species biofilm. Silver-based products are known to vary in their effectiveness against bacteria depending on the form, concentration, and release of silver from the dressing, with reports of silver tending to be more effective against Gram negative bacteria compared with Gram positive bacteria [49,50,51,52,53,54,55]. Evaluation of biofilm inhibition efficacy of the tested products using LIVE/DEAD and SYTO 9/WGA staining generally indicated correlations between viable plate counts and CSLM assays. Biofilm prevention capabilities of silver-containing products in this study were also reported in single species biofilm prevention models [56]. Silver-containing dressings absorb wound exudates and associated microorganisms into the dressings to kill the absorbed microorganisms and/or release active silver ions from the dressing into the wound bed. The silver ions either in the dressing or the wound bed bind to proteins and nucleic acids and impede the metabolic and replicative capability of the bacteria [57]. The Collagenase product cleaves denatured collagen at seven specific sites along the denatured collagen strand. It is feasible that in an immature and thin biofilm, the Collagenase is able to access and cleave bacterial collagen structures in the EPS to delay but not completely inhibit biofilm maturation. BDWG is an antimicrobial wound gel composed of a “pH buffer system and benzalkonium chloride surfactant, which destabilizes the biofilm matrix through the chelation of calcium and removes proteins from bacterial membranes causing cell lysis” [58] Compared with biofilm prevention, treatment of mature biofilms presents a much more significant challenge [45,46,47,48]. The DFR method used in this study is an American Society for Testing and Materials (ASTM)-approved method shown to produce biofilms with antibiotic tolerant characteristics similar to a porcine skin explant wound model, a mouse surgical excision wound model, and human clinical data [25,35,47,59]. Primarily, treatments that were efficacious against both bacteria in the mixed-species biofilm prevention assay were subsequently tested in the mixed-species mature biofilm treatment assay. Fewer products were efficacious at treatment of an established biofilm. Despite its theoretical capacity to impact EPS by degrading denatured collagen at several sites, Collagenase appeared unable to treat/disrupt the mixed-species biofilm in this in vitro setting. Nano Ag exhibited significant log reductions of S. aureus, which was consistent with prior publications [52,53], but was not efficacious against established P. aeruginosa, while Poly-Sheet Metallic Ag with its much lower concentration of silver (0.16 mg/in2 of total silver [60]), was only efficacious against P. aeruginosa in the biofilm treatment assays. BDWG designed to mechanistically disrupt EPS as well as lyse a broad spectrum of bacteria was the most effective wound care product tested in this study, with statistically significant mature biofilm treatment capability against both bacterial species. The main goal of this work was to gather in vitro data on the efficacy of various wound care products (commercially available at the time the study was run) against mixed species of bacteria embedded in newly developing and mature biofilms. Using established in vitro biofilm assays, differentiation in the performance of wound care products with just antimicrobial technology (silver-based products) versus antimicrobial with EPS-dissolving technology (BDWG) was clearly evident. Although these in vitro assay results may not necessarily translate to in vivo efficacy, the data may be considered as a foundation to evaluating these products in other in vitro biofilm assays, ex vivo assays, biofilm-based animal models and human clinical trial settings. The primary limitation of this study, as in most in vitro studies, is that no host components were included in the test systems. Continued and in-depth investigations are crucial to determining biofilm prevention and biofilm treatment capabilities of wound care products in use. Understanding which products specifically impact the protective EPS biofilm matrix could enable informed decisions on treatment of biofilm in wounds and potentially alleviate the significant morbidity and mortality associated with biofilm-related complications. ## 4. Materials and Methods To aid identification, the study assigned an alpha character for each control and product tested (A through J). Controls and Test Products:A.Control: Mixed species inoculate left untreated. B.Gauze: Saline saturated gauze was used to simulate sham treatment. C.Nano Ag: An antimicrobial barrier dressing with a nanocrystalline coating of silver that rapidly kills a broad spectrum of bacteria in as little as 30 min. It consists of three layers: an absorbent inner core sandwiched between outer layers of silver coated, low adherent polyethylene net. Nanocrystalline Silver protects the wound site from bacterial colonization while the inner core helps maintain a moist wound environment. D.CMC-$1.2\%$ Ag: The product is a silver-impregnated, antimicrobial, absorbent, sterile, non-woven hydroentangled dressing comprised of Hydrofiber (sodium carboxymethylcellulose). The dressing contains $1.2\%$ w/w ionic silver. The silver in the dressing kills a broad spectrum of wound bacteria. E.BDWG: The product is an antimicrobial wound gel made from citric acid ($3.41\%$) sodium citrate ($3.57\%$), benzalkonium chloride ($0.13\%$), polyethylene glycol, and water buffered to a pH of 4 at an osmolarity of 2330 mOsm/L. The gel provides wound management by maintaining a moist wound environment conducive to wound healing. While in place, the gel can chelate metal ions from EPS causing its disruption and remove proteins from bacterial cell membranes causing their lysis. F.CMC-Cellulose-$1.7\%$ Ag: The product is a non-woven dressing made of sodium carboxymethylcellulose (CMC), cellulose fibers, and silver oxysalts (0.2 mg Ag/cm2) (1.7 wt./wt.). The dressing reportedly kills at least $99.999\%$ of a broad spectrum of bacteria, kills bacteria within a biofilm, and prevents biofilm reformation. G.Fish Skin: The product is composed of intact fish skin and FDA coded as a skin substitute. The intact decellularized fish skin is used for the management of chronic wounds such as diabetic wounds, pressure ulcers, vascular ulcers, and draining wounds. The fish skin sheets contain fat, protein, elastin, glycans, and other natural skin elements. H.PU Foam-Ag Salt: The product is an absorbent dressing made from Polyurethane foam. The outer surface of the foam is bonded to a vapor-permeable PU membrane and contains a silver salt that disperses into the wound fluid and is designed for the management of low to moderately exuding wounds. It may be used on infected wounds. The product has fast (from 30 min, in vitro), sustained (up to 7 days, in vitro), and broad range antimicrobial action (in vitro).I.Poly-Sheet Metallic Ag: The product is a thin-film polymeric sheet composed of polyelectrolyte and polyvinyl alcohol containing ionic and metallic silver. The nanofilm matrix contains a low level of ionic and metallic silver (<25 µg/sq cm) to prevent microbial contamination and colonization of the matrix. J.Collagenase: The product is an enzymatic debridement agent composed of an exogenous bacterial collagenase derived from fermentation by Clostridium histolyticum, with a pH of 6–8. The mechanism of action involves impacting necrotic tissue by cleaving at multiple sites of denatured collagen molecules and effectively removing barriers to healing, enabling wound progression by creating polypeptide bioactive byproducts. Bacterial Strains and Media: Test bacteria included two clinical isolates: methicillin-resistant Staphylococcus aureus (MRSA, MBL Strain 10943) and Pseudomonas aeruginosa (MBL Strain 215), which were initially obtained from the Southwest Regional Wound Care Center in Lubbock, TX. These strains have been used in several wound and biofilm-related studies, are maintained as frozen stock cultures at −80 °C and are available from the Center for Biofilm Engineering (CBE). Inocula were grown overnight from frozen stock cultures in $10\%$-strength brain-heart infusion broth ($10\%$-BHI, Difco™ 237200, Becton Dickenson, Sparks, MD, USA). Prevention of Biofilm Formation using CDFR model: Prevention of biofilm formation was evaluated using the CDFR model, which was developed to mimic the wound environment and evaluate wound dressings [36]. In this model, biofilms are grown on microporous membranes with a continuous supply of nutrients from beneath. Briefly, 2.5 cm diameter absorbent pads (AP1002500, Millipore Sigma, Burlington, MA, USA) were attached to the centers of glass microscope slides (48300-047, VWR International) and placed in a Drip Flow Biofilm Reactor® (DFR 100-6, Biosurface Technologies Corp., Bozeman, MT, USA), which was then sterilized by autoclave. UV-sterilized, 1.3 cm diameter, 0.2 µm pore-size, polycarbonate membranes (GE Water & Process Technologies, Trevose, PA) were then placed on the absorbent pads. The P. aeruginosa culture was diluted 1:10 with phosphate-buffered saline (PBS) and mixed 1:1 with the MRSA culture. Ten µL of the mixture was applied to the centers of the membranes, and after 15 min of drying, treatments were applied to the top of the membrane. For gels, 0.5 mL was applied evenly over the membrane using a syringe. For dressings, a 2.5 cm × 2.5 cm2 was placed over the membrane. The flow of growth medium ($10\%$-BHI) was then supplied at a rate of 5 mL/hr. per channel and the CDFR was incubated at 33 °C for 24 h. After incubation, each membrane/biofilm sample was placed in 10 mL of double-strength Dey-Engley neutralization broth (2X-DE, Becton, Dickinson, and Company, Sparks, MD) in a 50 mL conical tube (Falcon, Corning, NY) to neutralize the treatments. The tubes were then vortexed at high speed for 30 s, sonicated at 60 kHz in an ultrasonic cleaner (Model CSU3HE, Tuttnauer, Hauppauge, NY), and then vortexed again for 30 s to produce a biofilm suspension. At least three independent experiments were run for each treatment. Plate Count Methodology: After biofilm growth in the CDFR or DFR assay, samples were then recovered and neutralized with Dey-Engley neutralization broth (as described above in the Prevention of Biofilm Formation methods) to stop any remaining activity. The biofilm suspensions were serially diluted 10-fold using sterile PBS, and the dilutions were plated for selective counts on Pseudomonas Isolation Agar and Staphylococcus Medium 110 (Becton, Dickinson, and Company, Sparks, MD, USA) using spread-plate and drop-plate methods. After 24–48 h of incubation at 37 °C, the plates were counted, and the log colony-forming units per membrane (Log CFU/membrane) were calculated. Confocal Scanning Laser Microscopy (CSLM): For CSLM, the product/membrane pairs were removed from the reactor, and either stained with the BacLight™ LIVE/DEAD™ Bacterial Viability Kit (Life Technologies, Carlsbad, CA) using 0.5 µL/ mL of the SYTO™ 9 component and 1.5 µL/ mL of the propidium iodide component (final concentrations 1.67 and 30.0 µM, respectively) or left unstained. The LIVE/DEAD™ staining was performed in the dark for 10 min at room temperature. The samples were then cryo-embedded in Optimum Cutting Temperature compound (OCT, Tissue-Tek®, Fisher Healthcare, USA) and stored at −80 °C. Thin sections (10 µm) were cut at −20 °C using a Leica CM 1850 Cryostat (Leica, Wetzlar, Germany) and placed on glass microscope slides. Thin sections from the unstained samples were stained with a solution containing 1.67 µM SYTO™ 9 and 125 µg/ mL of Texas Red® conjugate of wheat germ agglutinin (WGA, Life Technologies, Carlsbad, CA). Imaging was performed with a Leica SP5 confocal scanning laser microscope using a 63× water-immersion objective. For LIVE/DEAD™-stained samples, an excitation wavelength of 488 nm, and emission wave lengths of 500–550 nm and 600–650 nm were used. For the SYTO™ 9/WGA-stained samples, excitation wavelengths of 488 nm and 561 nm and emission wavelengths of 500–550 nm and 600–650 nm were used. Transmitted light images were also collected. The images were processed using Imaris® Oxford Instruments, Abingdon UK) and Metamorph® (Molecular Devices, San Jose, CA). Treatment of Mature Biofilm using DFR model: Treatments that were effective in preventing biofilm formation were further evaluated for efficacy against mature S. aureus and P. aeruginosa mixed-species biofilms established using the Drip Flow Biofilm Reactor® (DFR 100-6, Biosurface Technologies Corp., Bozeman, MT, USA) equipped with hydroxyapatite-coated glass slides. The DFRs were placed in a horizontal position and inoculated with 1.0 mL of the mixed-species inoculum, as described above for the CDFR experiments. The DFRs were then inclined to a 10° angle, and the flow of growth medium ($10\%$-BHI) was supplied at a rate of 10 mL/hr. per channel for three days at 33 °C. For treatment, the flow was halted, the DFRs were placed in a horizontal position, and the gels or products were applied. For the gels, 3.0 mL was applied evenly over the slide using a syringe. Dressings were cut precisely to match the glass slide and placed over biofilm. The DFRs were then incubated for 24 h without flow. The slides were then removed from the DFRs, scraped, rinsed with 10 mL 2X-DE into 50 mL conical vials, and placed in the vial. The vial was then vortexed–sonicated–vortexed, and plate counts were performed, as described in the plate count methodology. At least three independent experiments were run for each treatment. Statistical Design Log reduction (LR) was calculated relative to an untreated control membrane in each experiment. At least three independent experiments were conducted for each treatment in the prevention of biofilm formation assay using the CDFR model and in the treatment of mature biofilm assay using the DFR model. LR mean and standard deviations were calculated from individual experimental values. All individual LR values for each group were compared by ANOVA and Tukey’s post hoc test, and results were deemed significant at a p-values of <0.05 (Minitab 21.1.0). ## 5. Conclusions In this in vitro study, several commercially available wound care products with varying mechanisms of action were tested for mixed-species biofilm prevention and treatment efficacy. The silver containing wound care products Nano Ag and CMC-Cellulose $1.7\%$ Ag, the Collagenase product and the antimicrobial gel-BDWG exhibited statistically significant efficacy in preventing new biofilm formation by both S. aureus and P. aeruginosa. However, the antimicrobial gel BDWG, was the only product that demonstrated statistically significant efficacy in treating mature biofilms of both bacterial species when compared with untreated controls. ## References 1. Percival S.L., McCarty S.M., Lipsky B.. Biofilms and Wounds: An Overview of the Evidence. Adv. Wound Care (2015.0) 4 373-381. DOI: 10.1089/wound.2014.0557 2. 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--- title: Human umbilical cord mesenchymal stem cell-derived exosomes promote murine skin wound healing by neutrophil and macrophage modulations revealed by single-cell RNA sequencing authors: - Yuanyuan Liu - Mingwang Zhang - Yong Liao - Hongbo Chen - Dandan Su - Yuandong Tao - Jiangbo Li - Kai Luo - Lihua Wu - Xingyue Zhang - Rongya Yang journal: Frontiers in Immunology year: 2023 pmcid: PMC10044346 doi: 10.3389/fimmu.2023.1142088 license: CC BY 4.0 --- # Human umbilical cord mesenchymal stem cell-derived exosomes promote murine skin wound healing by neutrophil and macrophage modulations revealed by single-cell RNA sequencing ## Abstract ### Introduction Full-thickness skin wound healing remains a serious undertaking for patients. While stem cell-derived exosomes have been proposed as a potential therapeutic approach, the underlying mechanism of action has yet to be fully elucidated. The current study aimed to investigate the impact of exosomes derived from human umbilical cord mesenchymal stem cells (hucMSC-Exosomes) on the single-cell transcriptome of neutrophils and macrophages in the context of wound healing. ### Methods Utilizing single-cell RNA sequencing, the transcriptomic diversity of neutrophils and macrophages was analyzed in order to predict the cellular fate of these immune cells under the influence of hucMSC-Exosomes and to identify alterations of ligand-receptor interactions that may influence the wound microenvironment. The validity of the findings obtained from this analysis was subsequently corroborated by immunofluorescence, ELISA, and qRT-PCR. Neutrophil origins were characterized based on RNA velocity profiles. ### Results The expression of RETNLG and SLC2A3 was associated with migrating neutrophils, while BCL2A1B was linked to proliferating neutrophils. The hucMSC-Exosomes group exhibited significantly higher levels of M1 macrophages (215 vs 76, $p \leq 0.00001$), M2 macrophages (1231 vs 670, $p \leq 0.00001$), and neutrophils (930 vs 157, $p \leq 0.00001$) when compared to control group. Additionally, it was observed that hucMSC-Exosomes elicit alterations in the differentiation trajectories of macrophages towards more anti-inflammatory phenotypes, concomitant with changes in ligand-receptor interactions, thereby facilitating healing. ### Discussion This study has revealed the transcriptomic heterogeneity of neutrophils and macrophages in the context of skin wound repair following hucMSC-Exosomes interventions, providing a deeper understanding of cellular responses to hucMSC-Exosomes, a rising target of wound healing intervention. ## Introduction Effectively accelerating cutaneous wound healing remains a pressing challenge. Cutaneous wound repair consists of a series of closely linked and overlapping processes involving multiple cells that synergistically regulate inflammation, proliferation, and remodeling. Acute full-thickness skin injury immediately triggers a rapid immune system response, with neutrophils first recruited to the wound in response to signals such as chemokines, calcium, and hydrogen peroxide, serving as the first line of defense against pathogenic microorganisms by releasing proteases, generating neutrophil extracellular traps, and phagocytosis [1]. In the late inflammatory phase, neutrophils are phagocytosed by macrophages mainly through endocytosis. After 2-3 days of injury, monocytes are recruited and transformed into macrophages at the wound site, which fights infection mainly by releasing pro-inflammatory cytokines (IL-6, TNF-α, IL-1B) as well as by phagocytosis of pathogens [2]. Synergistic regulation of neutrophils and immune cells is essential for wound repair. The relationship between the diversity of neutrophils and macrophages within wounds and their differentiation pathways and functions remain unclear. In recent years, mesenchymal stem cells (MSCs) have been widely shown to be effective in promoting wound healing, however, stem cell treatments have a high carcinogenic risk and are prone to complications such as vascular embolism [3, 4]. Exosomes, the main effector of MSCs paracrine production, are thought to have more stable biological effects, higher transduction efficiency, and easier quantification and storage [5]. Studies have shown that human umbilical cord mesenchymal stem cells derived exosomes (hucMSC-Exosomes) can promote wound regeneration and repair by improving immune regulation, stimulating angiogenesis, promoting keratinocyte proliferation, and mediating extracellular matrix remodeling [6, 7]. Although evidence exists regarding the promotion of wound healing by hucMSC-Exosomes, the molecular mechanisms underlying the actions of different cell populations and the interactions between cells have not been consistent in reports (7–9). This is likely due to the diversity of cells involved in wound repair and the complexity of the immune microenvironment. The development of single-cell RNA sequencing (scRNA-seq) technologies has made it possible to study the heterogeneity of cell populations. Single-cell sequencing can identify extreme variability in the expression levels of individual cells within a given cell type, elucidate the unique role of specific cell expression levels on the overall phenotype, and predict the interaction of different cellular ligand-receptor interactions. This study aimed to reveal the heterogeneity of neutrophil and macrophage cells during exosome-induced tissue repair by single-cell sequencing and to reveal the potential mechanisms of hucMSC-Exosome accelerated tissue repair by ligand-receptor interaction analysis. ## Preparation and culture of huc-MSCs The isolation of human umbilical cord tissue-derived mesenchymal stromal cells (huc-MSCs) was executed using a previously established protocol, in which discarded umbilical cords were utilized as the source material [10]. Specifically, the Wharton’s jelly was extracted from the cords post-removal of the vascular structures. The extracted samples were subsequently diced into one cubic millimeter segments, which were then dispersed into a culture medium comprising DMEM-F12 (manufactured by Gbico) supplemented with $10\%$ fetal bovine serum (also from Gbico), $1\%$ penicillin-streptomycin (also from Gbico) and 10 ng/ml basic fibroblast growth factor. The cells were incubated under conditions of 37°C and $5\%$ CO2, and the medium was refreshed at an interval of twice per week. After a period of two weeks, cells with fibroblast-like morphological characteristics could be observed. As the cells reached a confluence level of 70-$80\%$, they were sub-cultured. The study employed only huc-MSCs within passages 3 to 5. ## Exosomes extraction and characterization Upon subjecting pre-confluent huc-MSCs to incubation in a serum-free DMEM medium for a period of 48 hours, the supernatants were then collected and subjected to sequential centrifugation as previously outlined [10], utilizing a centrifugal force of 1,000×g for 10 minutes, 4,000×g for 20 minutes, and 10,000×g for 40 minutes. Subsequently, hucMSC-Exosomes were precipitated through the utilization of ultracentrifugation at a force of 100,000×g for 70 minutes at 4°C using a Beckman Coulter Optima L-80 XP ultracentrifuge. Finally, the resulting solution was re-suspended in PBS before undergoing filtration through a 0.22 µm filter. ## Mice In order to conduct in vivo assays of punch-biopsy wound healing, six-week-old C57/BL6j mice were procured from Charles River Laboratories in Beijing. These mice were maintained under standard conditions, characterized by a temperature range of 22-24°C and a light-dark cycle of 12 hours, with ad libitum access to food and water. The animals were randomly allocated to various experimental groups, and all protocols were granted ethical approval by the institutional committee of Medical School of Chinese People’s Liberation Army prior to initiation of the experiments involving animal subjects. ## Punch-biopsy wound healing models An intraperitoneal anesthetic solution of $1\%$ pentobarbital sodium was used. Prior to the creation of 8-millimeter full-thickness cutaneous lesions in the midline of the shoulder region, the epidermal hairs of the mice were depilated. A sterile punch biopsy needle (Integra Miltex, Integra York, P.A., Inc.) was utilized for the infliction of the wounds. Twenty mice were randomly divided into two groups, which were subcutaneously injected with 100 micrograms of hucMSC-Exosomes or an equal volume of PBS around the wound edges at 4 injection sites, administered every 48 hours. On day 7, the periwound skin (mainly regenerated tissue) were excised for Single-cell RNA sequencing. ## Tissue processing Sterile PBS on ice was used to preserve skin tissue samples immediately after dissection. The samples were subsequently cleaned by PBS twice and cut into pieces sized 2-4 mm. Then tissues were processed following the instructions of the dissociation kit (Multi Tissue Dissociation Kit 1, Miltenyi Biotec, 130-110-201) to complete the dissociation. The single-cell suspension was passed through 70 and 40 μm cell strainers and centrifuged for 10 min, 500 × g at 4 °C. The red blood cell (RBC) lysis solution (130-094-183, Miltenyi Biotec) and Dead Cell Removal Kit (130-090-10, Miltenyi Biotec) were successively added to remove erythrocytes or dead cells. The process resulted in highly viable, typically ≥$85\%$, single-cell suspensions. For immediate single-cell capture, the cells were resuspended in $0.04\%$ Ultra-Pure BSA in PBS (Thermo Fisher Scientific) and concentration was adjusted to ≥106 cells/ml. ## Single-cell RNA sequencing The utilization of microdroplets in conjunction with barcoded primer beads enabled the capture of individual cells through the implementation of a droplet-based ultra-high throughput system for parallel gene expression detection. The Chromium Next GEM Single Cell 3ʹ Kit v3.1, developed by 10x Genomics (Part Number 1000268), was employed in the creation of gene expression (GEX) libraries. The generation of gel bead-in-emulsions, as well as the reverse transcription (RT) of the single-cell suspensions, was accomplished through the utilization of the 10x Genomics Single cell chip (Chromium Next GEM Chip G Single Cell Kit, Part Number 1000120, and an additional Dual Index Kit TT Set A, Part Number 1000215) run on the Chromium Controller (Part Number 110211) developed by 10x Genomics. The generated cDNA was amplified in order to produce GEX libraries after the RT step, subsequently quantified through the use of Qubit 3.0 fluorometer (Life Technologies, Part Number 15387293) and assessed through the utilization of HS DNA chips (Agilent Technologies, Part Number 5067-4627) in combination with the 2100 Bioanalyzer (Agilent Technologies, Part Number G2939BA). The Novaseq 6000, developed by Illumina, was employed for massively parallel sequencing. ## Data processing and analysis Raw scRNA-seq datasets were aligned to the mm10 reference genome using the Cell Ranger V6.1.2 from 10X Genomics Inc. Downstream data processing and visualization were performed using Seurat V4.0.6 [11]. The quality filtering on scRNA-seq data was performed by filtering cells expressing the lower number of genes (<200 genes), and genes only uniquely expressed in <3 cells. The percentage of mitochondrial genes was regressed during the subsequent normalization step. Potential doublets were identified by DoubletFinder V2.0.3 at a threshold of $10\%$ and filtered out [12]. The datasets were then merged and normalized using the SCTransform algorithm with the principal component parameter set to 30. Principal component analysis (PCA) was followed by Uniform Manifold Approximation and Projection (UMAP) analysis to cluster cells with similar gene expression patterns together. Well-established cell marker genes are then used to annotate each cluster. Immune cells (Ptprc+) were computationally separated as described by Vu et al. [ 13]. Non-parametric Wilcoxon Rank Sum test (p Adjusted value = 0.01, Fold Change = 1.2) was used to identify differences between treatment groups. ## Cellular trajectory and RNA velocity analysis Cellular transcriptomic profiles were used as input for Slingshot Package V2.4.0 [14] to predict differentiation trajectory and pseudotime data. The Gene expression matrix of each cell was fed into Tradeseq Package V1.10.0 to calculate the imbalance score, topology test value, and the subsequent differential trajectory between treatment and control [15]. P value < 0.05 was considered significant for the topology test to determine whether treatment and control share a common trajectory. The unsupervised RNA velocity analysis was done with ScVelo [16]. ## Pathways and systems biology analysis Gene Set Enrichment Analysis (GSEA) was performed to better understand the changes in biological pathways with treatment. Genes were sorted based on average log fold change along the trajectories. The result was fed to Fgsea package V1.22.0 [17] in combination with pathway data from MSigDB V7.5.1 [18] to generate a list of relevant enriched pathways. The significance of each biological pathway was determined by the permutation test. The detected biological pathways with p-value <0.05 were considered significant. Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis was performed. Differentially expressed genes were identified with the non-parametric Wilcoxon Rank Sum test (P-value <0.01 and Fold Change >2). R package clusterProfiler V4.4.4 was used to identify influenced pathways [19]. ## Real-time qPCR Total RNA was extracted using Trizol® reagent (Invitrogen) from tissues and cells. The cDNA was obtained using Prime Script™ RT Master Mix (TaKaRa Bio, RR036A). The qRT-PCR was performed according to the instructions for Power SYBR Green PCR Master Mix (Applied Biosystems, USA). Relative expression levels were calculated using the comparative threshold cycle (2-ΔΔCt) method [20]. Statistical significance was determined by two-tail unpaired t-tests, with p-values smaller than 0.05 considered significant. The GAPDH was employed as the internal control. The primer sequence is as follows: CCL6: CACCAGTGGTGGGTGCATCAAG/GTGCTTAGGCACCTCTGAACTC CXCL3: TGAGACCATCCAGAGCTTGACG/CCTTGGGGGTTGAGGCAAACTT IL1RN: TGTGCCTGTCTTGTGCCAAGTC/GCCTTTCTCAGAGCGGATGAAG CXCL9: CTGTTCCTGCATCAGCACCAAC/TGAACTCCATTCTTCAGTGTAGCA CXCL10: GGTGAGAAGAGATGTCTGAATCC/GTCCATCCTTGGAAGCACTGCA CXCL16: CCTATGTGCTGTGCAAGAGGAG/CTGGGCAACATAGAGTCCGTCT ## ELISA After the tissues are shredded and homogenized, the detection of cytokines (IL-4, IL-13, IL-17A, and IL-33) from tissue lysates was performed via ELISA according to the manufacturer’s (R&D Systems) instructions. ## Ligand and receptor-based cell interaction analysis NicheNetR V1.1.0, a method combining gene expression data with existing knowledge on ligand-to-target signaling paths to predict ligand-receptor interactions, was used to investigate alterations in the relationship among different cell types caused by exposure to hucMSC-Exosomes [21]. Differential gene expressions are combined with NicheNetR ligand-target weights to compute the Pearson correlation coefficient between ligand-receptor interaction. Ligand-receptor interactions with an expression of less than $10\%$ were filtered out. ## Histological analysis Skin samples were fixed with $4\%$ paraformaldehyde (PFA) to preserve tissue morphology. The fixed samples were subsequently embedded in paraffin and cut into 5-µm sections for histological analysis. Hematoxylin and eosin (H&E) and Masson Trichrome staining were performed using routine procedures. ## Immunofluorescence staining and imaging Skin samples were obtained from mice and subsequently preserved in $4\%$ PFA prior to sectioning into 5-μm slices. These sections were subjected to staining with antibodies against F480 (Cell Signaling Technology, 1:1000), Arg1 (Proteintech, 1:400), LY6G (ThermoFisher, 1:1000), BCL-2 (Bioss,1:200), and SLC2A3 (Proteintech, 1:100). Subsequently, the fluorescent signals of these markers were visualized and captured using a fluorescent inverted microscope. ## Exosome uptake assay HucMSC-Exosomes were mixed with PKH26 and Diluent C (MINI26, Sigma Aldrich) for a duration of 5 minutes. To stop the reaction, $0.5\%$ FBS was added, and the exosomes were then treated with a sucrose solution and subjected to ultracentrifugation in the dark at 120,000 × g for 90 minutes. Labeled hucMSC-Exosomes or PKH26-labeled control were then administered through subdermal injection into mouse periwound tissue, and the samples were collected after 4 and 24 hours. Subsequently, the nuclei of the cells were stained with 4’,6-diamidino-2phenylindole (DAPI; Leagene, China), and images were obtained using a confocal microscope. ## HucMSC-exosomes induce functional changes in neutrophils The isolation and culture of huc-MSCs was performed in adherence to established methods (Figure 1A), resulting in the generation of a monolayer of spindle-shaped cells (Figure 1B). The isolated huc-MSCs were verified for the expression of mesenchymal stem cell markers (CD90 and CD105) and the absence of hematopoietic stem cell markers (CD34 and CD45) via flow cytometry analysis (Figure 1C). Transmission electron microscopy revealed the exosomes to be spherical in shape, with a hypodense center and typical saucer-like structure enclosed by clear membranes (Figure 1D). The particle size distribution of the exosomes was observed to range from 60 to 150 nm in diameter, and the zeta potential was measured to be -22 ± 2.9 mV ($$n = 3$$) (Figure 1E). Furthermore, western blot analysis confirmed the expression of TSG101 and CD63 all of which are widely recognized as biomarkers of exosomes (Figure 1F). Compared to the control group (PBS injection), mice in the hucMSC-Exosomes intervention group had significantly faster healing rates (Figures 1G, H). Increased re-epithelialization were visible in H&E staining with higher collagen content detected through Masson staining (Figure 1I) [22]. The absorption of hucMSC-Exosomes was visualized via in vivo exosome uptake assay at 4h and 24h (Figure 1J). Figure 1: HucMSC-Exosomes accelerate wound healing. (A) Schematic overview of the study design. (B) Microscope image of huc-MSCs. The scale bar is 500 µm. (C) Flow cytometry analysis of HLA, CD29, CD34, CD45, CD73, CD90 and CD105 expression on the huc-MSC surface. (D) Transmission electron microscopy image of hucMSC-Exosomes. Scale bar, 100 nm. (E) Density plot of hucMSC-Exosomes. (F) Western blot of hucMSC-Exosome markers (G) Representative images of wound closure process of hucMSC-Exosomes treatment and PBS control. The scale bar is 4 mm. (H) Wound closure rate of hucMSC-Exosomes treatment and PBS control group. Two-tailed unpaired t-test (n=6). Error bar: mean standard deviation. ns, not significant, p < 0.05, *p < 0.001, *p < 0.0001. (I) H & E and Masson staining of wounds seven days after injury. Scale bar = 1mm. (J) Uptake of hucMSC-Exosome by skin wound in vivo. Scale bar, 500μm. Neutrophils and macrophages play a vital role in the initial phase of wound healing, helping to clear the wound of bacteria and debris and laying the foundation for subsequent tissue repair. To determine the heterogeneity of neutrophils and macrophages in hucMSC-Exosome-induced wound repair, we collected periwound skin from mice on postoperative day (POD) 7 (the most significant time point for difference in healing rate) for scRNA-seq analysis (Figure 1H) [22]. We analyzed samples from two mice randomly chosen from six in the control group (cell count = 12969, 8804) and two mice randomly chosen from six in the hucMSC-Exosomes group (cell count = 10928, 9838) on POD 7. ScRNA-seq data analysis was performed using Seurat version 4.0.6. During the quality control process, we eliminated cells expressing gene counts >3000 or <200, and cells with >$10\%$ mitochondrial genes. DoubletFinder V2.0.3 was used to remove doublets [12]. Subsequently, UMAP coordinates were calculated for each cell, and cells with similar expression profiles were clustered together. 11 immune cell types were identified via well-established marker genes (Figure 2A). These are, in descending order of abundance, Neu: neutrophils (LY6G+, CSF3R+, TREM1+), M1:M1 macrophages (CD80+, CD86+), M2: M2 macrophages (MRC1+), G-T: γδ T cells (IL17A+, CD3G+), NKT: NKT cells (CD3G+, KLRB1B+), Cd4: Cd4+ T cells (CD4+, CD3G+), NK: NK cells (NCR1+), Ost: osteoclast-like macrophages (CTSK+, CD68+), Bas: basophils (MCPT8+, NFIL3+), pDC: plasmacytoid dendritic cells (SIGLECH+), Mast: mast cells (CMA1+, TPSAB1+) (Figure 2B). Abundance ratios were calculated, the hucMSC-Exosomes group had significantly higher M1 macrophages (215 ($1.0\%$) vs 76 ($0.4\%$), $p \leq 0.00001$, chi-square test), M2 macrophages (1231 ($5.9\%$) vs 670 ($3.1\%$), $p \leq 0.00001$, chi-square test), and neutrophils (930 ($4.5\%$) vs 157 ($0.7\%$), $p \leq 0.00001$, chi-square test) (Figure 2C). Figure 2: HucMSC-Exosomes induce functional changes in neutrophils. (A) 2D scatterplot based on UMAP reduction of immune cell coordinates. (B) Stacked violin plot of immune cell annotation markers. (C) Stacked bar plot of immune cell abundance ratio. (D) 2D scatterplot based on UMAP reduction of neutrophil coordinates. (E) Stacked violin plot of neutrophil subcluster annotation markers (F) Stacked bar plot of neutrophil subcluster abundance ratio. (G) GSEA Heatmap of neutrophil differentially expressed genes (DEGs) following hucMSC-Exosomes exposure (H) KEGG Heatmap of neutrophil DEGs following hucMSC-Exosomes exposure. (I) Quantitative RT-PCR analysis of the Ccl6, Cxcl3, and Il1rn mRNA expressions in peri-wound samples. Two-tailed unpaired t-test (n = 6). Error bar: mean standard error. GAPDH was used as the reference gene. p < 0.01, p < 0.001, *p < 0.0001. (J) Merged immunofluorescence staining with BCL-2 (green), LY6G antibody (red) and nuclear staining (DAPI). The scale bar is 500 µm. White square indicate the region of (K). (K) Immunofluorescence staining with BCL-2 or SLC2A antibody (green) and LY6G antibody (red). Nuclear staining (DAPI) and merged images are also shown in the diagram. The scale bar is 200 µm. Upon tissue injury, neutrophils are recruited to the site of the wound where they phagocytose and kill invading microorganisms. They also release various cytokines and enzymes that contribute to the inflammatory response and stimulate the proliferation of other immune cells. Neutrophils in our dataset can be further divided into 2 subclusters: Neu-DS: migrated neutrophils and Neu-OS: proliferated neutrophils (Figure 2D). RETNLG, a gene suppressed in neutrophils in inflamed tissue [23], is the marker specific for the migrated neutrophils. Another marker for migrated neutrophils is SLC2A3, a gene associated with epithelial-mesenchymal transition (EMT) pathways [24]. Conversely, BCL2A1B, an anti-apoptosis gene for neutrophils [25], can be a marker for proliferating neutrophils. Migrated neutrophils had increased expression of CCL6 while proliferating neutrophils produced more CCL3 (Figure 2E). Subtype ratios were similar between groups ($$p \leq 0.16$$, chi-square test) (Figure 2F). The top expressed genes for each neutrophil subtype were illustrated in (Supplementary Figure 1A). We then investigated the functional differences between the two subtypes. GSEA showed that migrated neutrophil favors biological processes such as neutrophil migration, chemotaxis, and extravasation while proliferated neutrophils favor cytoplasmic translation and ribosomal biogenesis (Supplementary Figure 1B). KEGG analysis showed upregulation of JAK-STAT and HIF-1 signaling in migrated neutrophils and NOD and TNF signaling in proliferated neutrophils (Supplementary Figure 1C). When under the influence of hucMSC-Exosomes, migrated neutrophils reduced ribosomal gene expressions (RPS27A, RPS19), upregulated chemokines expression (CXCL3, CCL4), and CD33 expression (Supplementary Figure 1D). CD33 constitutively inhibits the production of pro-inflammatory cytokines such as TNF-α, IL-1β, and IL-8 [26]. Biological processes such as migration as well as responses to interferon alpha, beta, and gamma were elevated with decreased inflammation response after hucMSC-Exosome treatments (Figure 2G). NOD, TGF-β, and HIF-1 signaling were stimulated while JAK-STAT and TNF signaling were suppressed (Figure 2H). Similarly, proliferated neutrophils also decreased expression of ribosomal genes (RPL24, RPL32) after hucMSC-Exosomes exposure, increased chemokines expression (CCL6, CXCL3) and IL1RN expression (Supplementary Figure 1C). CXCL3 and IL1RN are critical for angiogenesis as well as collagen deposition [27, 28]. Collagen metabolic process and ERK cascades were increased with decreased cytoplasmic translation and ribosomal biogenesis (Figure 2G). HIF-1 and autophagy were upregulated while IL17 and TNF signaling were downregulated. Elevated expression of CCL6, CXCL3, and IL1RN was then confirmed via qRT-PCR on the sample (Figure 2I). In order to assess neutrophil distribution within the wound site, we employed immunological co-staining techniques. The treatment with hucMSC-Exosomes was accompanied by a marked increase in neutrophil density, as evidenced by the presence of LY6G on POD 7 (Figures 2J, K). In addition, we observed that BCL2 and SLC2A, although both overlapping with LY6G, were localized to distinct regions within the wound site and exhibited minimal overlap. This suggests that these two markers are indicative of different subtypes of neutrophils. Specifically, BCL2 was found to be concentrated at the upper edge of the wound and displayed a dispersed distribution pattern, while SLC2A was localized to the middle layer and displayed a more focused distribution pattern. These findings suggest that the neutrophils exhibiting these markers have distinct migratory behaviors within the wound site. The temporal dynamics of gene expression during the process of wound healing can be analyzed through the application of RNA velocity and pseudotime analysis, offering insights into the molecular mechanisms underlying tissue repair. To date, there has been a lack of research examining the role of neutrophil activity in skin wound healing utilizing these techniques. We have employed a novel approach that combines trajectory analysis, RNA velocity analysis, and feature analysis within a single set of UMAP coordinates, leveraging recent advancements in single-cell analysis for improved synergy between the analyses. This approach has been endorsed by both the updated Monocle R package and the widely utilized Slingshot R package, both of which are commonly employed in the single-cell analysis [14, 29]. The RNA velocity graph indicated the presence of two distinct flow patterns roughly equal in size, one highly organized flow stream starts from a differentiation point identified via the latent time graph. The organized nature of the cells in the stream signifies the presence of a complete differentiation path, therefore cells in the organized flow stream likely proliferated in the local wound. These are proliferated neutrophils. There is one disorganized flow stream with no clear start and end point. Likewise, disorganizations in the RNA velocity graph mean that most of the cells along the differentiation path were not captured in the sample, hence cells in the disorganized flow stream migrated to the wound site from various other locations (Figure 3A). These are migrated neutrophils. Figure 3: Pseudotime and RNA velocity profiles of neutrophils. (A) Scatterplot of neutrophils RNA velocity graph. (B) Scatterplot for imbalance score for neutrophils. Bright yellow regions indicate that nearby cells share the same condition. Deep blue regions mean that nearby cells are from a mix of the hucMSC-Exosomes group and the control group. (C) Latent time graph based on UMAP reduction of neutrophils; cells are colored based on latent time. (D) Scatter and trajectory plot based on UMAP reduction of neutrophils. (E) Heatmap of gene’s differential expression between conditions along the trajectory. (F) Barplot of the hucMSC-Exosomes groups vs the control group relevant GSEA elevated from differentially expressed genes between conditions along the trajectory. A subsequent analysis of the imbalance score revealed a discrepancy in the disorganized region of the flow plot (Figure 3B). However, the Topology test was unable to confirm the existence of alternate trajectories for the hucMSC-Exosomes group ($$p \leq 0.43$$). The cell trajectories were calculated using the origin point identified through the latent time graph. The lineage of the proliferating neutrophils was successfully identified. The hucMSC-Exosomes treatment produced trajectories that were similar to those of the control group (Figures 3C, D). During the process of differentiation, cellular expression of several markers integral to neutrophil function exhibited significant increases (Figure 3E). In the group treated with hucMSC-Exosomes, numerous chemokines displayed heightened expression at the onset of lineage development, including CXCL16, CXCL9, CCL27A, and CXCL10. However, expressions of these chemokines rapidly decreased along the differentiation trajectories to levels comparable to those observed in the control group. CCL5 expression was observed in the early stages of the trajectory in the hucMSC-Exosomes group, and in the later stages in the control group. This suggests dynamic shifts in neutrophil priorities, as CCL5 plays a crucial role in the recruitment of endothelial cell progenitors [30]. CXCL9, CXCL10, and CXCL16 have been shown to elicit type I immune responses and facilitate re-epithelialization, while CCL27A is critical for reducing inflammation [31, 32]. GSEA for differential expression along the trajectory showed upregulation of wound healing-related aspects like cell migration, vasculature development, and positive regulation of cell differentiation (Figure 3F). ## HucMSC-exosomes induce functional changes in macrophages Macrophages are crucial for effective wound healing as they serve to clear debris, stimulate angiogenesis, and induce inflammation. The diverse subtypes of macrophages play a complex and dynamic role in the wound healing process, coordinating the inflammatory response and promoting tissue repair and remodeling. M2 Macrophages can be further subclustered into SS: steady-state macrophages, M2a: M2a macrophages, and M2c: M2c macrophages (Figure 4A). The M2a cluster was annotated based on its functional expression of FN1. While M2c was annotated based on the expression of IL10 (Figure 4B). The hucMSC-Exosomes group had a higher ratio of M2a (745 ($3.6\%$) vs 282 ($1.3\%$), $p \leq 0.00001$, chi-square test) (Figure 4C). The top expressed genes for each macrophage subtype were illustrated in Figure 4D. Interestingly the IL10-rich cluster also coincides with focused expressions of CCL2, CCL3, CCL4, and CCL5 (Supplementary Figure 3). Figure 4: HucMSC-Exosomes induce functional changes in macrophages. (A) 2D scatterplot based on UMAP reduction of macrophage coordinates. (B) Stacked violin plot of macrophage subcluster annotation markers. (C) Stacked bar plot of macrophage subcluster abundance ratio. (D) Heatmap of top expressed marker of macrophage subclusters. (E) GSEA Heatmap of genes expressed by macrophages. (F) KEGG Heatmap of genes expressed by macrophages. (G) Stacked violin plot of top differentially expressed genes (DEGs) of macrophages following hucMSC-Exosomes exposure. (H) GSEA Heatmap of macrophage DEGs following hucMSC-Exosomes exposure. (I) KEGG Heatmap of macrophage DEGs following hucMSC-Exosomes exposure. (J) Quantitative RT-PCR analysis of the Cxcl9, Cxcl10, and Cxcl16 mRNA expressions in peri-wound samples. Two-tailed unpaired t-test (n = 6). Error bar: mean standard error. GAPDH was used as the reference gene. p < 0.01, p < 0.001, *p < 0.0001. (K) Merged immunofluorescence staining with ARG1 antibody (green), F4/80 antibody (red) and nuclear staining (DAPI). The scale bar is 500 µm. White square indicate the region of (L). (L) Immunofluorescence staining with ARG1 antibody (green) and F4/80 antibody (red). Nuclear staining (DAPI) and merged images are also shown in the diagram. The scale bar is 200 µm. We subsequently examined the functional changes brought by hucMSC-Exosomes exposure. Compared to the rest of the macrophages, M1 polarized macrophages showed increased ribosomal biogenesis, cell-cell adhesion, and decreased migration and activation (Figure 4E). JAK-STAT and AMPK signaling were higher with lower TNF and HIF-1 signaling (Figure 4F). Following exposure to hucMSC-Exosomes, M1 macrophages had increased VEGFA, IL1RN, and CXCL3 expression (Figure 4G, Supplementary Figure 1E), upregulated collagen metabolic process, migration, and ERK cascade (Figure 4H) as well as HIF-1, TNF signaling and autophagy (Figure 4I). Steady-state macrophages had higher cytoplasmic translation and ribosomal biogenesis with lower phagocytosis and inflammatory response (Figure 4E). Autophagy, HIF-1, TNF, and IL17 signaling were lower in steady-state macrophages (Figure 4F). Exposure to hucMSC-Exosomes lowered chemokine production (CXCL2, CXCL9) (Figure 4G, Supplementary Figure 1E), migration, inflammatory response (Figure 4H), TNF, HIF-1 and TGF-β signaling (Figure 4I). M2a macrophage subtype appears to focus on collagen metabolic process, migration, phagocytosis, autophagy, and HIF-1 signaling with lesser ribosomal biogenesis, interferon response, and IL17 signaling (Figures 4E, F). HucMSC-Exosomes triggered upregulation of ARG1, LY6C2, CCL8 (Figure 4G), ECM remodeling, angiogenesis, migration, phagocytosis (Figure 4H), HIF-1, Ras, and IL17 signaling (Figure 4I). M2c macrophages favor chemotaxis, TNF, GPCR, IL17, and Wnt signaling pathways with less NF-κB, collagen biosynthesis, and HIF-1 signaling (Figures 4E, F). HucMSC-Exosomes simulated elevation of CCL2, CCL4, collagen catabolic process, and ECM receptor interaction (Figures 4G–I). Elevations of CXCL9, CXCL10, and CXCL16 expression were verified with qRT-PCR (Figure 4J). Immunological co-staining was utilized to evaluate the occurrence of periwound macrophages. Both the control group and the hucMSC-Exosomes group exhibited a dispersed pattern of macrophages (Figures 4K, L). The presence of macrophages was notable in both groups, but with significantly higher density in the hucMSC-Exosomes group as demonstrated by F$\frac{4}{80}$ staining. ARG1 staining, which visualizes M2 macrophages, revealed a dispersed pattern in the control group and a more focused, localized pattern in the hucMSC-Exosomes group at the base of the wound. These findings suggest that exposure to hucMSC-Exosomes may alter the migratory and functional profile of M2 macrophages. Utilizing RNA velocity in conjunction with pseudotime analysis, we sought to investigate the functional changes in macrophages. By generating the unsupervised latent time graph from RNA velocity, we identified the center region of the UMAP scatterplot as the point of origin for macrophage differentiation (Figure 5A). Furthermore, we did not observe any distinctive expression of M1 or M2 markers in the origin point cluster. By combining latent time analysis with RNA velocity analysis of macrophages, we were able to identify three distinct endpoints of differentiation at the end of the M1, M2a, and M2c clusters (Figure 5B). The flow streams were highly organized and demonstrated a directional stream towards the regions of M1, M2a, and M2c. However, the M2c endpoint was absent from the control group flow stream plot, suggesting a shift in the priorities of differentiation (Figures 5C, D). We used the origin point from the latent time graph as input for subsequent pseudotime analysis, which generated three corresponding trajectories: the M1 Lineage, M2a Lineage, and M2c Lineage (Figure 5E). We then calculated the imbalance score between the control and hucMSC-Exosomes treatments to determine if hucMSC-Exosomes exposure affected the cellular trajectory (Figure 5F). A significant imbalance is visually apparent in the region bordering the M2a cluster and M2c cluster. The existence of an alternate trajectory for the hucMSC-Exosomes treatment was then verified using the Topology test ($p \leq 0.00001$). Figure 5: Pseudotime and RNA velocity profiles of macrophages (A) Latent time graph based on UMAP reduction of M1 and M2 macrophages; cells are colored based on latent time. (B) Scatterplot of macrophages RNA velocity graph. (C) Scatterplot of macrophages RNA velocity graph for the control group. (D) Scatterplot of macrophages RNA velocity graph for the hucMSC-Exosomes group. (E) Scatter and trajectory plot based on UMAP reduction of macrophages. (F) Scatterplot for imbalance score for macrophages. Bright yellow regions indicate that nearby cells share the same condition. Deep blue regions mean that nearby cells are from a mix of the hucMSC-Exosomes group and the control group. (G) Smoother plot of gene expression along trajectories for macrophages. (H) Heatmap of gene’s differential expression between conditions along the M1 trajectory. (I) Heatmap of gene’s differential expression between conditions along the M2a trajectory. (J) Heatmap of gene’s differential expression between conditions along the M2c trajectory. (K) Barplot of the hucMSC-Exosomes groups vs the control group GSEA biological pathways that are significantly (P value <0.05) elevated from differentially expressed genesalong the M2a trajectory. The elevated expression of PLAC8 signifies that the steady-state macrophages were positioned in the lower wound area (Supplementary Figures 1F, G). Interestingly, only roughly half of the M2a macrophages along the M2a trajectory maintained the expression of PLAC8, while its expression was absent from M2c and M1 macrophages, indicating distinct migratory behavior across subcutaneous locations as the macrophage differentiates. Testing the lineages for early drivers of differentiation revealed CTSB, SELENOP, CD14, and ITM2B as potential candidates for differentiation drivers (Figure 5G). These genes have the strongest differences between lineages at the point of lineage divergence. CTSB has been associated with the memory immune response of macrophages against tumors [33]. HIF1α plays a vital role in the complex process of wound healing, by coordinating the expression of various genes involved in angiogenesis, immune cell activation, and inflammation. The expression of HIF1α increased along both lineage M2a and M2c but remained consistently low for the M1 lineage (Figures 5H–J). The HIF1α expression increased in the beginning in both the hucMSC-Exosomes group and control group along the M2a lineage, however, the HIF1α expression for the M2a control group decreased midway. Similarly, HIF1α expression was higher at the latter half of the M2c trajectory for the hucMSC-Exosomes group. Both M2a and M2c trajectories for the hucMSC-Exosomes group end with elevated ARG1 expression, whereas their respective control trajectories plateaued. GSEA enrichment analysis shows an increase in the regulation of T cell activation and differentiation specifically for the M2a lineage in the hucMSC-Exosomes group (Figure 5K). ## HucMSC-exosomes alter wound healing microenvironment through ligand-receptor interaction The role of ligand-receptor interactions in wound healing is of paramount importance, as these interactions serve as the primary means of communication between cells and are essential for coordinating the various stages of tissue repair. In order to evaluate the alteration of the wound microenvironment from the perspective of ligand-receptor interactions, we utilized NicheNetR, a method that integrates gene expression data with existing knowledge on ligand-to-target signaling pathways to predict ligand-receptor interactions [21]. The recent advances in NicheNetR methodology enabled us to perform a differential analysis of ligand-receptor interactions between treatment conditions. The impact of hucMSC-Exosomes on basophils and neutrophils was marked by the interactions of CCL3-CCR1, CCL4-CCR5, IL6-IL6RA, and IL13-IL13RA1 (Figures 6A, B). These interactions facilitated enhanced mobility and recruitment of neutrophils, as well as suppression of excessive neutrophil infiltration and consequent protection against unnecessary tissue damage (2, 34–36). In addition, hucMSC-Exosomes stimulated the lymphatic vessel endothelial cells to augment neutrophil phagocytosis through the NTS-PTAFR and NTS-FPR2 interaction. ( Figure 6B). After hucMSC-Exosomes treatment, basophils released more CSF1, IL4, OSM, and LIF (Figures 6B–D; Supplementary Figures 2, 3). CSF1 regulates macrophage differentiation, proliferation, and survival [37]. IL4 suppresses macrophage pro-inflammatory mediators [38, 39]. OSM stimulates M2 macrophage polarization [40]. LIF stimulates macrophage infiltration [41]. M2 macrophages secreted more IL1RN for competitive inhibition of IL1A and reduce neutrophil-associated inflammation [42]. The significantly higher presence of IL-4, IL-13, IL-17, and IL-33 levels in samples was verified with ELISA (Figure 6E). Figure 6: Ligand-receptor interactions after hucMSC-Exosomes exposure. (A) Circos plot of differential ligand-receptor interaction for neutrophils (receiver). (B) Heatmap illustrating ligand-receptor expression comparison between the control group and the hucMSC-Exosomes treatment group for neutrophils (receiver). (C) Circos plot of differential ligand-receptor interaction for M1 macrophages (receiver). (D) Circos plot of differential ligand-receptor interaction for M2 macrophages (receiver). (E) Cytokine protein levels for IL-4, IL-13, IL-17 and IL-33 measured by ELISA. Data are presented as mean and standard error. Unpaired two-tail t-test. **p<0.00001. ## Discussion The objective of our study was to utilize scRNA-Seq to examine the immune cell heterogeneity involved in wound healing in mice with full-thickness skin lesions that received hucMSC-Exosome treatment compared to controls (which received PBS injections). Our analysis identified a variety of immune cells, including macrophages, γδ T cells, neutrophils, NK cells, basophils, plasmacytoid dendritic cells, and mast cells. The proportions of most cell types were found to be within expected ranges according to previous literature [43, 44]. Notably, we observed an elevation in the proportion of immune cells, particularly M2 macrophages and neutrophils, in wounds treated with hucMSC exosomes. During the inflammatory phase, neutrophils are the first immune cells recruited to the wound to re-establish a temporary barrier against microbial invasion [45]. Neutrophil infiltration is a prerequisite for the transition from inflammation to proliferation and is necessary for high-quality repair with activated neutrophils can also produce neutrophil extracellular traps to trap and eliminate exogenous pathogens [46]. Our data showed the presence of two subtypes of neutrophils, namely, proliferated neutrophils and migrated neutrophils, each with unique chemokine expression and RNA velocity profile. Their presence was verified with immunofluorescence co-staining. RETNLG and SLC2A3 are potential markers to identify migrated neutrophils of cutaneous wounds as BCL2A1B for proliferation neutrophils. This would help the identification and characterization of neutrophils in clinical and research settings. While both types of neutrophils serve critical functions, heterogeneity between migrated neutrophils and proliferating neutrophils in core neutrophil function was observed. The specialized function of migrated neutrophils at wound sites is characterized by enhanced migration, chemotaxis, extravasation, and upregulated HIF-1 signaling. These cells also favor the production of CCL6, which serves to recruit additional macrophages, thereby contributing to the increase in macrophage numbers at the site. In contrast, proliferated neutrophils stimulate tissue renewal through increased translation, proliferation, and NOD signaling. These cells preferentially produce CCL3, which stimulates angiogenesis. When introduced to the system, hucMSC-Exosomes elicit distinct responses in the two subtypes of neutrophils. Proliferated neutrophils further promote tissue regeneration through increased angiogenesis and collagen deposition, while the introduction of hucMSC-Exosomes leads to a reduction in inflammation in migrated neutrophils, thereby protecting against excessive tissue damage [27, 47]. Activated neutrophils can release cytokines to prolong and amplify neutrophil infiltration [48]. Therefore, it is interesting how recruited neutrophils facilitate the recruitment of other immune cells whereas proliferating neutrophils stimulate proliferation. Utilizing recent advances to chart RNA velocity, trajectory, and expression profile on a single set of cellular UMAP coordinates, we identified unique RNA velocity patterns that differentiate the two neutrophil subtypes. Continuous and organized streams in RNA velocity indicate that a significant portion of the cellular lifecycle was captured within the sample, which likely means the cells were proliferated from a common local source. The minimal presence of neutrophils in normal skin tissue suggests that the likely source of these cells is infiltrated, first responders. This finding suggests the existence of a temporal threshold that dictates the genotype of neutrophils. It is possible that either the heterogeneity of neutrophils from various sources or the alteration of the wound microenvironment determines the transition of migrated neutrophils into proliferated neutrophils. The present study introduces a novel annotation technique that utilizes RNA velocity profiles to characterize clusters. This method has proven efficacious in the classification of subclusters and the characterization of cellular origins. It is worth noting that, although neutrophil numbers exhibited the most substantial increase, the predicted trajectories were relatively similar between the control group and the hucMSC-Exosomes group. This finding suggests that neutrophil differentiation and infiltration are more resistant to qualitative changes than quantitative changes in response to the substantial microenvironmental modifications induced by hucMSC-Exosomes. Neutrophils possess a plethora of surface receptors, including GPCR, pattern recognition receptors, Fc receptors, and other receptors that aid in the detection of injury signals such as chemokines, damage-associated molecular patterns, and hydrogen peroxide released from damaged tissue [2]. Among the multitude of potential interactions, ligand-receptor analysis has revealed basophils as the primary mediators of the effect of human umbilical cord mesenchymal stem cell-derived exosomes, with CCL3, IL6, and IL13 identified as the core driving ligands. The transcriptomic profiles of hucMSC-Exosomes were analyzed by Luo, T., et al., the study showed the presence of MIF in the exosomes which contribute to the increased CCL3-CCR1, CCL4-CCR5, and IL6-IL6RA ligand-receptor interaction, leading to enhanced cellular proliferation and survival (49–52). Our findings are in line with these results, which demonstrated the proliferation of neutrophils at the wound site. Furthermore, the anti-inflammatory effects of M2 macrophages extend to neutrophils via the release of IL1RN. However, it must be noted that only a small proportion of identified ligand-receptor interactions have been documented with respect to their impact on wound healing. Thus, further characterization of these interactions is necessary in order to fully comprehend the effect of human umbilical cord mesenchymal stem cell-derived exosomes on the inflammatory response. Macrophages are also important for wound healing and regeneration. 2-3 days after skin damage, macrophages from local tissue and bone marrow accumulate in the wound. Studies have shown that wound healing is delayed in macrophage depletion, as evidenced by reduced angiogenesis and collagen deposition, and reduced growth factor release (53–56). In contrast, increasing the number of macrophages in the wound can significantly accelerate wound healing, as observed in our samples [57]. M1 macrophages are generally considered to be pro-inflammatory macrophages, characterized by the expression of TNF-α, IL-6, and IL-1B, capable of recognizing pathogens and phagocytosing them, as well as promoting MMP synthesis and degrading ECM [1, 58]. In the present investigation, the selected temporal parameter for obtaining samples was POD 7, characterized by the continued proliferation of various cells and the coexistence of inflammation and proliferation, as well as the remarkable acceleration of the healing rate in the experimental group compared to the control group. Our observations are in line with multiple studies, which collectively indicate that hucMSC-Exosomes play a preeminent role at this particular time point [59]. As the inflammation subsides, macrophages transform into an anti-inflammatory phenotype, M2 macrophages, which primarily promote angiogenesis as well as ECM deposition [60]. Chen et al. [ 61] reported that hucMSC-Exosomes can facilitate M2 macrophage polarization with increase Arg1 expression. In line with these results, our investigation revealed increased M2 macrophage number and enhanced expression of genes associated with the anti-inflammatory properties of M2 macrophages. Additionally, our trajectory analysis of macrophage differentiation points to a reinforcement of the M2a macrophage subtype. The highly organized and structured nature of the macrophage RNA Velocity graph, particularly when juxtaposed with that of neutrophils, suggests that the polarization event of macrophages is likely to be localized and emanating from a single, consistent cell source. Through the application of predictive modeling techniques, we hypothesized trajectories of steady-state macrophages differentiating into M1, M2a, and M2c phenotypes. In particular, M2a macrophages have been shown to possess the ability to promote the conversion of fibroblasts into myofibroblasts, thereby increasing collagen deposition, as well as the capacity to transform themselves into fibroblasts and deposit ECM components [62]. On the other hand, M2c macrophages exhibit a fibrinolytic phenotype and are typically observed following re-epithelialization to phagocytose excess neutrophils, stromal cells, and other debris [63, 64]. The variance in RNA velocity between treatment groups suggests that M2c macrophages may exhibit further differentiation into M2a macrophages in the control group while being more inclined to remain as M2c macrophages in the hucMSC-Exosomes group. Predicted trajectories are observed to diverge significantly between conditions, thus indicating that macrophages differentiate differently when exposed to hucMSC-Exosomes. Early differential markers such as CTSB, SELENOP, and CD14 have been identified. Given that polarizations of M2a, M2c, and M1 macrophages are induced by elements that are enriched within the cutaneous wound site, it is not surprising that the presence of hucMSC-Exosomes resulted in a divergence in trajectory for the macrophages. M1, M2a, and M2c differentiation trajectories exhibit distinct behavior while sharing a common starting point. This phenomenon is further compounded by the fact that the hucMSC-Exosomes group and the control group do not share trajectories. Functional analysis demonstrated that exposure to hucMSC-Exosomes led to a decrease in the inflammatory capabilities of M1 macrophages, while both M2a and M2c macrophages acquired an enhanced anti-inflammatory capability along their respective trajectories. Furthermore, shifts in positional markers such as PLAC8 suggest that macrophages exhibit a preference for distinct locations under the influence of hucMSC-Exosomes, which may be explained by altered ligand-receptor interactions of target cells. A study on the proteomics of hucMSC-Exosomes is consistent with our research results on macrophage ligand-receptor interactions, i.e., the dataset (PXD020948, ProteomeXchange Consortium) listed the presence of S100A8/A9 and TGF-β in hucMSC-Exosomes [65]. S100A8/A9 could directly result in elevated interaction between IL6-IL6RA and may stimulate TNF-α, leading to increased expressions of LIF and IL1RN for enhanced cellular proliferation and differentiation (66–70). TGF-β might partially be accountable for the heightened interaction between IL13-IL13RA1 and the expression of CSF1, supporting macrophage differentiation and debris clearance [71, 72]. It is also consistent with our results which showed increased M2 macrophages polarization and activity. Through a functional enrichment analysis of DEGs in macrophages, our investigation revealed that the activation of the Wnt signaling pathway was notably augmented following treatment with hucMSC-Exosomes, alongside the amplification of supplementary pathways such as HIF-1. It is noteworthy that the Wnt pathway holds a pivotal role in modulating various cellular processes, including but not limited to the regulation of cell proliferation and migration, thus establishing its relevance in skin cell homeostasis. As indicated in previous studies, the nuclear translocation of β-catenin, a downstream effector of the Wnt pathway, promotes the aforementioned phenotypes in skin cells [73]. In addition, upregulated TLR4 is believed to be involved in hucMSC-derived exosome-induced M2 macrophage inflammation and repair [74]. Similar to reported hucMSC-Exosomes’ effect on liver, we also detected the elevation of ERK pathway in neutrophils, its promotion of cell survival may be the cause of increase neutrophils [75].However, it is important to note that hucMSC-Exosomes consist of hundreds of proteins, RNAs, and miRNAs, which collectively result in multifaceted effects from complex combinations of multiple molecules (7, 49, 65, 76–78). Therefore, it is improbable that a single bioactive molecule in vivo could replicate the effects of hucMSC-Exosomes in the microenvironments of wounds. The isolation and identification of specific molecules’ functional characteristics would be a significant undertaking and remain a key objective of our future studies. Currently, it is still quite difficult to simulate and verify the functions of specific molecules in the microenvironment of a wound in vitro, which will also be a goal for our future research. The healing of wounds is a complex biological process that involves the coordinated interactions between cytokines and various cells. HucMSC-Exosomes may directly release or induce the secretion of certain cytokines to promote wound healing [79]. Interleukins (ILs) are a family of cytokines that play a key role in the process of wound healing. ILs are involved in a variety of immune and inflammatory responses, including the recruitment of immune cells to the site of injury. In the context of wound healing, ILs have been shown to promote the proliferation and migration of various cell types, including fibroblasts, keratinocytes, and endothelial cells. We observed elevated expression of various ILs including IL-4, IL-13, IL-17, and IL-33 with the result verified with ELISA. IL-4 has been shown to inhibit the production of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-alpha) and interleukin-1 beta (IL-1 beta), which can delay wound healing if present in excess [80]. IL-13 has similar effects to IL-4 with the additional effect of promoting the differentiation of fibroblasts into myofibroblasts, a specialized cell type that is responsible for wound closure by contraction [81]. IL-17 has also been shown to enhance the production of collagen and stimulate the migration of neutrophils [82]. IL-33 enhances the activation and recruitment of immune cells, such as neutrophils and eosinophils [83]. A recent report has identified elevated levels of IL-8 in keratinocytes in response to exosome exposure at the wound site [84]. However, no studies have yet characterized the depth and breadth of interleukins’ involvement in exosome treatments. This phenomenon is most efficiently evaluated through analysis of single-cell level transcriptomic profiles on tens of thousands of cells. The association between hucMSC-Exosomes and ILs has implications beyond wound healing, as it may have potential applications in cancer treatment as well [79]. Revealing the possible role of IL in the microenvironment of wound healing may help in the development of targeted and engineered extracellular vesicles for use in the treatment of wound repair. Through the utilization of a novel and high-throughput sequencing methodology known as single-cell sequencing, this study unveils the distinctive cellular factors and gene expression profiles that drive wound healing subsequent to full-layer skin damage in neutrophils and macrophages stimulated by the hucMSC-Exosomes. Moreover, this work elucidates the global impact on the expression level of such cells. Infiltration of the wound by neutrophils and macrophages is imperative to eliminate pathogens and synergistically regulate the healing process, with each phase necessitating coordination by a multitude of bioactive molecules through mediated cellular interactions. We provide a detailed description of the cellular fate of neutrophils and macrophages under the influence of hucMSC-Exosomes at the single-cell level, highlighting the upregulation of novel targets such as OSM, LIF and CSF1 etc. These molecules represent promising targets for exosome modification, thereby unveiling new prospects for future exosome application in wound repair and tissue regeneration. Additionally, we anticipate that the analysis of ligand-receptor interactions and pseudotime prediction between distinct cells will facilitate the prediction of potential mechanisms by which hucMSC-Exosomes promote wound healing, thus providing new ways for targeted therapy and engineered exosomes for wound repair. ## Data availability statement The datasets presented in this study can be found in online repositories. The names of the repository/repositories and accession number(s) can be found below: GSE224491 (GEO). ## Ethics statement The animal study was reviewed and approved by institutional committee of the PLA School of Medicine. ## Author contributions YYL and RY contributed to conception and design of the study. MZ, KL, LW organized the database. YYL performed the statistical analysis. YYL wrote the first draft of the manuscript. YL, HC, DS, JL, and XZ wrote sections of the manuscript. All authors contributed to the article and approved the submitted version. ## Conflict of interest The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. ## Publisher’s note All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. 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--- title: Abdominal fat and muscle distributions in different stages of colorectal cancer authors: - Jun Han - Xinyang Liu - Min Tang - Fan Yang - Zuoyou Ding - Guohao Wu journal: BMC Cancer year: 2023 pmcid: PMC10044362 doi: 10.1186/s12885-023-10736-2 license: CC BY 4.0 --- # Abdominal fat and muscle distributions in different stages of colorectal cancer ## Abstract ### Background The purpose of this study is to explore the difference of abdominal fat and muscle composition, especially subcutaneous and visceral adipose tissue, in different stages of colorectal cancer (CRC). ### Materials and methods Patients were divided into 4 groups: healthy controls (patients without colorectal polyp), polyp group (patients with colorectal polyp), cancer group (CRC patients without cachexia), and cachexia group (CRC patients with cachexia). Skeletal muscle (SM), subcutaneous adipose tissue (SAT), visceral adipose tissue (VAT), and intermuscular adipose tissue (IMAT) were assessed at the third lumbar level on computed tomography images obtained within 30 days before colonoscopy or surgery. One-way ANOVA and linear regression were used to analyze the difference of abdominal fat and muscle composition in different stages of CRC. ### Results A total of 1513 patients were divided into healthy controls, polyp group, cancer group, and cachexia group, respectively. In the development of CRC from normal mucosa to polyp and cancer, the VAT area of the polyp group was significantly higher than that of the healthy controls both in male (156.32 ± 69.71 cm2 vs. 141.97 ± 79.40 cm2, $$P \leq 0.014$$) and female patients (108.69 ± 53.95 cm2 vs. 96.28 ± 46.70 cm2, $$P \leq 0.044$$). However, no significant differences were observed of SAT area between polyp group and healthy controls in both sexes. SAT area decreased significantly in the male cancer group compared with the polyp group (111.16 ± 46.98 cm2 vs. 126.40 ± 43.52 cm2, $$P \leq 0.001$$), while no such change was observed in female patients. When compared with healthy controls, the SM, IMAT, SAT, and VAT areas of cachexia group was significantly decreased by 9.25 cm2 ($95\%$ CI: 5.39–13.11 cm2, $P \leq 0.001$), 1.93 cm2 ($95\%$ CI: 0.54–3.32 cm2, $$P \leq 0.001$$), 28.84 cm2 ($95\%$ CI: 17.84–39.83 cm2, $P \leq 0.001$), and 31.31 cm2 ($95\%$ CI: 18.12–44.51 cm2, $P \leq 0.001$) after adjusting for age and gender. ### Conclusion Abdominal fat and muscle composition, especially SAT and VAT, was differently distributed in different stages of CRC. It is necessary to pay attention to the different roles of subcutaneous and visceral adipose tissue in the development of CRC. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12885-023-10736-2. ## Introduction Colorectal cancer (CRC) remains a major health burden with high mortality throughout the world. Globally, there are 1.8 million cases and 880,792 deaths from CRC in 2018 [1]. With the changes in lifestyle such as the lack of physical activity, and the increasing prevalence of obesity in recent decades, the incidence of CRC in China has been raised [2]. It is well known that colorectal polyp is a key step in CRC development. Different polyp subtypes lead to cancer development through distinct neoplasia pathways, in which the adenoma-carcinoma pathway contributes up to 60–$70\%$ of all CRC [3]. A number of epidemiological studies have reported an association between the risk for CRC and obesity [4, 5]. Visceral obesity was reported as a risk factor for colorectal adenoma [6]. However, whether adipose tissue is increasing from adenoma to carcinoma is still unclear. Human white adipose tissue is a prominent energy reservoir and can be categorized into subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) [7]. Recent studies have shown that the metabolic characteristics and embryonic origin of SAT and VAT are different [7–9]. Increased VAT is a risk factor for various tumors, including CRC [10, 11]. VAT is also associated with a higher incidence of colorectal adenoma in a dose-dependent manner [12]. On the other hand, the increase of SAT is not associated with CRC and is even negatively associated with CRC in African Americans [13]. In addition, the prognostic value of SAT and VAT is also different in various cancers [14, 15]. Intramuscular adipose tissue (IMAT) is a measure of adipose tissue infiltration in skeletal muscle fibers [16]. IMAT highly correlates with muscle density and can lead to a higher risk of adverse health outcomes [17, 18]. However, the role of IMAT in the development of CRC is unclear. Measurement of waist circumference and body mass index (BMI) are two conventional methods to determine the abdominal fat and muscle composition. However, such methods cannot accurately distinguish SAT and VAT. In recent years, with the application of imaging techniques such as computed tomography (CT) scans, abdominal fat and muscle composition can be accurately segmented to be SAT and VAT as well as IMAT and skeletal muscle (SM) [19, 20]. The third lumbar vertebra (L3) is a common reference point for the estimation of abdominal fat and muscle composition [21, 22]. With its power to use neural networks and convolutional layers to learn the hierarchy of features from a large amount of given data, deep learning systems can be trained to analyze abdominal fat and muscle composition [23, 24]. In a previous study, we have developed a V-Net-Based segmentation deep learning system to segment skeletal muscle and adipose tissues quickly and accurately [15]. It provides a useful method for large-scale calculation of human abdominal fat and muscle composition (SM, SAT, VAT, and IMAT). Cancer cachexia is a common phenomenon of advanced tumors, which is mainly characterized by loss of skeletal muscle and adipose tissues [25]. A large number of studies have shown that skeletal muscle atrophy is an independent prognostic factor of cancer patients with cachexia [26–28]. However, prognostic value of adipose tissue loss in patients with cancer cachexia is still controversial [15, 29, 30]. Given CRC often occurs in obese patients, it is not clear whether CRC patients with cachexia would experience adipose tissue loss as other cancer patients do. In this study, we compared the abdominal fat and muscle composition differences in different stages of colorectal cancer (patients with and without colorectal polyp, CRC patients with and without cachexia), so as to provide evidence for the clinical prevention and treatment of CRC. ## Patients and groups Patients with CRC who underwent surgery in the Department of General Surgery from January 2020 to December 2020 and patients who underwent colonoscopy in Endoscopic Center during this period were selected in Zhongshan Hospital, Fudan University. Inclusion criteria: [1] patients with CRC were pathologically diagnosed as colorectal adenocarcinoma; [2] patients with colorectal polyp were detected by colonoscopy and pathologically confirmed as adenomas; [3] healthy controls were confirmed by colonoscopy and no polyp was found; [4] patients performed abdominal CT scans within 30 days before surgery or colonoscopy. The diagnostic criteria of cancer cachexia referred to the international consensus on cancer cachexia proposed in 2011 as weight loss of more than $5\%$ in the past 6 months [25]. In this study, all patients were divided into 4 groups: healthy controls (patients without colorectal polyp), polyp group (patients with colorectal polyp), cancer group (CRC patients without cachexia), and cachexia group (CRC patients with cachexia). The patient’s age and gender were recorded in all 4 groups of patients. Cancer stages were recorded based on the American Joint Committee on Cancer stage (8th edition) groupings in CRC groups with and without cachexia. The ethics committee of Zhongshan Hospital, Fudan University approved this study. ## Adipose tissue and muscle areas determination of abdominal CT Abdominal CT scans were performed within 30 days before colonoscopy or surgery. CT parameters for each patient were as follows: contrast-enhanced or unenhanced, 120 kVp, and 290 mA. The scanning layer was 1–5 mm thick and ranged from the xiphoid process to pubic symphysis. The areas of SAT, VAT, SM, as well as IMAT, were segmented by previously described method by our team [15]. A representative CT image marked with different parts of adipose tissue and SM was shown in Fig. S1. ## Statistical analyses Categorical variables were described as a number with percentages and were compared using χ2 test. Continuous variables were described as mean with standard deviation and were compared using one-way ANOVA and linear regression. A Bonferroni correction was applied to adjust for multiple comparisons in one-way ANOVA. Univariate and multivariate linear regression were both adopted to evaluate the crude and adjusted difference among different disease statuses. Two-sided tests were used, and a P-value < 0.05 was considered statistically significant. All statistical analyses were carried out with Stata 14.0. ## Characteristics of enrolled patients In this study, we included 483, 503, 399, and 128 patients in the healthy controls, polyp group, cancer group, and cachexia group, respectively. Table 1 showed the characteristics of age, gender, and abdominal fat and muscle composition of patients in each group. Significant differences were detected in age, gender and abdominal fat and muscle composition among the 4 groups ($P \leq 0.05$). The overall distributions of the abdominal fat and muscle composition in different genders were shown in Fig. 1. Table 1Patients’ characteristics of four groupsHealthy($$n = 483$$)Poloyp($$n = 503$$)Cancer($$n = 399$$)Cachexia($$n = 128$$) P Age60.67 ± 11.6262.82 ± 10.5963.03 ± 10.5363.29 ± 11.110.002Male (%)181(37.47)267(53.08)259(65.24)67(54.03)< 0.001SM115.87 ± 27.64122.90 ± 30.00125.21 ± 30.41110.64 ± 25.81< 0.001IMAT11.31 ± 6.5811.44 ± 6.6010.39 ± 5.629.38 ± 6.420.006VAT113.52 ± 64.95133.61 ± 67.00129.15 ± 69.2390.80 ± 61.21< 0.001SAT138.50 ± 53.40138.68 ± 55.29126.93 ± 54.84103.96 ± 55.66< 0.001Note: SAT: subcutaneous adipose tissue, VAT: visceral adipose tissue. IMAT: intramuscular adipose tissue; SM: skeletal muscle Fig. 1Comparison of abdominal fat and muscle composition of four groups (healthy controls, polyp group, cancer group, and cachexia group) according to different genders. A: Comparison of skeletal muscle (SM) areas in four groups. B: Comparison of intramuscular adipose tissue (IMAT) areas in four groups. C: Comparison of subcutaneous adipose tissue (SAT) areas in four groups. D: Comparison of visceral adipose tissue (VAT) areas in four groups. : $P \leq 0.05$ ## Changes of abdominal fat and muscle composition from normal mucosa to polyp and cancer Firstly, we compared the change of abdominal fat and muscle composition in the process of CRC from normal mucosa to polyp and cancer. Due to the obvious difference in abdominal fat and muscle composition between different genders, we compared the changes of each index according to different gender separately. As shown in Fig. 2, we found that there was no significant difference between the SM area and IMAT area among the healthy controls, polyp group, and cancer group in both genders. However, the VAT area of the polyp group was significantly higher than that of the healthy controls both in male (156.32 ± 69.71 cm2 vs. 141.97 ± 79.40 cm2, $$P \leq 0.014$$) and female patients (108.69 ± 53.95 cm2 vs. 96.28 ± 46.70 cm2, $$P \leq 0.044$$). There was no significant difference in VAT area both between the polyp group and cancer group, and between healthy controls and cancer group. There was no significant difference of SAT area between the polyp group and the healthy controls in male (126.40 ± 43.52 cm2 vs. 120.15 ± 48.47 cm2, $$P \leq 0.499$$) and female patients (152.16 ± 63.23 cm2 vs. 149.62 ± 53.25 cm2, $$P \leq 1.000$$). However, SAT area decreased significantly in the male cancer group compared with the polyp group (111.16 ± 46.98 cm2 vs. 126.40 ± 43.52 cm2, $$P \leq 0.001$$), while no such change was observed in female patients (157.62 ± 56.21 cm2 vs. 152.16 ± 63.24 cm2, $$P \leq 1.000$$). These results indicated that SAT begin to lose in male patients after the occurrence of CRC. The P values of these three groups compared with each other were shown in Table 2. Fig. 2Abdominal fat and muscle composition changes in the process of CRC from normal mucosa to polyp and cancer according to different gender. A: Comparison of skeletal muscle (SM) areas in three groups (healthy controls, polyp group, cancer group). B: Comparison of intramuscular adipose tissue (IMAT) areas in three groups. C: Comparison of subcutaneous adipose tissue (SAT) areas in three groups. D: Comparison of visceral adipose tissue (VAT) areas in three groups. : $P \leq 0.05$ Table 2P values of healthy group, polyp group, and cancer group compared with each other by different gendersSMIMATSATVATMFMFMFMF P1 1.0001.0001.0001.0000.4991.0000.0440.014 P2 0.2010.5560.4981.0000.1630.6411.0001.000 P3 0.0650.4160.3351.0000.0011.0000.1800.647Note: P1: polyp group VS. healthy group; P2: cancer group VS. healthy group; P3: cancer group VS. polyp group; SAT: subcutaneous adipose tissue, VAT: visceral adipose tissue. IMAT: intramuscular adipose tissue; SM: skeletal muscle; M: male; F: female ## Abdominal fat and muscle composition difference between different stages of CRC and healthy controls To clarify the abdominal fat and muscle composition characteristics in different stages of CRC, we compared abdominal fat and muscle composition difference between healthy controls and 3 other groups. As shown in Table 3, abdominal fat and muscle composition was compared after adjustment for age and gender. The SM area of the cachexia group was significantly lower than healthy controls, with a mean reduction area of 9.25 cm2 ($95\%$ CI: 5.39–13.11 cm2, $P \leq 0.001$). There was no significant difference of SM area between polyp group and cancer group compared with the healthy controls. Similarly, we found that IMAT area was significantly lower only in the cachexia group, with a decreased area of 1.93 cm2 ($95\%$ CI: 0.54–3.32 cm2, $$P \leq 0.006$$). The SAT area was found slightly higher in the polyp group and lower in the cancer group, and decreased significantly in the cachexia group, with a reduction area of 28.84 cm2 ($95\%$ CI: 17.84–39.83 cm2, $P \leq 0.001$). Interestingly, we found that the VAT area of the polyp group increased significantly by 12.19 cm2 ($95\%$ CI: 4.18–20.20 cm2, $$P \leq 0.003$$) compared to the healthy controls. There was no significant difference of VAT area between the cancer group and the healthy controls, while the VAT area of the cachexia group decreased significantly by 31.31 cm2 ($95\%$ CI: 18.12–44.51 cm2, $P \leq 0.001$) compared to the healthy controls. Table 3Relative changes of abdominal fat and muscle composition in different stages of CRC compared to healthy groupSMIMATSATVATUnivariateMultivariateUnivariateMultivariateUnivariateMultivariateUnivariateMultivariateβ P β P β P β P β P β P β P βPAge-0.73(-0.86,-0.59)< 0.001-0.74(-0.83,-0.65)< 0.0010.20(0.17,-0.23)< 0.0010.20(0.17,0.23)< 0.001-0.40(-0.66,-0.14)0.003-0.37(-0.62,-0.12)0.0040.92(0.60,1.23)< 0.0010.91(0.61,1.21)< 0.001Sex42.77(40.64,44.90)< 0.00143.15(41.17,45.12)< 0.001-1.35(-2.04,-0.67)< 0.001-1.21(-1.87,-0.56)< 0.001-33.38(-38.95,-27.81)< 0.001-33.01(-38.62,-27.39)< 0.00143.16(36.41,49.91)< 0.00142.63(35.89,49.36)< 0.001Healthy0.00---0.00---0.00---0.00---0.00---0.00---0.00---0.00---Polyp7.03 (3.35,10.71)< 0.0012.11(-0.21,4.44)0.0740.14(-0.67,0.95)0.740-0.08(-0.85,0.68)0.8330.18(-6.75,7.11)0.9595.50(-1.18,12.17)0.10620.09(11.65,28.53)< 0.00112.19(4.18,20.20)0.003Cancer9.33(5.17,13,49)< 0.001-1.48(-4.15,1.19)0.277-0.92(-1.86,0.02)0.055-1.06(-1.96,-0.16)0.021-11.57(-19.27,-3.86)0.003-1.29(-8.83,6.24)0.73615.62(6.25,25.00)0.0011.26(-7.78,10.30)0.784Cachexia-5.23(-11.39,0.92)0.095-9.25(-13.11,-5.39)< 0.001-1.93(-3.32,-0.54)0.006-2.29(-3.59,-0.99)0.001-34.54(-46.04,-23.05)0.001-28.84(-39.83,-17.84)< 0.001-22.72(-36.71,-8.73)0.001-31.31,(-44.51,-18.12)< 0.001Note: β(cm2); SAT: subcutaneous adipose tissue, VAT: visceral adipose tissue. IMAT: intramuscular adipose tissue; SM: skeletal muscle; M: male; F: female ## Relative changes of abdominal fat and muscle composition in cachexia group compared to other groups We focused on comparing the changes of abdominal fat and muscle composition in CRC patients with cachexia. By comparing the abdominal fat and muscle composition between the cachexia group and 3 other groups, we found that there were significant differences between the cachexia group and any other groups (Table 4). As shown in Fig. 1, significant difference was detected between cachexia group and other groups, regardless of male and female patients. Most of all, the SAT area of cachexia group deceased 28.84 cm2 ($95\%$ CI: 17.84–39.83 cm2, $P \leq 0.001$), 34.33 cm2 ($95\%$ CI: 23.38–45.27 cm2, $P \leq 0.001$), and 27.54 cm2 ($95\%$ CI: 16.11–38.98 cm2, $P \leq 0.001$), compared with the healthy controls, polyp group, and cancer group, respectively. The VAT area of cachexia group deceased 31.31 cm2 ($95\%$ CI: 18.12–44.51 cm2, $P \leq 0.001$), 43.51 cm2 ($95\%$ CI: 30.36–56.65 cm2, $P \leq 0.001$) and 32.57 cm2 ($95\%$ CI: 18.85–46.29 cm2, $P \leq 0.001$), compared with the healthy controls, polyp group, and cancer group, respectively. A higher extent of loss in VAT was observed in the cachexia group compared with SAT, suggesting that VAT was more metabolically active than SAT in CRC patients with cachexia. Table 4Relative changes of abdominal fat and muscle composition in CRC patients with cachexia compared to other groupsSMIMATSATVATUnivariateMultivariateUnivariateMultivariateUnivariateMultivariateUnivariateMultivariateβPβPβPβPβPβPβPβPAge-0.73(-0.86,-0.59)< 0.001-0.74(-0.83,-0.65)< 0.0010.20(0.17,-0.23)< 0.0010.20(0.17,0.23)< 0.001-0.40(-0.66,-0.14)0.003-0.37(-0.62,-0.12)0.0040.92(0.60,1.23)< 0.0010.91(0.61,1.21)< 0.001Sex42.77(40.64,44.90)< 0.00143.15(41.17,45.12)< 0.001-1.35(-2.04,-0.67)< 0.001-1.21(-1.87,-0.56)< 0.001-33.38(-38.95,-27.81)< 0.001-33.01(-38.62,-27.39)< 0.00143.16(36.41,49.91)< 0.00142.63(35.89, 49.36)< 0.001Healthy5.23(-0.92,11.39)0.0959.25(5.39,13.11)< 0.0011.93(0.54,3.32)0.0062.29(0.99,3.59)0.00134.54(23.05,46.04)< 0.00128.84(17.84,39.83)< 0.00122.72(8.73,36.71)0.00131.31(18.12,44.51)< 0.001Polyp12.24(6.10,18.41)< 0.00111.36(7.52,15.21)< 0.0012.07(0.68,3.46)0.0042.21(0.91,3.50)0.00134.72(23.23,46.22)< 0.00134.33(23.38,45.27)< 0.00142.81(28.82,56.80)< 0.00143.51(30.36,56.65)< 0.001Cancer14.57(8.12,21,09)< 0.0017.76(3.72,11.80)< 0.0011.01(-0.46,2.48)0.1801.22(-0.15,2.60)0.08022.97(11.00,34.95)< 0.00127.54(16.11,38.98)< 0.00138.35(23.77,52.92)< 0.00132.57(18.85,46.29)< 0.001Cachexia0.00---0.00---0.00---0.00---0.00---0.00---0.00---0.00---Note: β(cm2); SAT: subcutaneous adipose tissue, VAT: visceral adipose tissue. IMAT: intramuscular adipose tissue; SM: skeletal muscle; M: male; F: female ## Discussion In this study, by comparing the abdominal fat and muscle composition in patients with and without colorectal polyp, CRC patients with and without cachexia, we found significant differences of abdominal fat and muscle composition in different stages of CRC. Most importantly, VAT area was the largest in patients with colorectal polyp compared to other groups. In CRC patients with cachexia, the areas of SAT, VAT, SM, as well as IMAT were all found decreased significantly compared to other groups. This is the first comprehensive study focused on the of abdominal fat and muscle composition difference during the different stages of CRC progression from normal mucosa to polyp to cancer and cachexia. Although the association between VAT and CRC was controversial, its association with colorectal polyp was quite well established [31, 32]. Various studies have demonstrated that increase in VAT area was an independent risk factor for colorectal polyp [33]. In this study, significant increase of VAT area was found in the patients with polyp compared with the healthy controls both in male and in female. Further research showed that the increase of VAT in female was higher than that in male, suggesting that the increase of VAT was more likely to promote the occurrence of adenoma in female patients. No significant increase of SAT area was observed in patients with polyp in both genders, which was consistent with the previous study [34]. These results suggested that the growth of VAT but not SAT was a risk factor for colorectal polyp, and female patients should pay more attention to visceral obesity. VAT related inflammation was supposed to promote CRC initiation and progression [10]. However, Akay et al. reported that areas of VAT and SAT decreased in CRC patients compared with the healthy controls [35]. In this study, we found that the VAT area in CRC patients without cachexia was slightly higher than that of healthy controls, while slightly lower than that of patients with colorectal polyp. This suggested that the VAT was the largest area in patients with colorectal polyp, and VAT began to decrease when cancer occurs. We also found that the SAT area in CRC patient without cachexia was significantly lower than patients with polyp in male, while slightly higher than patients with polyp in female. These results suggests that SAT and VAT may have different roles during the development of CRC. According to the consensus of cancer cachexia proposed in 2011, patients with cancer cachexia were characterized by muscle loss with or without adipose tissue loss [25]. However, a large number of studies have found that most of cancer patients with cachexia such as gastric cancer and pancreatic cancer were associated with adipose tissue loss [36]. As commonly known, obese patients are more likely to suffer from CRC. When cancer cachexia develops, it is unclear whether adipose tissue will also be significantly decreased on the basis of obesity. In this study, we found both areas of SAT and VAT in patients with cachexia were significantly lower than those of normal patents, polyp patients, and non-cachexia patients. This suggested adipose tissue loss was one of the important characteristics of CRC patients with cachexia. Ebadi et al. reported that loss of VAT precedes SAT in advanced colorectal and cholangiocarcinoma cancer patients [37]. However, SAT, but not VAT, began to lose in male patients without cachexia in this study, while the loss degree of VAT is greater than that of SAT. Although the underlying mechanism is still not very clear, VAT contains more immune cells than SAT, which may promote lipolysis [8]. More attention should be paid to the SAT loss in the early stage of CRC and VAT loss in the late stage of CRC. As one of the most important characteristics of cancer cachexia, the study of SM atrophy and its mechanism was more comprehensive than that of adipose tissue loss [38]. In this study, we compared the difference of SM area in different stages of CRC. There was no significant change of SM area among the healthy controls, polyp group, and cancer group, suggesting that SM atrophy did not exist until the early stage of CRC. SM area began to decrease in patient with cachexia, demonstrating that adipose tissue loss precedes muscle loss in CRC patient. It was also suggested that cancer cachexia might be caused by the interaction of muscle and adipose tissue [39]. To our knowledge, this is the first analysis to investigate the IMAT area in different stages of CRC. Interestingly, we found that the IMAT area was smaller in cachexia patients than that in healthy controls and polyp patient, while was similar with CRC patient without cachexia. Nevertheless, as IMAT is a novel topic in adipose tissue depot, more data is needed to validate our findings. Our study has several limitations. First, this is a retrospective cross-sectional study and has a relatively small cachexia sample size. Second, IMAT area varies greatly among different groups, the final difference in different groups still need to be determined by further research. Thirdly, this study mainly includes two major confounding factors: age and gender. It is unclear whether there are other factors that affect the results. Despite these limitations, we still believe that subcutaneous and visceral adipose tissue play different roles in the different stages of CRC development. ## Conclusion Our research provides important insights into the abdominal fat and muscle composition, especially subcutaneous and visceral adipose tissue, in different stages of colorectal cancer. These findings provide a novel understanding of the association between adipose tissues and CRC. 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--- title: Identifying potential biomarkers for the diagnosis and treatment of IgA nephropathy based on bioinformatics analysis authors: - Xiaohui Li - Mengru Zeng - Jialu Liu - Shumin Zhang - Yifei Liu - Yuee Zhao - Cong Wei - Kexin Yang - Ying Huang - Lei Zhang - Li Xiao journal: BMC Medical Genomics year: 2023 pmcid: PMC10044383 doi: 10.1186/s12920-023-01494-y license: CC BY 4.0 --- # Identifying potential biomarkers for the diagnosis and treatment of IgA nephropathy based on bioinformatics analysis ## Abstract ### Background IgA nephropathy (IgAN) has become the leading cause of end-stage renal disease in young adults. Nevertheless, the current diagnosis exclusively relies on invasive renal biopsy, and specific treatment is deficient. Thus, our study aims to identify potential crucial genes, thereby providing novel biomarkers for the diagnosis and therapy of IgAN. ### Methods Three microarray datasets were downloaded from GEO official website. Differentially expressed genes (DEGs) were identified by limma package. GO and KEGG analysis were conducted. Tissue/organ-specific DEGs were distinguished via BioGPS. GSEA was utilized to elucidate the predominant enrichment pathways. The PPI network of DEGs was established, and hub genes were mined through Cytoscape. The CTD database was employed to determine the association between hub genes and IgAN. Infiltrating immune cells and their relationship to hub genes were evaluated based on CIBERSORT. Furthermore, the diagnostic effectiveness of hub markers was subsequently predicted using the ROC curves. The CMap database was applied to investigate potential therapeutic drugs. The expression level and diagnostic accuracy of TYROBP was validated in the cell model of IgAN and different renal pathologies. ### Results A total of 113 DEGs were screened, which were mostly enriched in peptidase regulator activity, regulation of cytokine production, and collagen-containing extracellular matrix. Among these DEGs, 67 genes manifested pronounced tissue and organ specificity. GSEA analysis revealed that the most significant enriched gene sets were involved in proteasome pathway. Ten hub genes (KNG1, FN1, ALB, PLG, IGF1, EGF, HRG, TYROBP, CSF1R, and ITGB2) were recognized. CTD showed a close connection between ALB, IGF, FN1 and IgAN. Immune infiltration analysis elucidated that IGF1, EGF, HRG, FN1, ITGB2, and TYROBP were closely associated with infiltrating immune cells. ROC curves reflected that all hub genes, especially TYROBP, exhibited a good diagnostic value for IgAN. Verteporfin, moxonidine, and procaine were the most significant three therapeutic drugs. Further exploration proved that TYROBP was not only highly expressed in IgAN, but exhibited high specificity for the diagnosis of IgAN. ### Conclusions This study may offer novel insights into the mechanisms involved in IgAN occurrence and progression and the selection of diagnostic markers and therapeutic targets for IgAN. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12920-023-01494-y. ## Introduction IgA nephropathy (IgAN) is recognized as the most prevalent primary glomerular disease worldwide accompanied by a global incidence rate of more than 2.5 per 100,000 [1, 2]. In addition, IgAN has accounted for the biggest proportion of end-stage renal disease (ESRD) in the young with up to $30\%$ of IgAN ultimately ending up as ESRD within 20 years and demanding renal replacement therapy [3, 4]. Clinically, the common manifestation of IgAN is macroscopic hematuria which frequently follows a mucosal infection, such as an upper respiratory or gastrointestinal infection [1]. However, the heterogeneity of clinical course is so substantial that patients with IgAN may also present highly diverse syndromes ranging from minor urinary abnormalities to rapidly progressive glomerulonephritis [5]. Due to the lack of specific biomarkers, the current gold standard for diagnosis of IgAN exclusively relies on renal biopsy characterized by dominant IgA deposition in the glomerular mesangial region [1]. Among multitudinous concepts regarding the pathogenesis of IgAN, the four-hit theory is widely accepted: excessive abnormal hypogalactosylated IgA1 (Gd-IgA1) are induced by unknown factors and serve as antigen leading to the production of specific autoantibodies, subsequent antigen-antibody complexes take form in the blood circulation, thus finally deposit in the glomerular mesangium [6, 7]. As a result, chronic inflammation and kidney impairment occur [8]. Still, there are a great many underlying mechanisms left unexplored. Regrettably, the main therapies including immunosuppression and supportive care, are still unsatisfactory. Given the invasive diagnosis and unclear mechanisms as well as limited therapies, identifying underlying mechanisms and key biomarkers to provide novel diagnosis and optimal treatment is warranted. In recent years, microarray and bioinformatic analyses have been extensively employed to detect new biomarkers and potential molecular pathogenesis in various diseases but not many in IgAN. This research is committed to screening potential hub genes involved in IgAN, thereby providing novel markers for non-invasive diagnosis and potential targets for treatment of IgAN. Most previous studies applied two datasets to identify some hub genes with or without validation by external validation set. There are several innovation points of this study. Firstly, our study integrated three datasets which contained almost all human IgAN sample currently uploaded to GEO. Secondly, our study applied as many bioinformatics methods as possible to systematically and comprehensively elucidate the underlying mechanisms of IgAN. Thirdly, one hub gene with the highest expression level and the strongest diagnostic ability among identified 10 hub genes was also verified using in vitro and in vivo experiments. Consequently, three microarray data sets from Gene Expression Omnibus (GEO) database were obtained and related expression profiles were extracted. Then, this study identified differentially expressed genes (DEGs) between IgAN patients and the normal controls, and determined tissue- or organ-specific expressed genes via BioGPS. DEGs and all detected genes were subsequently exposed to Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis using clusterProfiler package in R software and Gene set enrichment analysis (GSEA) software respectively. Next, protein-protein interaction (PPI) network was established based on the STRING tool, and key modules and hub genes were screened by Cytoscape. Maximal Clique Centrality (MCC) algorithm in Cytohubba was also utilized to determine the top ten hub genes. Eventually, the intersection of the two results was deemed the final hub genes, out of which several genes were found closely associated with IgAN via the Comparative Toxicogenomics Database (CTD) database. The GO and KEGG functional enrichment of these ten hub genes were investigated through ClueGO and Cluepedia tools in Cytoscape. To verify the identified hub genes, Nephroseq v5 online platform was applied. Further, ROC curve and immune infiltration analysis were performed to assess the diagnostic ability of the selected hub genes and elucidate the correlation between the selected hub genes and the immune microenvironment separately. Subsequently, our study predicted potential therapeutic drugs for IgAN via the CMap online database, followed by the chemical structure of identified small molecular drugs retrieved from the PubChem database. Ultimately, TYROBP gene with the highest levels among unexplored hub genes was selected to perform further validation. In conclusion, this study unraveled the mechanisms of disease development in IgAN and identified 10 hub genes, which may become prospective biomarkers for noninvasive diagnosis and therapeutic targets to improve the prognosis of IgAN. ## Microarray data acquisition “IgA nephropathy” served as search term on GEO database, and “Homo sapiens” and “Expression profiling by array” were used for further screening. The microarray datasets with IgAN glomerular data were included and those with duplicate sample data were excluded. Three microarray data (GSE37460, GSE99339, and GSE104948) were screened and downloaded from GEO (https://www.ncbi.nlm.nih.gov/geo/), which is a public repository containing high-throughput genomic data, microarrays, and chips [9]. GSE37460 is based on two platforms, namely GPL11670 (Affymetrix Human Genome U133 Plus 2.0 Array) and GPL14663 (Affymetrix GeneChip Human Genome HG-U133A Custom CDF). The former platform includes eighteen glomerular tissue samples from normal controls while the latter platform includes twenty-seven glomerular tissue samples from patients with IgAN and nine normal controls [10]. GSE99339 based on GPL19184 (Affymetrix Human Genome U133A Array), contains twenty-six glomerular tissue samples from IgAN patients [11], and GSE104948 based on GPL24120 (Affymetrix Human Genome U133A Array), collects twenty-seven glomerular tissue samples from IgAN patients [12]. ## Data normalization and DEGs identification The probes in three downloaded original files were converted into gene symbols. Among them, probes without corresponding gene symbols were discarded and genes with multiple probes were averaged. After that, our study merged samples which were from IgAN and normal group in three microarray data sets. Further, the integrated data was removed batch effect by sva package in R software (version 4.0.5). Finally, the limma package was applied to detect DEGs between glomerular tissues of patients with IgAN and the controls. Genes satisfying the screening criteria of the absolute value of log FC (fold change) > 1 and $p \leq 0.05$ were judged to be statistically significant, which were visualized by heatmaps and volcano plots based on R software. ## Tissue/Organ-Specific gene expression The online tool BioGPS (http://biogps.org) was utilized to explore tissue/organ-specific expression of the DEGs [13]. Transcripts complying the following screening standards were esteemed highly tissue-specific: [1] localized to a single organ system with the expression level of more than 10 times the median, and [2] the expression in the second-most abundant tissue was less than one-third of the highest level [14, 15]. ## Functional enrichment analysis In order to fully understand the pathogenic process DEGs involved in, clusterprofiler package in R software was employed to conduct GO and KEGG enrichment analysis of DEGs [16]. GO analysis consists of molecular function (MF), biological process (BP) and cellular component (CC), with the goal of indicating protein function [17]. KEGG was performed mainly for pathway analysis (www.kegg.jp/kegg/kegg1.html). [18–20]. GO term and KEGG pathways with $p \leq 0.05$ and gene count > 2 were deemed significant. In addition, the study also performed KEGG enrichment analysis of all detected genes via GSEA software (version 4.1) and c2:curated gene sets (c2.cp.kegg.v7.1.symbols.gmt) [21]. ## PPI network construction and hub genes recognition The Protein-Protein Interaction (PPI) network of DEGs was established through the online tool STRING (https://string-db.org/), which predicted interactions between proteins and determined the mechanisms of IgAN [22, 23]. Interaction with a combined score > 0.7 was set as the threshold for PPI network construction, and the result was visualized based on Cytoscape software (v3.8.2) [24]. Key modules and hub genes were acquired with MCODE score ≥ 2 via Cytoscape’s plug-in molecular complex detection (MCODE). Besides, the top 10 hub genes were also defined by MCC algorithm in the plug-in cytoHubba of Cytoscape. Ultimately, the intersecting genes of the two results were recognized as the final hub genes. GO and KEGG analyses of final hub genes were then examined and showed using ClueGO and Cluepedia tools in Cytoscape. ## The association between screened hub genes and IgAN The comparative toxicogenomics database (CTD, http://ctdbase.org) is a public database featuring abundant information regarding interactions between chemicals, genes, and diseases [25]. The CTD database was used to elucidate the relationship between hub genes and IgAN risk. ## Immune infiltration analysis The CIBERSORT package was employed to retrieve the fraction of immune cells. The CIBERSORT algorithm is based on a predefined immune signature matrix and gene expression array to calculate the relative proportions of 22 kinds of immune cell subsets in these samples. Principal component analysis (PCA) was conducted to analyze whether immune cell infiltration in IgAN glomerulus differed from that in normal controls. The difference in each immune cell infiltration between patients with IgAN and normal samples was determined using the ggplot2 package in R language. The R software package psych was used to compute Pearson’s correlation coefficient between immune cell subpopulations and hub genes. ## Expression of hub genes grounded on nephroseq v5 online platform The expression of hub genes in patients with IgAN and normal samples was observed using Nephroseq v5 online platform (http://v5.nephroseq.org). Group data were presented as mean ± SEM. Two-group comparisons were performed by Student’s t-test and significant difference in statistics was indicated by $p \leq 0.05$,$p \leq 0.01$,*$p \leq 0.001.$ ## ROC curves of hub genes To evaluate the levels of identified hub genes distinguishing IgAN from the normal group, the receiver-operating characteristic (ROC) curve analysis was employed. The R software pROC package was used for implementing data analysis and the ggplot2 package for visualizing the results. ## Small molecule therapeutic drugs prediction The Connectivity Map (CMap) is an online database embracing the functional relationship between diseases, genes and small molecular compounds based on intervention gene expression profiles and commonly applied to predict potential drugs for diseases therapy [26, 27]. A negative connectivity score indicates the drugs may be candidates for the treatment of diseases by reversing the specific gene expression pattern in disease states. These ten hub genes were submitted to the CMap database to predict potential therapeutic drugs ameliorating IgAN prognosis. PubChem (https://pubchem.ncbi.nlm.nih.gov/) was utilized to obtain the chemical structure of identified small molecule drugs. ## Cell culture and treatment The human glomerular mesangial cell line HMC (CBR130735, Cellbio, China) was grown in Dulbecco’s Modified Eagle’s Medium/Nutrient F-12 Ham (DMEM/F12) medium (Gibco, USA) plus$10\%$ fetal bovine serum (FBS) (BI) and $1\%$ antibiotics at 37 °C in an atmosphere of $5\%$ CO2. Aggregated IgA (aIgA1) were acquired by heating and aggregating monomeric human IgA1(Abcam) for 150 min at 65 °C as previously described 7. Then, HMCs were incubated with 25 µg/ml concentrations of aIgA1 for 24 h to creat the cell model of IgAN and were collected for western blot. ## Western blot method Briefly, the protein extraction from HMC cells was conducted using radio immunoprecipitation assay (RIPA) buffer with protease/phosphatase inhibitor cocktail. Protein concentration was detected by a BCA protein assay kit (Thermo Fisher Scientific). Proteins were separated using 8–$12\%$ SDS-PAGE and later transferred onto PVDF membranes. After blocking with $5\%$ skim milk in phosphate-buffered saline solution containing $0.1\%$ Tween-20, the blots were exposed to the anti-TYROBP primary antibody (Santa, 1:1000). β-actin was applied as an internal reference. ## Immunohistochemistry staining Paraffin-embedded renal sections were dewaxed and rehydrated, followed by antigen retrieval and blocking. Subsequently, the sections were incubated with the primary anti-TYROBP antibody (Santa, 1:200) overnight at 4 °C. After that, the sections were incubated with HRP-conjugated secondary antibodies and diaminobenzidine (DAB) served as a substrate to develop color. Images were obtained by a Nikon microscope and analyzed by Image-Pro plus* 6.0. In the validation cohort, 15 patients were enrolled. These patients consisted of 3 minimal change disease (MCD) patients, 3 IgAN patients, 3 diabetic nephropathy (DN) patients, 3 focal segmental glomerular sclerosis (FSGS) patients and 3 membranous nephropathy (MN) patients. Immunohistochemical staining was conducted on three independent cases and controls. ## Identification of DEGs in IgAN This study design was depicted in Fig. 1. Three microarray datasets (GSE37460, GSE99339, and GSE104948) were downloaded to obtain DEGs related to IgAN. After normalizing the microarray results, a total of 113 DEGs involved in IgAN were filtered by limma package ($p \leq 0.05$, \|logFC\| > 1), including 49 up-regulated genes and 64 down-regulated genes, as shown in the heatmap and volcano plot (Fig. 2). Fig. 1Flow chart to demonstrate the process of data analysis and experimental validation Fig. 2Identification of DEGs in renal glomerular tissue from IgAN patients compared with control samples samples. ( A) Heatmap of the top 10 upregulated genes and the top 10 downregulated genes. Red rectangles indicate upregulated genes and blue rectangles indicate downregulated genes. ( B) Volcano plot of identified DEGs. Red dots indicate upregulated genes and blue dots indicate downregulated genes. DEG: differentially expressed gene; IgAN: IgA nephropathy ## Identification of tissue- or organ-specific expressed genes This study identified 67 tissue/organ-specific expressed genes via BioGPS (Table 1). Among them, most of these genes were specifically expressed in the haematologic/immune system ($\frac{27}{67}$, $40.30\%$). The digestive system was the second organ-specific expressed system, which comprised 15 genes ($\frac{15}{67}$, $22.39\%$). It was followed by the urinary system ($\frac{6}{67}$, $8.96\%$), genital system ($\frac{5}{67}$, $7.46\%$), endocrine system ($\frac{3}{67}$, $4.48\%$), skeletal/muscle system ($\frac{3}{67}$, $4.48\%$), and placenta ($\frac{2}{67}$, $2.99\%$). At last, the respiratory system, circulatory system, tongue and adipose tissues shared the lowest specific expressed genes ($\frac{1}{67}$, $1.49\%$). However, the results also showed that 17 genes were highly expressed in both the digestive system and kidney, such as PRODH2, SLC27A2, GBA3, PBLD, PCK1, FBP1, HAO2, GLYAT, HPD, BHMT2, APOM, DPYS, GATM, SLC7A9, DPEP1, PLG, and DEFB1. Table 1Distribution of tissue/organ-specific expressed DEGs from BioGPSSystem/OrganGenesCountsHaematologic/Immune Haematologic/Immune cellsFOSB,LPAR6,HLX,GBP2,HCLS1,FGL2,LY96,FCER1G,CX3CR1,TYROBP,FCN1,HCK,PTPRC,NCF2,PYCARD,MS4A6A,MGAM,ISG1518 Immune organsCD48,IL10RA,CD53,ITGB2,CD52,PLAC8,LTF,CXCL14, DI019NervousCALB1,NETO22DigestiveAPOH,ALB,HRG,KNG1,AFM,GBA3,HMGCS2,FTCD,SERPINA6,FABP1,UPB1,CYP4F2,RBP4,KLK1,NNMT15UrinarySLC22A8,EGF,ALDH6A1,XPNPEP2,SLC13A3,SLC22A66RespiratoryVSIG41CirculatoryC1QB1EndocrineSLC19A2,NR4A2,SST3Skeletal/musclePLK2,POSTN,TGFBI3PlacentaCSF1R,HSD11B22GenitalIGF1,ACE2,SLC17A3,SERPINA5,UMOD5Others TongueECM11 AdiposeCD361 ## GO and KEGG enrichment analyses of DEGs To elucidate the biological function of the screened DEGs, GO term and KEGG pathway enrichment analysis were conducted by Metascape. The GO enrichment analysis revealed that most significant enrichment in molecular function involved peptidase regulator activity, heparin binding and endopeptidase inhibitor activity. Most remark enrichment in biological process consisted of regulation of cytokine production, immune effector process and response to bacterium. Changes in cell component of DEGs were mainly enriched in collagen-containing extracellular matrix, secretory granule lumen and cytoplasmic vesicle lumen (Fig. 3A-C). KEGG signaling pathways results showed that DEGs mainly focused on complement and coagulation cascades, pertussis and PPAR signaling pathway (Fig. 3D). Fig. 3GO terms and KEGG pathways enrichment analyses of DEGs. ( A-C) The bubble diagram of GO terms enrichment analyse. ( A) BP terms. ( B) CC terms. ( C) MF terms. The x-axis indicates the gene ratio and the y-axis indicates GO terms. Distinct point shapes indicate distinct different categories and bubble size indicates gene count. Coloring indicates -log10(p value) with higher in red and lower in green. ( D)The bubble plot of KEGG pathway analyse [18–20]. The x‐axis represents gene ratio and the y‐axis represents KEGG pathway. Bubble size indicates gene count and color indicates –log10(p value) with higher in red and lower in green ## Functional enrichment analysis of all detected genes GSEA analysis was also performed to estimate the key pathways correlated with the IgAN group. The results suggested that most of the enriched gene sets were involved in proteasome pathway at the screening criteria of $p \leq 0.05$ and FDR < 0.25 (Fig. 4A). When the screening criterion for significant gene sets was $p \leq 0.05$, the most significantly enriched pathways included proteasome, allograft rejection, viral myocarditis, graft versus host disease, FCγR mediated phagocytosis, and natural killer cell-mediated cytotoxicity (Fig. 4A-F). Fig. 4GSEA analysis demonstrating most enriched gene sets between the healthy and IgAN group. ( A) The most significant enriched gene set was proteasome pathway (ES = 0.666, NES = 1.782, $p \leq 0.05$). ( B) The second significant enriched gene set was allograft rejection (ES = 0.707, NES = 1.586, $p \leq 0.05$). ( C) The third significant enriched gene set was viral myocarditis (ES = 0.548, NES = 1.568, $p \leq 0.05$). ( D) The fourth significant enriched gene set was graft versus host disease (ES = 0.685, NES = 1.568, $p \leq 0.05$). ( E) The fifth significant enriched gene set was FCγR mediated phagocytosis (ES = 0.524, NES = 1.562, $p \leq 0.05$). ( F) The sixth significant enriched gene set was natural killer cell mediated cytotoxicity. ( ES = 0.543, NES = 1.534, $p \leq 0.05$). GSEA: gene set enrichment analysis; ES: enrichment score; NES: normalized enrichment score; IgAN: IgA nephropathy ## PPI network constitution of DEGs and hub genes recognition To investigate the interaction between proteins encoded by DEGs, PPI network was constructed by STRING tools and visualized by Cytoscape (Fig. 5A). The network was comprised of 83 nodes and 178 edges. And the down- and up-regulated proteins were represented by green diamond and red ellipse, respectively. In addition, the five most significant modules were detected by MCODE plugin in Cytoscape. As shown in Fig. 5B-F, cluster 1 possessed the highest cluster score (score: 7, 7 nodes and 21 edges), which was followed by cluster 2 (score: 6.667, 10 nodes and 30 edges), cluster 3 (score: 3, 3 nodes and 3 edges), cluster 4 (score: 3, 3 nodes and 3 edges), and cluster 5 (score: 3, 3 nodes and 3 edges). In total, 26 hub genes were screened from these five key modules. Subsequently, the top 10 hub genes (KNG1, FN1, ALB, PLG, IGF1, EGF, HRG, TYROBP, CSF1R, ITGB2) were extracted with CytoHubba (Fig. 5G). Later, our study took the intersection of these hub genes obtained by the two methods as the final hub genes and listed them in Table 2. Within ClueGO and Cluepedia, GO and KEGG enrichment analysis of these 10 final hub genes revealed that they were significantly enriched in platelet alpha granule lumen, synapse pruning, positive regulation of myeloid leukocyte mediated immunity, positive regulation of phagocytosis, macrophage activation, complement and coagulation cascades, and pertussis (Fig. 5H-I). Fig. 5PPI network of DEGs, five cluster modules extracted by MCODE and identification of hub genes. ( A) A network of PPI among the DEGs was established on STRING database. The highly expressed genes are illustrated by the red ellipses and lowly expressed genes by the green diamonds. Nodes represent genes, while edges represent protein-protein interaction. ( B) Cluster 1 (MCODE score = 7). ( C) Cluster 2 (MCODE score = 6.667). ( D) Cluster 3 (MCODE score = 3). ( E) Cluster 4 (MCODE score = 3). ( F) Cluster 5 (MCODE score = 3). ( G) Ten crucial genes were screened through MCC algorithm in Cytoscape. The higher the score, the deeper the color. ( H) The GO terms enrichment analysis of the ten identified hub genes. ( I) KEGG pathways analysis of identified ten hub genes. DEG: differentially expressed gene; PPI: protein-protein interaction Table 2Ten hub genes identified via CytohubbaGene symbolDescriptionlogFCKNG1kininogen 1-1.50FN1fibronectin 11.12ALBalbumin-2.44PLGplasminogen-1.43IGF1Insulin like growth factor 1-1.10EGFepidermal growth factor-1.33HRGhistidine rich glycoprotein-1.06TYROBPtransmembrane immune signaling adaptor TYROBP1.70CSF1Rcolony stimulating factor 1 receptor1.01ITGB2Integrin subunit beta 21.30 ## The interaction between hub genes and IgAN based on the CTD database To identify candidate crucial genes interrelated with IgAN, the CTD database was employed to evaluate the relationship between selected hub genes and IgAN. As shown in Fig. 6, 10 hub genes targeting IgAN, glomerulonephritis, kidney diseases and immune system diseases. Inference scores embodied the correlation between chemicals, disease and genes. Apparently, ALB, IGF1 and FN1 were highly interconnected with IgAN. Fig. 6Recognization of potential crucial genes related to IgAN by CTD database ## Immune cell infiltration in IgAN It is well known that IgAN is determined synthetically by genetic factors, environmental disturbance and immune response. Among them, immune inflammation achieved a dominant position in the occurrence and progression of IgAN. PCA cluster analysis is a tool to evaluate the consistency of biological duplication and divergence among distinct populations. So this PCA cluster plot results indicated that immune infiltration differed substantially between IgAN patients and the healthy group (Fig. 7A). Then, our study investigated the distribution of 22 types of immune cell subtypes in tissue using the CIBERSORT algorithm. From the box diagram in Fig. 7B, it was found activated NK cells, monocytes and M0 macrophages, CD8 + T cells, regulatory T cells infiltrated more, while naïve B cells, resting CD4 memory T cells, T cells follicular helper, resting dendritic cells, activated mast cells, and neutrophils infiltrated less in glomerular tissue of IgAN patients compared to that of normal group. Furthermore, correlation heatmap confirmed that monocytes were significantly positively correlated with M0 macrophages. Memory B cells and naive CD4 + T cells also showed a positive relationship significantly. However, resting CD4 memory T cells were significantly negatively correlated with monocytes. Naive B cells and memory B cells also had a great negative correlation. And there was a high correlation between activated mast cells and resting mast cells (Fig. 7C). The correlation heat map was performed to demonstrate the correlation of identified hub genes with immune cell infiltration. The results revealed that IGF1, EGF, HRG, FN1, ITGB2 and TYROBP were highly associated with the abundance of monocytes, naive B cells, resting CD4 memory T cells, and T cells follicular helper, which provided insight into the potential role of these crucial genes in immune landscape (Fig. 7D). Fig. 7The immune landscape in IgAN and normal controls. ( A) Principal components analyses (PCA) performed on all samples. ( B) The difference in infiltrating immune cells between IgAN and the normal group. The IgAN group was illustrated in red color and the normal group was illustrated in blue color. ( C) Correlation heatmap of all 22 immune cells. The size of the colored dots indicates the strength of the correlation. The red color stands for a positive correlation, while the blue color stands for a negative correlation. Darker color indicates a stronger correlation. * $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001.$ ( D) Correlation heatmap between hub genes and immune cells infiltration. The color depth of the triangle below is positively correlated with the correlation coefficient. The red color indicates a negative correlation, while the blue color indicates a positive correlation. The color depth of the upper triangle indicates p value. $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$ ## mRNA expression of hub genes in renal glomeruli of IgAN Patients Using nephroseq v5 online platform Comparing with the normal group, the expression of FN1, TYROBP, CSF1R, and ITGB2 significantly increased while that of other 6 hub genes visibly decreased in the IgAN samples (Fig. 8). Fig. 8mRNA expression of hub genes in the glomerulus of IgAN patients based on Nephroseqv5 platform. ( A) The expression of KNG1 decreased in IgAN. ( B) The expression of FN1 elevated in IgAN. ( C) The expression of ALB downregulated in IgAN. ( D) The expression of PLG reduced in IgAN. ( E) The expression of IGF1 decreased in IgAN. ( F) The expression of EGF descended in IgAN. ( G) The expression of HRG declined in IgAN. ( H) The expression of TYROBP upregulated in IgAN. ( I) The expression of CSF1R enhanced in IgAN. ( J) The expression of ITGB2 increased in IgAN. $p \leq 0.05$ was considered statistically significant. $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001.$ IgAN: IgA nephropathy; mRNA: messenger RNA ## ROC curve analysis of 10 hub genes in predicting IgAN The receiver operating characteristic (ROC) curve, a fundamental tool for diagnostic test evaluation, was brought to explore the diagnostic value of identified hub genes, of which area under the curve (AUC) served as a quantitative indicator for intrinsic effectiveness of diagnostic test. According to Fig. 9, TYROBP has the strongest diagnostic ability for IgAN with the highest AUC value of 0.910, which was followed by ALB (AUC: 0.886) and ITGB2 (AUC: 0.884). The predictive value of other genes in the diagnosis of IgAN are follows: CSF1R (AUC: 0.872), FN1 (AUC: 0.869), EGF (AUC: 0.853), KNG1 (AUC: 0.846), HRG (AUC: 0.844), IGF1 (AUC: 0.829), PLG (AUC: 0.772). Therefore, TYROBP, ALB and ITGB2 may serve as novel diagnostic biomarkers for IgAN. Fig. 9Diagnostic accuracy of hub genes. ROC: receiver operating characteristic; AUC: area under the ROC curve ## Identification of small molecule therapeutic drugs to arrest glomerular injury of IgAN Aiming at predicting potential therapeutic agents targeting IgAN, CMap database was applied to acquire small molecular compounds which may reverse hub genes expression. The top 10 small molecular compounds were screened (Table 3). Among them, verteporfin, moxonidine, procaine, and prenylamine were the most significant four compounds, indicating that these compounds may become candidate drugs to improve IgAN. Further, the PubChem database, a public repository for information on small molecules and their biological activities, was utilized to acquire the chemical structures of small molecular compounds among which the structures of verteporfin and STOCK1N-35,215 had not been identified (Fig. 10). Table 3Potential drugs were provided by CMap database according to hub genesCmap nameMeanNEnrichment p Percent non-nullverteporfin-0.8593-0.990100moxonidine-0.6463-0.8960.00218100procaine-0.5975-0.8480.00024100prenylamine-0.6144-0.8140.00225100sulfadoxine-0.5613-0.7950.01757100cortisone-0.5323-0.7940.01785100buspirone-0.5254-0.7780.00501100STOCK1N-35,215-0.5533-0.7680.02552100primidone-0.2514-0.6990.0172550emetine-0.4544-0.6870.0206775 Fig. 10Ten most significant small molecules as potential drugs for IgAN treatment targetting ten hub genes. ( A–H) Predicted chemical structure of targeted drugs. IgAN: IgA nephropathy ## The validation of TYROBP in IgAN Based on the highest expression and the highest diagnostic value as well as the close correlation with immune infiltration among these five unexplored hub genes, TYROBP was further validated in vivo and in vitro. The results showed that TYROBP was highly expressed in aIgA1-treated HMC and renal tissues from IgAN patients (Fig. 11). However, TYROBP was expressed at low abundance in other types of kidney disease including minimal change disease (MCD), diabetic nephropathy (DN), focal segmental glomerular sclerosis (FSGS) and membranous nephropathy (MN) (Fig. 11). These results demonstrated that TYROBP possessed considerable diagnostic value for IgAN. Fig. 11The expression level and diagnostic significance of TYROBP were verified. ( A) TYROBP protein expression was measured through western blotting. ( B) Quantitative analysis* indicated that the expression of TYROBP in aIgA1-stimulated HMCs was evidently up-regulated compared with the control group. ( C) Immunohistochemical staining for TYROBP was conducted in different renal pathologies to evaluate its diagnostic accuracy in IgAN. Macroscopic and microscopic examination were presented. ( D) Quantitative results of immunohistochemical staining revealed that TYROBP was markedly higher in IgAN than that in other renal pathologies. * $p \leq 0.05$, $p \leq 0.01$, $p \leq 0.001$, *$p \leq 0.0001.$ Data are representative of 3 independent experiments and are expressed as mean ± SD ## Discussion IgAN is the most common cause of primary glomerulonephritis around the world, which comprises a large fraction of the ESRD (20–$40\%$) [3, 4, 28]. Because of high morbidity and mortality rates, it has brought about an immense social and economic burden [3]. Nevertheless, the pathophysiologic mechanisms of IgAN have not been elucidated thoroughly. Invasive renal biopsy is the only currently available tool to identify IgAN and current treatment methods have not achieved the desired efficacy in the clinic. Thus, there is a pressing need for developing novel non-invasive approaches and more effective therapeutic options based on a full understanding of pathogenesis. Our study merged and analyzed gene expression profiling of 80 IgAN samples and 27 normal samples from 3 datasets by bioinformatics methods. As a result, 113 DEGs were identified, consisting of 49 genes up-regulated and 64 down-regulated. Of these, 67 tissue/organ-specific expressed genes were screened via BioGPS and the results indicated that the haematologic/immune system held the highest degree of specificity, which could clarify and verify the autoimmune nature of IgAN [29, 30]. Meanwhile, second organ-specific expressed system was the digestive system consistent with the common occurrence of intestinal mucosal infections in patients with IgAN [31]. Evidence emerged that mucosal immune reaction, particularly in the intestines, may be the major source of hypo-galactosylated IgA1 [32]. Furthermore, quite a few genes were not only highly expressed in kidney, but were also abundantly expressed in digestive system. Combining the hypothesis of gut-kidney axis existing in IgAN, there was an intricate crosstalk between gut and kidney. Various pathogenic virulence incited the injury of intestinal mucosal barrier and thus promoted the enhanced production of aberrantly glycosylated polymeric IgA1, which was then released into the blood circulation and deposited in the glomeruli [6]. Further, this validated the fact that the kidney merely acted as an innocent bystander. GO annotation analysis of DEGs suggested that collagen-containing extracellular matrix, regulation of cytokine production, and immune effector process were mainly enriched, which are very consistent with the characteristics of IgAN including deposited circulating immune complexes-induced proliferation of glomerular cells and overproduction of extracellular matrix (ECM), secretion of inflammatory cytokines, as well as infiltration of diverse immune cells [3, 33–35]. Additionally, KEGG pathways result showed that the prominent pathways were enriched in complement and coagulation cascades, pertussis and PPAR signaling pathway, confirming that immune responses and inflammation exerted great effects on the onset and development of IgAN. It has been reported that toll-like receptor 4 (TLR4)-induced proinflammatory effects, which could accelerate the progression of IgAN, can be efficiently attenuated by PPAR-γ agonists in vivo and in vitro [36, 37]. Further, GSEA software was employed to conduct KEGG pathway analysis targeting a total of 9556 genes and the results suggested the IgAN group was most positively associated with proteasome pathway. The switch from proteasome to immunoproteasome was observed in those with ongoing progressive IgAN, which represents the hyperactivation of the proteasome system responding to infectious agents-induced IFNs production or oxidative stress [38]. A single-center open-label pilot trial implicated that bortezomib, a proteasome inhibitor targeting plasma cells, achieved more expected efficacy in patients with severe IgAN compared to rituximab targeting B-cell depletion [39]. Then, PPI network was constructed to distinguish 10 hub genes, including KNG1, FN1, ALB, PLG, IGF1, EGF, HRG, TYROBP, CSF1R, and ITGB2. The results from enrichment analysis of these hub genes demonstrated that they were highly correlated with immune-related pathways. Among these hub genes, five genes, namely FN1, ALB, PLG, IGF1 and EGF, have been reported in IgAN. FN1 is identified as the principal component of ECM, and elevated FN1 levels reflect more severe renal fibrosis in IgAN [40]. Roszkowska Blaim et al. found that urinary excretion of FN may be an indicator to evaluate the disease activity of IgAN [41]. Consistent with these studies, our study found that FN1 expression was enhanced in IgAN, which may indicate a higher degree of fibrosis. ALB encodes serum albumin. As the most abundant protein in blood, serum albumin manifests the nutrition status and regulates the plasma colloid osmotic pressure and functions as a transporter of numerous substances. Some data revealed that time-averaged serum albumin (TA-ALB) may be predictive of long-term outcome in IgAN patients who have achieved remission [42]. Compared with the control samples, the ALB expression was markedly decreased in IgAN patients, which may be reflective of a poor prognosis. PLG (Plasminogenis), a serine protease that circulates in blood plasma as an inactive zymogen, is converted to the active protease, plasmin. Plasmin degrades many blood plasma proteins, containing fibrin-containing blood clots. Accumulating evidence showed that a considerable proportion of IgAN patients presented stable fibrin deposition followed by fibrinolysis and platelet viability in the glomerulus [43]. Our study suggested PLG was greatly down-regulated in patients with IgAN, which may mirror PLG was over activated and the activity of fibrinolytic system was enhanced. IGF1 has been proved significant in fostering mesangial cells and podocytes proliferation and ECM reshaping [44]. In addition, most of IgAN patients showed increased IGF-I expression in peripheral blood mononuclear cells (PBMC), while not detected in patients with other glomerulonephritis or normal samples [45, 46]. It was also found that the level of IGF-1 in PBMC had a strong positive correlation with urinary protein excretion and histopathological alterations [46]. Thus, these evidences illustrated that abnormal expression of IGF-I in PBMC may act as a useful biomarker of IgAN activity and progression. Furthermore, the rs2195239, rs1520220, and rs978458 variants played vital roles in the pathological progression of IgAN [47]. Compared to the healthy group, the expression of IGF1 in IgAN was slightly lower in our study, which calls for further investigation. In IgAN patients, urinary IL-6 and MCP-1 exhibited a significant increase, while EGF excretion exhibited a reduction. These changes paralleled the progression of staging. Previous studies have indicated that EGF urinary excretion or 24-hours excretion of EGF may be an effective indicator to estimate interstitial fibrosis and renal function outcomes in patients with IgAN [48]. Further evidence suggested urinary IL-6/EGF ratio might serve as a prognostic biomarker for renal impairment and EGF/MCP-1 ratio in urine may function as a valuable measure to predict the prognosis of ESRD in IgAN patients [49, 50]. In our study, EGF showed significantly lower levels in IgAN patients than in healthy individuals. These may reflect the progression and prognosis of IgAN. Thus far, no study has been performed on the correlation between such hub genes and IgAN as KNG1, HRG, TYROBP, CSF1R and ITGB2. Kininogen 1 (KNG1) could be degraded to High-molecular weight kininogen (HMWK) and low-molecular weight kininogen (LMWK), and HMWK played a significant role in inflammation and coagulation. Some data demonstrated that KNG1 rs5030062 and rs710446 variants were bound up with higher eGFR [51]. Our study found that KNG1 was dramatically down-regulated in IgAN samples compared to normal samples, which may indicate increased degradation. Being an adapter, histidine-rich glycoprotein (HRG) participates in a wide variety of pathways, such as inflammation, immune function, fibrinolysis, and coagulation [52–54]. As was reported that HRG could prevent septic lethality via inhibiting neutrophil adhesion to endothelial cells, indicating that HRG may become a therapeutic option for inflammatory diseases [55]. Brier et al. illustrated that HRG significantly improved risk prediction for AKI following cardiac surgery [56]. At present, HRG has not been reported in IgAN-related studies. TYROBP, named as TYRO protein tyrosine kinase binding protein, is an adapter protein containing an immunoreceptor tyrosine-based activation motif. It involves in diverse physiological processes including signal transduction, cell activation, immune function, immune inflammation, and apoptosis through non-covalently associating with receptors on the surface of various immune cells. The abnormal expression of TYROBP has been reported to take part in numerous diseases, such as Alzheimer’s disease, breast cancer, osteosarcoma and renal cell carcinoma. Takahashi et al. found that TYROBP improved microglia phagocytosis of amyloid β and gave rise to the occurrence and development of AD [57]. Pottier et al. considered that TYROBP could be used for early diagnosis and intervention of AD [58]. Wang et al. and Li F et al. indicated that in renal cell carcinoma, TYROBP was significantly increased and was related to poor prognosis [59, 60]. Nevertheless, TYROBP has not been reported in IgAN. The findings indicated that TYROBP expression was greatly enhanced in IgAN samples compared with the normal samples. Notably, TYROBP held a significant diagnostic value for IgAN, as indicated by the AUC up to 0.910. Thus, TYROBP was considered as a novel and promising index for the diagnosis of IgAN. CSF1R gene-encoded protein is the receptor for colony stimulating factor 1, which is related to the production, differentiation, as well as function of macrophages. CSF1R was predominantly expressed in immune cells, such as monocyte, macrophage, bone marrow cell precursors, and microglia in the central nervous system [61]. Notably, CSF-1 can induce tubule proliferation, which is critical for kidney repair from acute kidney injury. Perry et al. demonstrated that enhanced CSF-1 expression in tubule epithelial cells protected against acute kidney injury through binding to its receptor CSF1R and promoting tubular cell proliferation [62]. Proximal tubule-specific knockout of CSF-1 led to a significant increase in neutrophils and a decrease in macrophages/dendritic cells [62]. In our study, the expression of CSF1R was elevated in glomeruli of IgAN patients, which may be responsible for immune cell infiltration, particularly macrophages. Integrin subunit beta 2(ITGB2) encodes the β integrin subunit, which can constitute distinct integrins through interacting with various α subunits. It was reported that ITGB2 can foster the adhesion of leukocytes to the vascular endothelium and subsequent extravasation [63, 64]. ITGB2 was also found related to ECM-remodelling, which was correlated with poor survival outcomes in patients with RCC [65]. In CKD patients, ITGB2 was negatively associated with estimated glomerular filtration rate (eGFR). Additionally, bioinformatics analysis illuminated that other diseases which were associated with ITGB2 included lupus nephritis and diabetic nephropathy. In this study, ITGB2 was identified highly expressed in patients with IgAN, which might be associated with immune cell infiltration, renal fibrosis and decreased renal function. The result from the CTD database showed that ALB, IGF1 and FN1 exhibited a higher score with IgAN, reflecting a tight linkage between these three crucial genes and the occurrence and development of IgAN. Even further, ROC curve analyses were conducted to confirm the sensitivity and specificity of hub genes for IgAN diagnosis. As the results demonstrated, all the hub genes have good diagnostic performance with an AUC of over 0.75. Especially TYROBP, holding the highest AUC value of 0.910, may serve as a potential molecular biomarker for the diagnosis of IgAN. It has been widely acknowledged that constant Gd-IgA1 deposition brings about the proliferation of mesangial cells and subsequent production of cytokines and chemokines, which act as the key mediators of the crosstalk between pathological mesangium and other renal cells [66]. As a result, glomerulosclerosis and tubulointerstitial fibrosis are caused. Notably, growing evidence suggests infiltrating immune cells in the kidney play a critical role in the pathogenesis of IgAN [34, 67, 68]. Hence, CIBERSORT was employed to quantify the immune infiltration in IgAN patients for investigating the role of immune cells in IgAN. It was found that activated NK cells, monocytes, M0 macrophages, CD8 + T cells, and regulatory T cells were considerably increased in the glomerular tissue of IgAN, while naïve B cells, resting CD4 memory T cells, T cells follicular helper, resting dendritic cells, activated mast cells, and neutrophils memorably decreased. Previous studies demonstrated that HLA-DR expressing NK cells were prominently augmented in IgAN patients and patients with higher HLA-DR manifested faster deterioration of renal function [69]. The CD56dimCD16 + subpopulation accounts for $90\%$ of NK lymphocytes and has higher cytotoxicity and a higher capacity for cytokine secretion. Esteve Cols C et al. stated that CD56dimCD16 + NK cells were present in higher proportions in IgAN patients compared with the normal [70]. Consequently, NK cells may involve in IgAN pathogenesis via creating an inflammatory microenvironment and attracting more immune cells. It has been proved that infiltrating immune cells in the kidney, especially monocytes/macrophages, play an important role in albuminuria and kidney impairment in IgAN. Elevated Tim-3 monocytes/macrophages in circulation and renal tissue might contribute to the pathogenesis of IgAN and could be used to evaluate disease severity [71]. Increasing evidence indicated an association between the abundance of renal macrophages and the severity of IgAN [72]. More recently, two single-cell RNA-sequencing studies in IgAN confirmed monocytes and macrophages greatly enhanced in the kidney of IgAN patients. Zheng et al. elucidated that PLGRKT and CCL2 were upregulated in mesangial cells and recruited monocytes/macrophages [34]. Tang et al. proved that three genes FAM49B, GPX3 and FCGBP associated with ROS production, mitochondrial function and EMT, respectively, were downgregulated in macrophages of IgAN patients [73]. In addition, Tomino et al. uncovered that CD8 + T cells were the most abundant glomerular-infiltrating cell in IgAN patients [74]. Anti-CD8 T cell treatment has been proved to trigger a profitable effect of suppressing mesangial expansion in an animal model of IgAN [75]. Another study showed that, compared to children with non-progressive IgAN, significantly higher percentages of CD8 + T cells in the glomeruli and in the interstitium were detected in the children with progressive IgAN [76]. Furthermore, Zheng et al. also revealed that genes associated with effector T cell marker and the cytotoxicity, were greatly decreased, while T cell exhaustion-related genes were increased in CD8 + cytotoxic T lymphocytes, indicating CD8 + cytotoxic T lymphocytes dysfunction may be linked to IgAN progression [34]. An Italian study detected aberrant methylation in CD4 + T cells from IgAN patients and this may give rise to impaired TCR signaling and attenuated T cell activation [77]. Neutrophil apoptosis could be triggered by immobilized IgA [78], which might explain a marked reduction in the number of neutrophils. The results of our study combined with the above literature evidence have elucidated that immune cell infiltration is closely associated with IgAN progression and the potential mechanisms should be further explored. Furthermore, correlation analysis between immune cells revealed that monocytes were significantly positively correlated with M0 macrophages. Memory B cells also exhibited a significant positive correlation with naive CD4 + T cells. However, monocytes were significantly negatively correlated with resting CD4 memory T cells. Naive B cells and memory B cells also had a great negative correlation. These evidences suggest that there is an intricate interaction between immune cells and the underlying mechanisms need to be further clarified. The correlation heat map revealed that six identified hub genes, namely IGF1, EGF, HRG, FN1, ITGB2 and TYROBP, were strongly related to the abundance of monocytes, naive B cells, resting CD4 memory T cells, and T cells follicular helper. Among them, TYROBP was negatively correlated with T cell and B cell infiltration, and positively correlated with dendritic cells and mast cell infiltration. All these associations provided insight into the potential role of these crucial genes in the occurrence and progression of IgAN. Based on the CMap database, ten key genes were used to search for potential drugs for IgAN treatment and the top ten candidate drugs were identified. Among these drugs, only cortisone, which belongs to corticosteroids and acts as one of existing immunosuppressive options, has been extensively used in the clinic to treat IgAN. Verteporfin, a apharmacological inhibitor of YAP, was reported to inhibit fibrosis in many models of kidney diseases including AKI, DKD and UUO [79–81]. A recent study demonstrated that verteporfin could alleviate renal inflammation mediated by tubular repair after ischemia/reperfusion [82]. In addition, verteporfin produced protective effects against UUO-induced renal tubulointerstitial inflammation and fibrosis by suppressing the TGF-β1/Smad signaling pathway [83]. Feng et al. showed that verteporfin also prevented kidney fibrosis by impeding Wnt5a- and TGFβ1-mediated M2 macrophage polarization [84]. Another study reported that verteporfin administration or specific deletion of YAP (yes-associated protein) in renal proximal tubule cells apparently mitigated renal tubulointerstitial fibrosis in DKD mice [80]. Moxonidine is an inhibitor of central sympathetic outflow, which can act centrally to decrease sympathetic nervous activity [85]. Therefore, it is generally viewed as an antihypertensive drug. Tsutsuiet al. showed that moxonidine served as an agonist of α2/imidazoline Ι1-receptor and repressed the renal sympathetic nervous system activity, thereby protecting against acute ischemic kidney injury [86]. Hausberg et al. revealed that a low dose of moxonidine generated significant and sustained suppression of sympathetic outflow without any detrimental effect on hemodynamics, which may improve the outcome of ESRD patients [87]. Procaine was illustrated to mitigate nephrotoxicity caused by cisplatin by forming a less toxic complex with cisplatin [88]. Nevertheless, there is no more related study on kidney diseases. Prenylamine acts as a calcium antagonist and has been applied for treating angina pectoris [89]. In addition, prenylamine could induce cell apoptosis, thus becoming a candidate agent for carcinoma [90]. Combining with pyrimethamine, sulfadoxine can be used for treatment and prevention of chloroquine-resistant malaria [91]. Buspirone can activate the 5-HT1A receptor and belongs to non-benzodiazepine anxiolytics [92, 93]. Bioinformatics analysis showed that STOCK1N-35,215 may be a targeted drug for recurrent implantation failure [94]. However, the present studies regarding the effects of STOCK1N-35,215 are limited and further inquiry is called for. Primidone is considered an aromatic antiepileptic used for the treatment of partial, generalized and complex seizures. More recently, Riebeling et al. revealed that RIPK1 activation and RIPK1-driven cell death, playing a crucial role in hyperinflammatory diseases including renal ischemia-reperfusion injury (IRI), can be prevented by primidone, an effective and specific inhibitor of RIPK1 [95]. Currently, primidone is FDA-approved and it has the potential to become a promising candidate for treating inflammatory disorders. Emetine is applied for treating acute amoebic dysentery and parenteral amoebiasis, however, It has certain toxicity. In addition, It has been proved to promote the degradation of HIF-2α in clear cell renal cell carcinoma [96]. Therefore, this warrants further work to investigate the effect of those compounds in IgAN as well as the underlying mechanisms. Among these five unexplored hub genes, TYROBP held the highest expression level and highest diagnostic efficiency. Therefore, the following experiment focused on investigating this hub gene. In vitro and in vivo experiments, the results confirmed the high abundance of TYROBP in IgAN. Significantly, TYROBP could differentiate IgAN from other renal pathologies well, which showed that TYROBP may be a good diagnostic marker for IgAN. This study has, however, certain limitations. First, the sample size is not big enough. Second, further clinical researches and basic experiments are required to validate the analytical results and explore the molecular mechanisms underlying these results. Third, CIBERSORT analysis relies on the restricted genetic data which may diverge from heterotypic cell-cell interactions, phenotypic plasticity, or disease-induced disturbances. While CIBERSORT has significantly lower estimation bias compared with other methods, some kinds of immune cells might be systematically overestimated or underestimated. ## Conclusion In total, 113 DEGs and 10 hub genes were selected on the strength of bioinformatics methods, which may involve in the pathogenesis of IgAN and become diagnostic biomarkers and therapeutic targets for the disease. In addition, this study predicted several candidate drugs that may ameliorate glomerular injury and improve IgAN outcomes. Furthermore, TYROBP may serve as a good biomarker for the diagnosis of IgAN. Collectively, our results provide a more in-depth insight into the occurrence and progression of IgAN. Nevertheless, further clinical and basic studies are required to elucidate biological functions of those genes in IgAN. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. Wyatt R, Julian B. IgA nephropathy*. 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--- title: Community data-driven approach to identify pathogenic founder variants for pan-ethnic carrier screening panels authors: - Yaron Einhorn - Moshe Einhorn - Alina Kurolap - Dror Steinberg - Adi Mory - Lily Bazak - Tamar Paperna - Julia Grinshpun-Cohen - Lina Basel-Salmon - Karin Weiss - Amihood Singer - Yuval Yaron - Hagit Baris Feldman journal: Human Genomics year: 2023 pmcid: PMC10044388 doi: 10.1186/s40246-023-00472-w license: CC BY 4.0 --- # Community data-driven approach to identify pathogenic founder variants for pan-ethnic carrier screening panels ## Abstract ### Background The American College of Medical Genetics and Genomics (ACMG) recently published new tier-based carrier screening recommendations. While many pan-ethnic genetic disorders are well established, some genes carry pathogenic founder variants (PFVs) that are unique to specific ethnic groups. We aimed to demonstrate a community data-driven approach to creating a pan-ethnic carrier screening panel that meets the ACMG recommendations. ### Methods Exome sequencing data from 3061 Israeli individuals were analyzed. Machine learning determined ancestries. Frequencies of candidate pathogenic/likely pathogenic (P/LP) variants based on ClinVar and Franklin were calculated for each subpopulation based on the Franklin community platform and compared with existing screening panels. Candidate PFVs were manually curated through community members and the literature. ### Results The samples were automatically assigned to 13 ancestries. The largest number of samples was classified as Ashkenazi Jewish ($$n = 1011$$), followed by Muslim Arabs ($$n = 613$$). We detected one tier-2 and seven tier-3 variants that were not included in existing carrier screening panels for Ashkenazi Jewish or Muslim Arab ancestries. Five of these P/LP variants were supported by evidence from the Franklin community. Twenty additional variants were detected that are potentially pathogenic tier-2 or tier-3. ### Conclusions The community data-driven and sharing approaches facilitate generating inclusive and equitable ethnically based carrier screening panels. This approach identified new PFVs missing from currently available panels and highlighted variants that may require reclassification. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40246-023-00472-w. ## Background Carrier screening aims to identify individuals or couples at risk of having offspring affected with a serious heritable autosomal recessive or X-linked disorder. Couples at risk can then receive personalized counseling regarding their risk and consider their reproductive options, such as prenatal diagnosis or preimplantation genetic testing. The 2015 recommendations for carrier screening by the American College of Medical Genetics and Genomics (ACMG) focused on a limited number of genes and conditions [1]. In recent years, the rapid development of high-throughput next-generation sequencing (NGS) and its decreasing costs have allowed the simultaneous identification of sequence variants across many genes. As such, NGS has made carrier screening more widely accessible for a wide range of conditions in diverse populations. As a result, the ACMG recently proposed precise tier-based recommendations for carrier screening [2]. Carrier screening based on previous recommendations now falls in tiers 1 & 2, which include a subset of recommended genes for screening by the ACMG and the American College of Obstetricians and Gynecologists, and genes with a carrier frequency of ≥ $\frac{1}{100}$, respectively. Two additional screening tiers were added: tier 3 recommends carrier screening for variants with a carrier frequency of ≥ $\frac{1}{200}$ and variants for X-linked conditions for all pregnant patients and those planning a pregnancy, while tier 4 recommends screening for variants with a carrier frequency < $\frac{1}{200}$ when a pregnancy stems from a known or potential consanguineous relationship, or when warranted by family or personal medical history [2]. Previous screening recommendations were based on self-reported ancestry [1], which is error-prone and often biased. Therefore, the ACMG now recommends that carrier screening should be ethnicity- and population-neutral, and more inclusive of diverse populations [2]. While many pan-ethnic conditions are well-established, some genes carry pathogenic founder variants (PFVs) that are prevalent only in specific ethnic groups; such PFVs may not be assigned to the correct tier or may not be represented at all in the general carrier screening panels. Moreover, while the majority of ethnic groups are usually well-characterized, minority ethnic subpopulations are often less investigated, and thus, PFVs relevant to them may not be considered. Pan-ethnic carrier screening panels with better representation of different ancestries are required to improve carrier screening. We developed a community data-driven pipeline for creating a pan-ethnic carrier screening panel on top of Franklin data analysis platform (Genoox, Tel Aviv, Israel) [3]. Community data from 150,000 samples from various populations have been uploaded to the Franklin data analysis platform. The platform has been used in over 100 studies at genetics institutes around the world, varying from resolving variants in individual patients [4, 5], to automatically classifying variants from clinical variation databases [6], and identifying rare disease-associated variants in large patient cohorts [7, 8]. Moreover, the platform has been used clinically to analyze NGS data for several years in hundreds of genetics institutes worldwide, including in Israel. The current study aimed to demonstrate the usefulness of a community data-driven approach to create a pan-ethnic carrier screening panel that meets the new ACMG recommendations. The specific study's objectives were to: [1] apply the community data-driven pipeline to the Israeli population, which is ethnically diverse, and determine ancestry-specific carrier frequencies; [2] identify new PFVs that were previously missed and should be added to current screening panels; and [3] demonstrate the crucial role of sharing clinical evidence between community members to remove incorrectly classified variants and confirm true PFVs. ## Dataset The initial dataset included de-identified exome sequencing (ES) data from 6242 Israeli individuals from Tel-Aviv Sourasky Medical Center and Rambam Health Care Campus. All genetic testing was performed as a clinical service under standard clinical consent. The study was approved by the Tel Aviv Sourasky Medical Center institutional review board (no. 0039-15-TLV) and the Rambam Health Care Campus institutional review board (RMC-D-0259-22). Retrospective data collection from patients’ records was granted a waiver of informed consent, as all clinical data contained in this report have been de-identified. Sequencing was performed on a NovaSeq 6000 Sequencer (Illumina, San Diego, CA), X100 paired-end. Quality control steps included removing first- and second-degree relatives as well as duplicated samples based on their kinship coefficients. As the majority of samples were trios, affected probands were removed, and healthy parents were used. A set of 3061 unique ES samples remained for further analysis (Fig. 1).Fig. 1Pipeline for generation and application of a carrier screening panel. Arrows are used to indicate each processing step, and rectangles represent data generated after each step. Left panel: Ethnicity-specific cohort creation—initial dataset of 6242 exomes. a Quality control (QC) and related samples removal resulted in 3061 samples; b a machine learning model performed ethnicity/ancestry detection, as detailed in the methods section. The two largest ancestry groups were Ashkenazi Jewish (1013 samples) and Muslim Arabs (613 samples), as well as 11 additional inferred ethnicities with smaller numbers of samples. Middle panel: Prevalent PFV candidates—c intersection of the cohort variants with ClinVar and Franklin Community submissions resulted in 3847 reported P/LP variants. Variant frequencies were calculated per each ancestry for each of these P/LP variants; d in order to focus on novel PFVs only, variants present in existing carrier screening panels were removed. Removal of variants with a carrier frequency less than $\frac{1}{200}$ in the Ashkenazi Jewish or Muslim Arab ancestries resulted in 195 candidate tier 2 or 3 variants (Additional file 1: Table S2). Right panel: Curation and novel PFV detection—e a semi-automated process to filter out variants with an overall gnomAD frequency > $0.5\%$ or with three or more homozygous counts, or variants associated with mild conditions, resulted in 43 strong candidates for novel PVFs; f retrieval of real-world evidence from Franklin community members with homozygous samples and evidence from the literature resulted in eight novel curated PFVs ## Sample analysis Each sample was processed in order to find carrier P/LP variants. The entire bioinformatics pipeline from FASTQ files to final results was performed using the Franklin genetic analysis platform, as previously described [9]. In brief, we aligned FASTQ files against the GRCh37/hg19 reference genome with BWA version 0.7.17 [10]; variant calling for single nucleotide variants and indels was performed using GATK version 4.0.12.0 and FreeBayes version 1.3.1 [11, 12]. Sequence variant annotation and automated classification were performed using Franklin according to ACMG guidelines [13, 14], data from curated sources (e.g., ClinVar), scientific literature, and Franklin community members’ variant classifications. ## Ancestry inference Machine learning was used to predict each individual's most likely ancestry so that PFVs could be examined for each subpopulation separately. A subset of 567 of the 3061 samples with self-reported ethnicities was used as a training subset. We performed a principal component analysis with 22,952 common exonic variants; to mitigate bias due to false self-reported ethnicities outlier samples that did not cluster with their reported ancestries were removed. We trained a model using the first ten principal components and then used this model to predict the ancestry of all samples. Details on the algorithms can be found in Additional file 1. ## Candidate PFV selection Variants reported as P/LP by Franklin community members or ClinVar submissions were selected and compared with variants in an existing carrier screening panel dedicated to the Israeli population that is commonly used [15], in order to find novel PFVs (i.e., pathogenic variants not present in the existing panels). Frequencies of the candidate PFVs were calculated for each of the Israeli subpopulations based on the data of the cohort’s 3061 samples. Additional relevant annotations were used through the Franklin platform, such as variant-specific publications, associated conditions and phenotypes, carrier frequencies, and the number of homozygotes in other populations and the Franklin community database. These annotations were used to exclude variants that are unlikely to be PFVs using a semi-automated process in which variants matching the following conditions were filtered out: an overall gnomAD frequency of > $0.5\%$; variants with three or more homozygous counts; or variants associated with mild conditions. To confirm pathogenicity, suspected variants were manually curated using evidence and clinical data from samples in the Franklin community database [16]. Using Franklin community variant matching [16], we contacted Franklin community members that had previously observed these variants in a homozygous state, or members that shared evidence about these variants, to determine whether they were variants of unknown significance (VUS) or bona fide P/LP variants. For accurately calculating the carrier frequency and determining the tier of the final candidate variants, we removed for each variant the samples of parents of probands that were homozygous or compound heterozygous for that variant from the frequency calculations. ## Comparison with existing carrier screening panels Finally, we compared these variants with existing carrier screening panels that are currently used. Two types of comparisons were performed: [1] four commercial NGS-based carrier screening panels: two Ashkenazi Jewish carrier screening panels with 64 (Sema4, Stamford, CT, USA) [17], and 48 genes (Inheritest, Labcorp, Burlington, NC, USA) [18], and two pan-ethnic carrier screening panels with 502 (Sema4 Elements, Sema4) [19], and 302 genes (Invitae, San Francisco, Ca, USA) [20]; and [2] genotype-based panel which was developed specifically for the Israeli population [15]. ## Novel tier 2 and tier 3 PFV detection The automatically inferred ancestry grouped the samples into 13 different ancestries (Additional file 1: Table S1). This was done using machine learning with the most probable predicted ancestry with the strongest ethnic indicators (see Additional file 1). The largest number of samples was classified as Ashkenazi Jewish ($$n = 1011$$), followed by Muslim Arabs ($$n = 613$$). Note that non-Ashkenazi Jews (such as Sephardic Jews), as well as non-Muslim Arabs, were classified in various separate ancestry groups by machine learning. We focused on the Ashkenazi Jewish and Muslim Arab ancestries due to their larger sample size, making their frequencies and results more accurate. The eleven other ancestry groups were too small to analyze for accurate carrier frequency calculations. The pipeline started with 3847 variants that had been previously classified as P/LP in the 3061 samples. Initial filtration for reported P/LP variants that were not in the Israeli panel [15] and with carrier frequency ≥ $\frac{1}{200}$ in either the Ashkenazi Jewish or Muslim Arab ancestry groups resulted in 195 variants (Additional file 1: Table S2). Additional filtration steps for potential variants that should be included in carrier screening resulted in 25 potential novel PFVs in the Ashkenazi Jewish group and 18 potential PFVs in the Muslim Arab group, i.e., in total, 43 potential PFVs were identified in these two groups (Fig. 1, Additional file 1: Table S3). To confirm pathogenicity, manual curation was done for the 43 potential PFVs using evidence from the Franklin community. We were able to retrieve additional evidence and confirmation of pathogenicity for nine ($20.9\%$) of the 43 variants (Table 1). Six of these nine variants were found in homozygous cases in the Franklin community and were indeed causal for their associated phenotypes, thus confirming their pathogenicity. Three variants were determined to be P/LP based on literature evidence only. Of the additional 34 variants, ten were excluded as they appeared in a homozygous state in healthy individuals or without an associated phenotype. Four additional variants were excluded because they were associated with a mild condition, including stationary night blindness (GPR179, MIM #614565), mild cystinuria (SLC7A9, MIM #220100), postaxial polydactyly (IQCE, MIM# 617642) and pseudoxanthoma elasticum (ABCC6, MIM #264800). The remaining 20 variants did not have sufficient evidence to be further classified, thus remaining of uncertain significance and, for now, should not be included in carrier screening panels. Table 1Eight new pathogenic founder variants identified in two Israeli populationsPopulation variantDisease (MIM #)EvidenceCarrier frequencyTierAshkenazi JewsWFS1 NM_006005.3:c.1672C > T; p.Arg558CysWolfram syndrome 1 (#606201)Five individuals homozygous for this variant were observed in the Franklin community. Three of the patients’ ages ranged between 42 and 49. Phenotypes included Maturity Onset Diabetes of the Young (MODY, in all patients), optic atrophy (in three patients), urinary and fecal incontinence, cerebellar atrophy on MRI, and hearing impairment. This variant was previously reported as a causal variant for Wolfram syndrome with milder phenotypes with only $\frac{1}{8}$ presenting with optic atrophy [30]. Our results show that optic atrophy might be more frequent than previously thought ($\frac{3}{5}$)References: [21, 31]$\frac{1}{682}$PCDH15 NM_001384140.1:c.733C > T; p.Arg245Usher syndrome, type 1F (#602083)Appeared in two affected cases in the Franklin communityReferences: [32, 33]$\frac{1}{1133}$DDX11 NM_030653.4:c.1763-1G > C; p.?Warsaw breakage syndrome (#613398)Appeared in two affected cases in the Franklin community. It was recently added to the recommended Israeli variants panel (independent of our findings)References: [26, 34]$\frac{1}{1133}$EYS NM_001142800.2:c.9286_9295delGTAAATATCG; p.Val3096Leufs28Retinitis pigmentosa 25 (#602772)Appeared in two affected cases in the Franklin communityReference: [35]$\frac{1}{1133}$VPS41 NM_014396.4:c.1984C > T; p.Arg662Spinocerebellar ataxia, autosomal recessive 29 (#619389)A nonsense variant where LOF is the disease mechanism, for which no community evidence was foundReference: [36]$\frac{1}{1133}$TKT NM_001064.4:c.769_770insCTACCTCCTTATCTTCTG; p. Trp257delinsSerThrSerLeuSerSerGlyShort stature, developmental delay, and congenital heart defects (#617044). Also known as: Transketolase deficiencyNo community evidence was foundReference: [37]$\frac{1}{1453}$CLCN1 NM_000083.3:c.1238 T > G; p.Phe413CysCongenital myotonia, autosomal recessive (#255700)No community evidence was foundReferences: [38, 39]$\frac{1}{1453}$Muslim ArabsACSF3 NM_001243279.3:c.1470G > C; p.Glu490AspCombined malonic and methylmalonic aciduria (#614265)Appeared in two Muslim Arab cases in the Franklin community as a causal variantReference: [40]$\frac{1}{1543}$MIM Mendelian Inheritance in Man We performed a manual inspection of samples of parents of probands that were homozygous or compound heterozygous for a PFV, to improve accuracy of carrier frequency calculations. Upon this inspection, four samples of Muslim Arab parents were removed. These were parents of probands homozygous for NM_003682.4(MADD):c.2816 + 1G > A; p.?, which was a tier 2 variant. Recalculation of the carrier frequency resulted in tier 3. In addition, a single Jewish Ashkenazi parent of a proband compound heterozygous for NM_001384140.1(PCDH15):c.733C > T; p.Arg245 was removed, which did not change its tier. Overall, the analyses resulted in a final list of eight new tier 2 or tier 3 P/LP variants from either Ashkenazi Jewish (seven PFVs) or Muslim Arab (one PFVs) ethnic groups, which were not yet available in existing Israeli carrier screening panels (Table 1). ## Clinically significant variants in the Ashkenazi Jewish and Muslim Arab ethnic groups The full list of novel PFVs and their evidence of pathogenicity can be found in Table 1. Of these, a single PFV was a tier 2 variant, and the rest were tier 3. The tier 2 variant, WFS1 (NM_006005.3): c.1672C > T; p.Arg558Cys, was also found in the Ashkenazi Jewish cohort, with a carrier frequency of $\frac{1}{67.}$ While this variant had previously been detected in a homozygous state in individuals with Wolfram syndrome (MIM #222300, insulin-dependent diabetes mellitus and optic atrophy), it was reportedly associated with a mild phenotype, and only 1 of 8 cases had optic atrophy [21]. Our results, however, demonstrate that this variant was present in five affected individuals in the Franklin community, all diagnosed with maturity onset diabetes of the young (MODY), three of whom had optic atrophy, and one patient also displayed urinary and fecal incontinence, cerebellar atrophy, and hearing impairment. In addition, one of the PFVs detected, DDX11 (NM_030653.4):c.1763-1G > C, in association with Warsaw breakage syndrome (MIM #613398), has already been included in the national screening panel in 2021 by the Israeli Ministry of Health [22], further validating our results and methods. An additional potential tier 2 variant, PTPN23 (NM_015466.4): c.3884_3886delAGA; p.Ala1292del, was found in Ashkenazi Jewish samples with a carrier frequency of $\frac{1}{53.}$ Variants in PTPN23 are associated with an autosomal recessive neurodevelopmental disorder and structural brain anomalies with or without seizures and spasticity (MIM #618890). Indeed, through the Franklin community, it was established that a girl with severe neurodevelopmental disease was homozygous for this variant and that the case had been published [23]. Recently (April 2022), a submission for this variant was added to ClinVar (SCV002345885.1), classifying it as benign. After contacting the submitting laboratory, they shared that the evidence for this submission was based on the high frequency in the Ashkenazi Jewish subpopulation, which was considered sufficient for benign classification, without observed evidence from healthy homozygous individuals in their database. ## Variants identified in smaller ancestry groups All eleven other ancestry groups were each represented by small numbers of individuals (25 to 309) (Additional file 1: Table S1). Consequently, a relatively small number of variants were detected in these groups, and they were too small to estimate true carrier frequencies. Nonetheless, we were able to identify several potential PFVs in these ancestry groups. For example, in the Druze ancestry group, we identified 21 potential PFVs (Additional file 1: Table S4). One of these is the HBB (NM_000518.5): c.-136C > G variant, which was detected in four out of 301 individuals (carrier frequency of $\frac{1}{75}$). This variant was previously reported in a Syrian Druze family with β-thalassemia (MIM #613985) [24]. However, a larger dataset is required to accurately determine the carrier frequency and the tier level of these potential PFVs in the smaller ancestry groups. ## Comparison with existing NGS-based carrier screening panels We compared the 43 potential PFVs (the eight confirmed novel PFVs, as well as the remaining candidate 34 VUS/mild phenotype variants, which were reported as P/LP) with variants in commercially available carrier screening panels. Although NGS-based panels are expected to capture more pathogenic variants, they may also yield uncertain or falsely reported variants that can burden the interpretation process, often due to a false report in ClinVar or a submission without detailed evidence. We initially compared the 25 potential Ashkenazi Jewish PFVs we identified with two panels dedicated to the Ashkenazi Jewish population—Sema4 and Labcorp—containing 64 and 48 genes each, respectively (Additional file 1: Table S3). None of the 25 variants (including the seven confirmed novel PFVs) observed in Ashkenazi Jews were present, since the genes were not included in these panels. We also compared the 43 candidate PFVs with two larger pan-ethnic panels—Sema4 and Invitae—that include 502 and 302 genes each, respectively (Additional file 1: Table S3). Only 15 of the variants were present in these panels, of which three were confirmed PFVs, five had benign supporting evidence through the Franklin community, and seven were VUS. ## Validation and confirmation of known PFVs To assess the sensitivity of our pipeline, i.e., our ability to detect all known tier 2 and tier 3 variants, we used a commonly applied Israeli mutation-based carrier screening panel as a reference set [15]. We identified tier 2 or 3 variants from the existing carrier screening panel by calculating their estimated carrier frequency using an independent dataset taken from the gnomAD Ashkenazi Jewish population dataset and selecting those with a carrier frequency > $\frac{1}{200.}$ This resulted in a selection of 55 variants, 53 ($96\%$) of which were also included in our initial dataset for candidate PFVs in the Ashkenazi Jewish population (Additional file 1: Table S5). Two of the variants did not occur in our Ashkenazi Jewish dataset—one was a deep intronic variant in the CFTR gene, which was not covered in our exome-based dataset, and a second variant was not included in our dataset as it had been previously reported as a VUS rather than pathogenic [25]. Of the 53 overlapping variants, 42 were assigned to tiers 2 or 3 in our dataset (carrier frequency > $\frac{1}{200}$), while 11 were in tier 4 with carrier frequency between $\frac{1}{250}$ to $\frac{1}{1011}$ (Additional file 1: Table S5). These differences in tier classification (tiers 2 and 3 compared with tier 4) can be explained by the sample sizes of the datasets (gnomAD and ours) and by heterogeneity within the Ashkenazi Jewish population [26]. In an additional validation, we compared our 25 final Ashkenazi Jewish potential novel PFVs, and calculated their tier assignment using the independent dataset from gnomAD. Twenty-three of the 25 variants were also assigned to tiers 2 or 3 when using gnomAD frequencies, while the remaining two were tier 4 in gnomAD, but close to tier 3, as they appeared with a carrier frequency of $\frac{1}{215}$ (MYO15A variant) and $\frac{1}{216}$ (FKRP variant) (Additional file 1: Table S3). ## Discussion The latest ACMG recommendations state that “All pregnant patients and those planning a pregnancy should be offered tier 3 carrier screening” and that “carrier screening paradigms should be ethnicity- and population-neutral, and more inclusive of diverse populations to promote equity and inclusion” [2]. These recommendations are expected to strengthen the current trend of both extending the number of genes which should be tested and the scope of the examined variants in each gene, from a limited number of known/common variants to full gene sequencing. Providing such robust tests that support these scopes requires an evidence-based and careful curation process to avoid disclosure of VUS or likely benign variants while making sure no proper P/LP variants are misclassified. Carrier screening is well established and widely used in Israel, and a wealth of knowledge has accumulated throughout the years on ethnically based pathogenic variants, which are major strengths for our study. The Ministry of Health (MOH) established the Israeli genetic carrier screening program as early as the 1980s [27–29]. The program gradually expanded over the years to include tier 1 testing and is offered to all couples in Israel based on their ethnic backgrounds and provided without out-of-pocket expense. Tier 2 tests are subsidized for $80\%$ of the population who acquire supplemental health insurance. Thus, the wide availability of tier 1 & 2 carrier testing made Israel a unique place for the current study. The valuable insights gained from tier 1 & 2 testing serve as the basis for evaluating the impact of the recent ACMG recommendations. Furthermore, the data accumulated via clinical exome testing over the last five years allowed us to compare the current tier 1 & 2 testing platform against real-world NGS data. The key focus of this work was to develop a pipeline for creating pan-ethnic carrier screening panels following the recent ACMG recommendations and to demonstrate its clinical utility and validation by comparison to well-established commercial carrier tests. We show that a data-driven pipeline, which leverages real-world data, is crucial for establishing a complete and accurate panel. With a relatively small dataset, we managed to detect > $98\%$ of the variants being tested today and identify additional variants which may warrant further review and consideration regarding their inclusion in future carrier screens. Moreover, a substantial contribution from the Franklin community supported benign evidence for candidate variants. Contacting community members facilitated the decision of whether a variant should be included or excluded (e.g., due to mild phenotypes, low penetrance, or benign classification). This shows that public-only datasets, and even commercially available tests, may not be accurate enough to determine the best composition of a carrier screening panel. Specifically, our results show that NGS allows for a standardized platform for the diverse Israeli ethnic subpopulations and can unveil novel tiers 2 and 3 P/LP PFVs. When comparing our findings with existing carrier screening panels for the Ashkenazi Jewish population, we noted that none of the seven novel Ashkenazi Jewish PFVs we observed were included in the existing panels, showing the limitations of targeted carrier screening panels even in well-studied populations. In addition, even when comparing the eight novel PFVs (including both Ashkenazi Jewish and Muslim Arab) against full-gene expanded carrier screening panels, six variants (corresponding to six genes) were not included in these panels. The additional three PFVs, which were covered in the pan-ethnic panels, show the sensitivity advantage when using NGS panels. Conversely, when comparing the 43 potential PFVs with variants in various commercially available carrier screening panels, we found that these pan-ethnic panels included 12 VUS variants, five of which had benign supporting evidence through the Franklin community but were reported as P/LP by another clinical laboratory or in the literature. These findings demonstrate the additional workload and possibly false-positive reports that can derive from whole-gene NGS-based panels. Similarly, the case of the PTPN23 potentially novel PFV, which was classified as benign in ClinVar, demonstrates how PFVs can potentially be erroneously classified, solely based on their high frequency in certain subpopulations, which is not uncommon for PFVs. These potential misclassifications can be reduced by evidence-sharing and continuous investigation of candidate PFVs, until reaching a consensus. This study has several limitations. First, the determined carrier frequencies may be somewhat overestimated as the samples were derived from affected families. However, as the families came for a variety of conditions, this limitation is unlikely to affect the identification of specific PFVs, but may affect their assignment to a specific tier. Second, although we used a large number of unique unrelated samples ($$n = 3061$$), these were derived from individuals of various ancestries. As such, most ethnic groups were not large enough to accurately estimate carrier frequencies, and consequently, we only estimated carrier frequencies in the two largest groups (Ashkenazi Jews and Muslim Arabs). This limitation will be overcome with time as the numbers of ES tests in the community database are growing, which will eventually allow the analysis of the less common ethnic groups. Also, our study did not account for mixed ancestries and used the most probable ethnicity assignment, which may impact the ancestry-specific frequencies. Therefore, the carrier frequencies might be somewhat higher or lower in these subgroups. Nevertheless, to verify our results and tier assignment, we calculated the carrier frequency of our eight new PFVs in Ashkenazi Jews using the Ashkenazi Jewish subpopulation in the gnomAD database, which resulted in similar tier assignments and validated our results. Third, our approach is based on large numbers of previously identified variants combined with evidence from a community database, meaning that this approach is not specifically geared to the identification of novel P/LP variants in known disease-causing genes or potential P/LP variants in novel genes. However, this limitation also exists for currently employed screening panels and thus is not unique to the approach we developed. Last, this study relied on an ES dataset which precludes the analysis of intragenic or deep noncoding regions and structural variations. This limitation can be overcome with the increasing use of genome sequencing. In conclusion, we demonstrated the proof-of-concept utility of a community data-driven pipeline that meets the current ACMG recommendations in two common Israeli ethnic groups. We demonstrated that our pipeline can identify various PFVs, including those not yet incorporated in existing Israeli carrier screening panels. Moreover, this approach facilitated the proper reclassification of variants in existing screening panels. As was shown for the Israeli population, the Franklin pipeline is available for developing pan-ethnic carrier screening panels in other countries. With this successful proof-of-concept NGS-based expanded carrier screening, and the foreseeable expansion of the community database, we will be able to apply our panel to all Israeli subpopulations to create an inclusive and equitable carrier screening panel. ## Supplementary Information Additional file 1. Supplementary Methods. Descripton of ancestry inference method. Table S1. Distribution of ancestries in the Israeli cohort. Table S2. 195 P/LP variants with carrier frequency ≥ $\frac{1}{200}$ in either the Ashkenazi Jewish or Muslim Arab ancestry groups that are present in existing carrier screening panels. Table S3. 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--- title: 'Deprescribing psychotropic medicines for behaviours that challenge in people with intellectual disabilities: a systematic review' authors: - Danielle Adams - Richard P. Hastings - Ian Maidment - Chetan Shah - Peter E. Langdon journal: BMC Psychiatry year: 2023 pmcid: PMC10044393 doi: 10.1186/s12888-022-04479-w license: CC BY 4.0 --- # Deprescribing psychotropic medicines for behaviours that challenge in people with intellectual disabilities: a systematic review ## Abstract ### Background Clear evidence of overprescribing of psychotropic medicines to manage behaviours that challenges in people with intellectual disabilities has led to national programmes within the U.K. such as NHS England’s STOMP to address this. The focus of the intervention in our review was deprescribing of psychotropic medicines in children and adults with intellectual disabilities. Mental health symptomatology and quality of life were main outcomes. ### Methods We reviewed the evidence using databases Medline, Embase, PsycINFO, Web of Science, CINAHL and Open Grey with an initial cut-off date of 22nd August 2020 and an update on 14th March 2022. The first reviewer (DA) extracted data using a bespoke form and appraised study quality using CASP and Murad tools. The second reviewer (CS) independently assessed a random $20\%$ of papers. ### Results Database searching identified 8675 records with 54 studies included in the final analysis. The narrative synthesis suggests that psychotropic medicines can sometimes be deprescribed. Positive and negative consequences were reported. Positive effects on behaviour, mental and physical health were associated with an interdisciplinary model. ### Conclusions This is the first systematic review of the effects of deprescribing psychotropic medicines in people with intellectual disabilities which is not limited to antipsychotics. Main risks of bias were underpowered studies, poor recruitment processes, not accounting for other concurrent interventions and short follow up periods. Further research is needed to understand how to address the negative effects of deprescribing interventions. ### Trial registration The protocol was registered with PROSPERO (registration number CRD42019158079) ### Supplementary Information The online version contains supplementary material available at 10.1186/s12888-022-04479-w. ## Background Intellectual disabilities are a group of diverse developmental conditions characterised by lower intellectual functioning (usually an IQ of less than 70), and significant impairments of social or adaptive functioning, with an associated onset during childhood [1]. It is relatively common for people with intellectual disabilities to develop behaviours that challenge, with a prevalence of around 10–$18\%$ in individuals accessing educational, health or social care services [2–4]. Behaviours that challenge - defined as culturally abnormal behaviour, placing a person at risk of harm to themselves and others - can significantly affect engagement with community amenities due to their duration, intensity, or frequency [5]. These behaviours can include aggression, self-harm, withdrawal, and disruptive or destructive behaviour, including behaviours which may bring the person into contact with the criminal justice system [6]. Prescribing psychotropic medications to treat mental illness may be clinically appropriate for individuals with intellectual disabilities [7, 8]. However, no psychotropic medicines have marketing authorisations for behaviours that challenge in the absence of mental health conditions, except for the short-term use of risperidone and haloperidol for behavioural and psychological effects of dementia. Despite this, behaviours that challenge are independently associated with increased use of psychotropic medication [4, 9]. Psychotropics, particularly if used over a long period of time without adequate review and monitoring, can cause significant harm including: anticholinergic burden, tardive dyskinesia [10, 11], weight gain, and development of metabolic syndrome increasing morbidity and mortality [12]. Therefore, reducing the use of psychotropic medicines for individuals with intellectual disabilities and behaviours that challenge is indicated for reasons of health and quality of life, in addition to being a current policy priority [13, 14]. The purpose of the present paper is to report findings from a systematic review addressing the following question: What are the effects of deprescribing psychotropic medicines as a part of a care pathway or treatment plan for people of all ages with intellectual disabilities and behaviours that challenge? A previous systematic review of deprescribing psychotropic medicines in adults with intellectual disabilities was restricted to antipsychotic medicines involving databases searched between 1st January 1990 and 1st March 2016 [15]. Our review extends this evidence base by including all psychotropic medicines used with children or adults and including research since 2016. ## Methods The review was conducted and reported in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines 2020. The protocol was registered with the international database of prospectively registered systematic reviews in health and social care (PROSPERO) (registration number CRD42019158079). ## Selection criteria The eligibility criteria were developed in accordance with the Population, Intervention, Comparator, Outcome (PICO) framework. The population were people with intellectual disabilities prescribed psychotropic medicines of any class for the management of behaviours that challenge. Studies that included adults or children with intellectual disabilities were included. Studies that included fewer than $50\%$ of participants with intellectual disabilities or where data relating to those participants with intellectual disabilities were not reported separately were excluded. The focus of the intervention had to be deprescribing of psychotropic medicines. Studies conducted in both inpatient settings and community settings were eligible for inclusion. Community settings included residential care as well independent living supported by paid or unpaid carers. The primary outcomes were changes in behaviours that challenge and secondary outcomes were changes in quality of life measures or other outcomes such as mental health symptomatology (see Table 1).Table 1Secondary outcomesNumber and frequency of hospital admissionsNumber and frequency of referrals to intensive nursing supportNumber of placement breakdownsNumber of dose increases or dose decreases of psychotropic medicinesNumber of new psychotropic medicines initiated, or psychotropic medicines withdrawnFrequency, severity, or impact of behaviour that challengesNumber of hours of social care supportEstimates of costs associated with deprescribing approachesChanges in physical health parameters Any experimental, quasi experimental, observational, or case report study reporting relevant quantitative data was included. For the synthesis, studies were grouped according to study design: randomised controlled trials, other comparison designs, pre post studies, longitudinal studies, and descriptive case reports. Studies reporting both individual participant-level data and summary data estimates were included. There were no restrictions on language or date of publication. We did not include abstracts and conference presentations. ## Search strategy Six electronic databases were searched: Medline, Embase, PsycINFO, Web of Science, CINAHL and Open Grey by the lead reviewer (DA) with a cut-off date of 21st August 2020 (see Table 2 for search strategy). Update searches were carried out on 14th March 2022 with one further case study identified for inclusion. Table 2Search strategy for medline. Search terms were grouped by [1] “intellectual disabilities, [2] “psychotropic medication”, and [3] “deprescribing”, along with their synonyms. Search terms from within each of groups 1, 2 and 3 were separated using the Boolean operator “OR”. Search terms from groups 1, 2 and 3 were then combined using the Boolean operator “AND”. An example of the full search can be seen in Table 1 below1 ID OR LD OR PMLD OR PIMD OR PDD OR ASD OR autis* OR autism OR Asperger* OR angelman OR PDD NOS OR FASD OR neurofibromatosis OR hypothyroid* OR phenylketonuria OR rubinstein-taybi OR digeorge OR lesch-nyhan OR SEN OR SEND OR DD OR handicap* OR disab* OR (intellectual* adj1 disab) OR (learning adj1 disab) OR (intellectual* adj1 deficien) OR (intellectual adj1 impair) OR (learning adj1 difficult) OR (learning adj1 deficien) OR (learning adj1 impair) OR (mental* adj1 retard) OR (mental adj1 deficien) OR (mental adj1 handicap) OR (intellectual adj1 developmental adj1 disab) OR (intellectual adj1 development adj1 disorder) OR (down adj1 syndrome) OR (fragile adj1 x adj1 syndrome) OR (fragile adj1 x) OR (william adj1 syndrome) OR (angelman adj1 syndrome) OR (profound* adj2 multiple adj1 learning adj1 disab) OR (profound adj1 intellectual* adj1 multiple adj1 disab) OR (Rett adj1 syndrome) OR(overgrowth adj syndrome) OR (Asperger adj1 syndrome) OR (autis* adj1 spectrum adj1 disorder) OR (pervasive adj1 developmental* adj1 disorder) OR (pervasive adj1 developmental adj1 disorder* adj2 otherwise specified) OR (f?etal adj1 alcohol adj1 syndrome) OR (f?etal adj1 alcohol) OR (prenatal adj1 alcohol adj1 exposure)OR (velocardiofacial adj1 syndrome) OR (klinefelter adj1 syndrome) OR (childhood adj1 disintegrative adj1 disorder) OR (smith adj1 magenis) OR (cri adj1 du adj1 chat) OR (cornelia adj1 de adj1 lange) 36 (de adj1 lange) OR (genetic adj1 disorder) OR(static adj1 encephalopathy) OR (complex adj1 need) OR (special adj1 education* adj1 need) OR (special adj1 need) OR (special adj1 education* adj1 need* adj2 disabilit) OR (special adj1 need adj2 disabilit) OR (developmental adj1 disabilit) OR (neurodevelopmental adj1 disorder) OR (developmental adj1 disorder) OR (neurodevelopmental adj1 disabilit) OR (development adj1 delay) OR (development adj1 difficult) OR (developmental adj1 impair) OR (abnormal adj1 develop) OR (prader adj1 willi adj1 syndrome)2 psychotropic OR antidepressant* OR anti-depressant* OR antipsychotic* OR anti- psychoticor anticonvulsant OR anti-convulsant* OR antimanic* OR anti-manic* OR antiepileptic* OR anti-epileptic* OR hypnotic* OR SSRI* OR anxiolytic* OR benzodiazepine* OR neuroleptic* OR alprazolam OR fludiazepam OR camazepam OR nordazepam OR etizolam OR clotiazepam OR cloxazolam OR tofisopam OR bentazepam OR loprazolam OR zentiva OR lormetazepam OR dormagen OR niravam OR xanax OR medazepam OR potassium clorazepate OR oxazepam OR bromazepam OR chlordiazepoxide OR chlordiazachel OR libritabs OR lygen OR librium OR oxazepam OR ketazolam OR prazepam OR halazepam OR pinazepam OR adinazolam OR serax OR zaxopam OR emylcamate OR mebutamate OR meprobamate OR benzoctamine OR amosene OR bamate OR equanil OR mepriam OR meprospan OR miltown OR neuramate OR tranmep OR hydroxyzine OR captodiame OR mephenoxalone OR gedocarnil OR etifoxine OR fabomotizole OR atomoxetine OR strattera OR guanfacine OR intuniv OR tenex OR amfetamine OR metamfetamine OR pemoline OR fencamfamine OR modafinil OR fenozolone OR(fenetylline OR armodafinil OR solriamfetol OR caffeine OR propentofylline OR meclofenoxate OR pyritinol OR piracetam OR deanol OR fipexide OR citicoline OR acetylcarnitine OR aniracetam OR idebenone OR prolintane OR pipradrol OR pramiracetam OR adrafinil OR vinpocetine OR tetramethylglycoluril OR phenibut OR oxiracetam OR pirisudanol OR linopirdine OR nizofenone OR dexmethylphenidate OR methylphenidate OR ritalin OR concerta OR delmosart OR equasym OR matoride OR medikinet OR xaggitin OR xenidate OR adhansia OR aptensio OR cotemplar OR daytrana OR dexmethylphenidate OR focalin OR jornay OR metadate OR methylin OR quillichew OR quillivant OR dexamfetamine OR amfexa OR elvanse OR lisdexamfetamine OR vyvanse carbamazepine OR tegretol OR carbagen OR carbatrol OR carnexiv OR epitol OR equetro OR hetrazan OR teril OR eslicarbazepine OR valproate OR belvo OR depacon OR depakene OR stavzor OR depakote OR epilim OR episenta OR epival OR valpromide OR aminobutyric acid OR progabide OR phenytoin OR ethotoin OR mephenytoin OR fosphenytoin OR paramethadione OR trimethadione OR ethadione OR ethosuximide OR phensuximide OR mesuximide OR diazepam OR valium OR dizac OR qpam OR diastat OR mysoline OR primidone OR zonisamide OR zonegran OR vigabatrin OR sabril OR vigadrone OR rufinamide OR inovelon OR banzel OR tiagabine OR gabitril OR topamax OR topiramate OR qsymia OR qudexy OR trokendi OR lamotrigine OR lamictal OR levetiracetam OR keppra OR desitrend OR elepsia OR spritam OR phenobarb* OR methylphenobarbital OR barbexaclone OR metharbital OR oxcarbazepine OR oxtellar OR trileptal OR brivaracetam OR briviact OR clonazepam OR rivotril OR klonopin OR sultiame OR phenacemide OR felbamate OR pheneturide OR zonisamide OR stiripentol OR lacosamide OR cannabidiol ORcarisbamate OR beclamide OR retigabine OR perampanel OR clobazam OR onfi OR sympazan OR frisium OR perizam OR tapclob OR zacco OR pentobarbital OR amobarbital OR butobarbital OR bartbital OR aprobarbital OR secobarbital OR talbutal OR vinylbital OR vinbarbital OR cyclobarbital OR heptabarbital OR reposal OR methohexital OR hexobarbital OR thiopental OR etallobarbital OR allobarbital OR proxibarbal OR choral hydrate OR choralodol OR (dichloralphenazone OR paraldehyde OR lorazepam OR ativan OR loraz OR temazepam OR restoril OR temaz OR nitrazepam OR mogadon OR flurazepam OR flunitrazepam OR estazolam OR midazolam OR brotizolam OR quazepam OR loprazolam OR doxefazepam OR cinolazepam OR remimazolam OR glutethimide OR methyprylon OR pyrithyldioneOR dalmane OR triazolam OR zopiclone OR zimovane OR zolpidem OR stilnoct OR zolpimist OR tovalt OR intermezzo OR edluar OR ambien OR zaleplon OR sonata OR ramelteon OR tasimelteon OR melatonin OR circadin OR slenyto OReszopiclone OR lunesta OR methaqualone OR clomethiazole OR bromisoval OR carbromal OR scopolamine OR propiomazine OR triclofos OR hexapropymate OR ethchlorvynol OR bromides OR apronal OR valnoctamide OR methylpentynol OR niaprazine OR dexmedetomidine OR suvorexant OR lyrica OR pregabalin OR lecaent OR alaxid OR alzain OR gabapentin OR gralise OR horizant OR neurontinOR lecomig OR lithium OR camcolit OR liskonum OR eskalith OR lithane ORlithobid OR lithonate OR lithobid OR benperidol OR anquil OR chlorpromazine OR largactil OR promapar OR sonazine OR thorazine OR asenapine OR secuado OR saphris OR sycrest OR flupentixol OR depixol OR psytixol OR flupenthixol OR clopenthixol OR chlorprothixene OR tiotixene OR loxapine OR adasuve OR loxitane OR levomepromazine OR levoprome OR promazine OR sparine OR acepromazine OR cyamemazine OR chlorproethazine OR triflupromazine OR vesprin OR pericyazine OR dixyrazine OR thiopropazate OR acetophenazine OR thioproperazine OR butaperazine OR perazine OR piperazine OR thioridazine OR mesoridazine OR pipotiazine OR periciazine OR perphenazine OR promethazine OR sominex OR phenergan OR trilafon OR pimozide OR orap OR fluspirilene OR penfluridol OR prochlorperazine OR compazine OR compro OR procomp OR sulpiride OR tiapride OR remoxipride OR sultopride OR trifluoperazine OR stelazine OR zuclopenthixol OR clopixol OR fluphenazine OR modecate OR permitil OR prolixin OR veralipride OR levosulpiride OR amisulpride OR solian OR aripiprazole OR abilify OR aristada OR clozapine OR fazaclo OR versacloz OR denzapine OR clozaril OR zaponex OR lurasidone OR latuda OR iloperidone OR cariprazine OR brexpiprazole OR pimavanserin OR paliperidone OR invega OR xeplion OR trevicta risperidone ORrisperdal OR prothipendyl OR mosapramine OR olanzapine OR zyprexa OR zylasta OR clotiapine OR quetiapine OR mintreleq OR brancico OR biquelle OR atrolak OR alalquet OR seroquel OR haloperidol OR haldol OR trifluperidol OR melperone OR moperone OR zotepine OR moperone OR pipamperone OR bromperidol OR benperidol OR droperidol OR fluanisone OR oxypertine OR molindone OR sertindole OR ziprasidone OR buspirone OR buspar OR sertraline OR zoloft OR lustral OR fluoxetine OR prozac OR olena OR sarafem OR selfemra OR paroxetine OR brisdelle OR paxil OR pexeva OR seroxat OR citalopram OR celexa OR lexapro OR cipramil OR fluvoxamine OR faverin OR luvox OR duloxetine OR cymbalta ORdrizalma OR escitalopram OR zimeldine OR alaproclate OR etoperidone cipralex OR venlafaxine OR alventa OR amphero OR depefex OR majoven OR politid OR sunveniz OR venaxx OR vencarm OR venladex OR venlalic OR vensir OR venzipOR viepax OR effexor OR desvenlafaxine OR khedezla OR pristiq OR clomipramine OR anafranil OR janimine OR pramine OR dibenzepin OR presamine OR desipramine OR imipramine OR tofranil OR opipramol OR protriptyline ORiprindole OR melitracen OR butriptyline OR amoxapine OR dimetacrine OR amineptine OR maprotiline OR quinupramine OR amitriptyline OR amitid OR amitril OR elavil OR endep OR dosulepin OR dothiepin OR doxepine OR mianserin OR trazodone OR oxitriptan OR nomifensine OR nefazodone OR minaprine OR bifemelane OR viloxazine OR oxaflozane OR bupropion OR medifoxamine OR tianeptine OR pivagabine OR levomilnacipran OR milnacipran OR gepirone OR duloxetine OR vilazodone OR molipaxin OR hyperici herba OR esketamine OR desyrel OR oleptro OR trialodine OR trimipramine OR surmontil OR lofepramine OR thioridazine OR melleril OR aventyl OR pamelor OR nortriptyline ORtranylcypromine OR advanz OR parnate OR phenelzine OR nardil OR nialamide OR iproniazide OR iproclozide OR isocarboxazid OR marplan OR moclobemide OR manerix OR toloxatone OR reboxetine OR edronax OR mirtazapine OR remeron OR zispin OR clomipramine OR vortioxetine OR brintellix OR trintellix OR tryptophan OR agomelatine OR valdoxan OR modafinil OR provigil OR armodafinil OR nuvigil OR norpramin OR pertofrane OR pamelor OR aventyl OR vivactil OR asendin OR ludikomil OR serzone OR zyban OR wellbutrin OR forfivo OR aplenzin OR contrave OR khedezla OR pristiq OR viibryd OR cylert OR focalin OR gemonil OR dilantin OR diphenylan OR phenytek OR peganone OR cerebyx OR mesantoin OR paradione OR tridione OR zarontin OR milontin OR aptiom OR phenurone OR felbatol OR zonegran OR diacomit OR vimpat OR fycompa OR antepar OR bryrel OR multifuge OR vermidol OR serentil OR inapsine OR moban OR geodon OR taractan OR adasuve OR loxitane OR fanapt OR rexulti OR nuplazid OR serax OR zaxopam OR centrax OR atarax OR orgatrax OR vistaril OR bamate OR amosene OR equanil OR mepriam OR meprospan OR miltown OR neuromate OR tranmep OR nembutal OR sarisol OR butabarb OR butalan OR butisol OR buticaps OR seconal OR prosom OR halcion OR rozerem OR triclos OR placidyl OR precedex OR belsomra OR fetzima OR savella OR (valproic adj1 acid) OR (psychotropic adj1 medicine) OR (psychotropic adj1 medication) OR (psychotropic adj1 drug) OR (psychotropic adj1 agent) OR (antidepressant adj1 medicine) OR (antidepressant adj1 medication) OR (antidepressant adj1 drug) OR (antidepressant adj1 agent) OR (anti-depressant adj1 medicine) OR (anti-depressant adj1 medication) OR (anti- depressant adj1 drug) OR (anti-depressant adj1 agent) OR (antipsychotic adj1 medicine) OR (antipsychotic adj1 medication) OR (antipsychotic adj1 drug) OR (antipsychotic adj1 agent) OR (anti-psychotic adj1 medicine) OR (anti-psychotic adj1 medication) OR (anti-psychotic adj1 drug) OR (anti-psychotic adj1 agent) OR (neuroleptic adj1 medicine) OR (neuroleptic adj1 medication) OR (neuroleptic adj1 drug) OR (neuroleptic adj1 agent) OR (anticonvulsant adj1 medicine) OR (anticonvulsant adj1 medication) OR (anticonvulsant adj1 drug) OR (anticonvulsant adj1 agent) OR (anti-convulsant adj1 medicine) OR (anti- convulsant adj1 medication) OR (anti-convulsant adj1 drug) OR (anti-convulsant adj1 agent)) OR ((antimanic adj1 medicine) OR (antimanic adj1 medication) OR (antimanic adj1 drug) OR (antimanic adj1 agent) OR ((anti-manic adj1 medicine) OR (anti-manic adj1 medication) OR (anti-manic adj1 drug) OR (anti-manic adj1 agent)) OR ((antiepileptic adj1 medicine) OR (antiepileptic adj1 medication) OR (antiepileptic adj1 drug) OR (antiepileptic adj1 agent) OR (anti-epileptic adj1 medicine) OR (anti-epileptic adj1 medication) OR (anti-epileptic adj1 drug) OR (anti-epileptic adj1 agent) OR (ADHD adj1 medication) OR (ADHD adj1 medicine) OR (selective adj1 serotonin adj1 reuptake adj1 inhibitor) OR (serotonin adj2 norepinephrine adj1 reuptake adj1 inhibitor) OR (serotonin adj2 noradrenaline adj1 reuptake adj1 inhibitor) OR (ethyl adj1 loflazepate) OR (lavandulae adj1 aetheroleum) OR (amino adj1 valeric adj1 acid) OR (valerianae adj1 radix).3 discontin or deprescrib* or de-prescrib* OR deprescrip* OR polypharmacy OR taper* OR (medication adj5 withdraw) OR (medicine adj5 withdraw) OR (drug adj5 withdraw) OR (medicine adj5 discontin) OR (medication adj5 discontin) OR (drug* adj5 discontin) OR (medicine adj5 reduc) OR (medication adj5 reduc) OR (dose* adj5 reduc) OR (inappropriate adj2 prescription) OR (inappropriate adj2 prescribing) OR (medicine* adj5 decreas) OR (medication adj5 decreas) OR (dose* adj5 decreas)4 1 AND 2 AND 3Limits: Human Studies only References were imported into an EndNote library, removing duplicates using both the software function and a manual check. Forwards and backwards reference searching of included papers was also conducted to track citations after the initial search and again after the updated search. Four key researchers, identified as having published several studies in this field over the last 10 years, were contacted to identify any further studies. Trial registries were not searched. ## Data selection Following the removal of duplicates, the titles and abstracts of the remaining records were reviewed by the primary reviewer (DA) against the eligibility criteria. A second reviewer (CS) undertook an independent screening of a random sample of $20\%$ of abstract/title records. Following this, the remaining papers were subjected to full text screening by DA with another random sample of $20\%$ of full texts screened by CS. Near perfect agreement was achieved for title /abstract screening ($k = 0.86$) and for full text screening ($k = 0.81$). Disagreements were resolved by discussion together with a third arbiter (PL). Automation tools were not used. ## Data extraction A bespoke data extraction form, consisting of six main categories with sub-categories, was developed to extract relevant data. The primary reviewer (DA) extracted data from all included studies and the second reviewer (CS) conducted independent data extraction for a random selection of $20\%$ of studies. No formal agreement statistics were calculated but a high level of agreement was achieved. ## Quality appraisal Following data extraction, studies were individually appraised for risk of bias by DA using the appropriate tool from the Critical Appraisals Skills Programme Tools [16] each of which consist of ten questions to assess internal and external validity. Case reports and case series were quality appraised by using tool developed by Murad et al. [ 17]. The second independent reviewer (CS) quality appraised a random sample of $20\%$ of the included studies, full agreement was reached. ## Synthesis methods Due to the heterogeneity of research design and the variability in participants, interventions and settings of included studies, a narrative approach was selected to synthesise the data, summarising the current evidence base in relation to the review question. Grouping the studies according to study design, the narrative synthesis focused on patterns in the direction and size of the effects of the deprescribing interventions and exploring relationships within and between studies and identifying factors that may help us to understand differences in reported findings [18]. ## Results We identified 8675 records, and 57 reports relating to 54 studies met our eligibility criteria and were included in the review. This is reported in the PRISMA flow diagram (Fig. 1).Fig. 1PRISMA 2020 flow diagram for the deprescribing of psychotropic medicines in people with intellectual disabilities prescribed for behaviour that challenges: a systematic review [19] Studies excluded at full text review together with reasons were recorded and listed in Table 3. Summary tables of extracted data for included studies are reported in Tables 4, 5, 6, and 7. A summary table of quality appraisal for included studies is reported in Table 8.Table 3List of excluded studies at full text screening with reasons. The following studies were excluded during screening of full text papers as they did not meet the eligibility criteria for the reasons listed belowIPPIncorrect Patient PopulationIOIncorrect OutcomeIIIncorrect InterventionFTPUFull Text Paper UnavailableEXCLUDED STUDYREASON FOR EXCLUSIONAlblowi, M. A., and F. D. Alosaimi. “ Tardive Dyskinesia Occurring in a Young Woman after Withdrawal of an Atypical Antipsychotic Drug.” Neurosciences 20.4 [2015]: 376–79.IPPBaglio, Christopher. “ Evidence and Impact of Expectancies Associated with Psychotropic Medication Reductions in Persons with Mental Retardation.” ED.D.Dissertations. 15. [ 2010].IOBranford, D. “Antipsychotic Drugs in Learning Disabilities (Mental Handicap).” Pharmaceutical Journal 258.6936 [1997]: 451–56.IIBriggs, R. “Monitoring and Evaluating Psychotropic Drug Use for Persons with Mental Retardation: A Follow-up Report.” American Journal of Mental Retardation 93.6 [1989]: 633–9.FTPUCampbell, M., et al. “ Tardive and Withdrawal Dyskinesia in Autistic Children: A Prospective Study.” Psychopharmacology Bulletin 24.2 [1988]: 251–55.FTPUCampbell, M., et al. “ Neuroleptic-Related Dyskinesias in Autistic Children: A Prospective, Longitudinal Study.” Journal of the American Academy of Child & Adolescent Psychiatry 36.6 [1997]: 835–43.IPPConnor, D. F., S. Benjamin, and K. R. Ozbayrak. “ Case-Study - Neuroleptic Withdrawal Dyskinesia Exacerbated by Ongoing Stimulant Treatment.” Journal of the American Academy of Child and Adolescent Psychiatry 34.11 [1995]: 1490–94.IPPConnor, D. F., and T. J. McLaughlin. “ A Naturalistic Study of Medication Reduction in a Residential Treatment Setting.” Journal of Child & Adolescent Psychopharmacology 15.2 [2005]: 302–10.IPPDavies, S. J., et al. “ Discontinuation of Thioridazine in Patients with Learning Disabilities: Balancing Cardiovascular Toxicity with Adverse Consequences of Changing Drugs.” BMJ 324.7352 [2002]: 1519–21.IPPDeb, S., and W. Fraser. “ The Use of Psychotropic Medication in People with Learning Disability: Towards Rational Prescribing.” Human Psychopharmacology 9.4 [1994]: 259–72.IIDeb, S., G. Unwin, and T. Deb. “ Characteristics and the Trajectory of Psychotropic Medication Use in General and Antipsychotics in Particular among Adults with an Intellectual Disability Who Exhibit Aggressive Behaviour.” Journal of Intellectual Disability Research 59.1 [2015]: 11–25.IIGranas, A. G., et al. “ Interdisciplinary Medication Review to Improve Pharmacotherapy for Patients with Intellectual Disabilities.” International Journal of Clinical Pharmacy 41.6 [2019]: 1516–25.IIHancock, Robert D., et al. “ Changes in Psychotropic Drug Use in Long-Term Residents of an Icf/Mr Facility.” American Journal on Mental Retardation 96.2 [1991]: 137–41.IIMalone, R. P., et al. “ Repeated Episodes of Neuroleptic-Related Dyskinesias in Autistic Children.” Psychopharmacology Bulletin 27.2 [1991]: 113–7.FTPUMalone, R. P., et al. “ Risperidone Treatment in Children and Adolescents with Autism: Short- and Long-Term Safety and Effectiveness.” Journal of the American Academy of Child & Adolescent Psychiatry 41.2 [2002]: 140–7.IOOkorie, E., and C. Connaughton. “ Antipsychotic Prescribing in a Residential Facility for Clients with Learning Disabilty.” British Journal of Developmental Disabilities 57.2 [2011]: 11722.IIPerez, C. A., S. S. Garcia, and R. D. Yu. “ Extrapyramidal Symptoms as a Result of Risperidone Discontinuation During Combination Therapy with Methylphenidate in a Pediatric Patient.” Journal of Child and Adolescent Psychopharmacology 26.2 [2016]: 182.IPPSilva, R. R., et al. “ Haloperidol Withdrawal and Weight Changes in Autistic Children.” Psychopharmacology Bulletin 29.2 [1993]: 287–91.FPTUSovner, R. “Thioridazine Withdrawal-Induced Behavioral Deterioration Treated with Clonidine: Two Case Reports.” Mental Retardation 33.4 [1995]: 221–5IPPTiihonen, Jari. “ Fatal Agranulocytosis 4 Years after Discontinuation of Clozapine.” The American Journal of Psychiatry 163.1 [2006]: 161.IIWrein, D. “Understanding the Role of Care Staff in Supporting Individuals with an Intellectual Disability Who Take Psychotropic Medication.” Prof Doc Thesis University of East London School of Psychology [2019].IOTable 4Summary of included randomised controlled trialsAuthor/Year, CountryStudy DesignParticipants (n, age, gender, ethnicity, level of intellectual disabilities)SettingIntervention (including medication that was targeted, Duration of deprescribing intervention and length of follow up)Outcome MeasuresSummary of FindingsResearch Units on Pediatric Psychopharmacology Autism Network.2005USA [20]Randomised Controlled Trial (RCT)n: 38mean age: 9 years old (5 to 17)gender: $87\%$ maleethnicity: not reportedID = mild 8 ($21\%$) moderate 6 ($16\%$) severe 7 ($18\%$) profound 6 ($16\%$) plus 4 incomplete data and remainder borderline or above average IQCommunityIntervention: Part of a two-stage study. Maintenance dose reduced by $25\%$ per week in the experimental group. Control group continued risperidone. Medication: risperidoneDuration: reduced over 3 weeksLength of follow up: 5 weeks after planned discontinuationABCCGI$84\%$ completed discontinuationAfter 32 participants completed the study, trial was stopped. Relapse rates were $62.5\%$ for gradual placebo substitution and $12.5\%$ for continued risperidone; difference was found to be statistically significant. Ahmed et al. 2000U.K. [21]RCTn = 56(Includes participants also reported in Smith et al., 2002 [22])mean age = 43(20 to 78)gender: $48\%$ maleethnicity: not reportedID = incomplete data$45\%$ NHS hospitals$9\%$ NHS Community unit $46\%$Community residential homesIntervention: Thirty-six participants randomly allocated to the experimental group underwent four, monthly $25\%$ drug reduction stages. There were no planned drug changes for the control groupMedication: Participants received 12 different antipsychotic drugs, most frequently thioridazine (18 people, $12\%$), haloperidol (13, $23\%$) and chlorpromazine (8, $14\%$).Duration: Reduced over 4 monthsLength of follow up: 1 month after planned discontinuationABC, DISCUS, weighing scales, direct observation using palm-top Psion 3a portable computers, Number of participants Successfully deprescribed12 participants ($33\%$) completed full withdrawal 7 participants ($19\%$) achieved and maintained at least a $50\%$ reduction. Drug reduction was associated with increased dyskinesia and higher activity engagement but not increased maladaptive behaviour. Some setting characteristics were associated with drug reinstatement.de Kuijper et al. 2014 [23]The NetherlandsRCTn: 98(Includes participants also reported in de Kuijper et al. ,2013 [24], de Kuijper et al. 2014 [25] and de Kuijper et al. 2018 [26])mean age: 49.8 (15 to 66)gender: $64\%$ maleethnicity: not reportedID:profound 35 ($36\%$)severe 26 ($27\%$)moderate 30 ($31\%$)mild 7 ($7.1\%$)CommunityIntervention: Participants underwent $12.5\%$ antipsychotic dose reduction every 2 or 4 weeksMedication: 65 pipamperone, 18 haloperidol, 15 risperidone, 8 olanzapine, 7 levomepromazine 1 pimozideDuration: Reduced over 14 or 28 weeksLength of follow up: 12 weeks after planned discontinuationPrimary outcome: ABC (irritability subscale)Secondary outcomes: other ABC subscales, CGI, SCOPA-AUT, Epworth Sleepiness Scale, AIMS, Barnes, Unified Parkinson’s Disease Rating ScalePhysical Health parameters- weight, BP, lipids, waist circumference, pulse, prolactin, testosterone, Number of participants Successfully deprescribedOf 98 participants, 43 achieved complete discontinuation; at follow-up 7 had resumed use of antipsychotics. Mean ABC ratings improved significantly for those who achieved complete discontinuation and at follow-up for those who had not achieved complete discontinuation. Similar results with respect to most ABC sub-scales, including the ‘irritability’ subscale. No significant differences in improvement of ABC ratings between both discontinuation schedules. Higher ratings of extrapyramidal and autonomic symptoms at baseline associated with less improvement of behavioural symptoms after discontinuation; higher baseline ABC rating predicted higher odds of incomplete discontinuation.de Kuijper et al. 2013 [24]The NetherlandsRCTadditional reporting of [23](Includes participants also reported in de Kuijper et al., 2014 [23, 24])’Fasting glucose, triglycerides, high density lipoproteins, low-density lipoproteins, and total cholesterol in blood; height, weight, and waist circumference and systolic and diastolic blood pressureDiscontinuation of anti-psychotics led to a significant decrease in waist circumference, weight, BMI, and systolic blood pressure. Higher baseline dosage associated with a larger decrease in waist circumference, weight, and BMI in these participants. No significant difference between discontinuation in 14 or 28 weeks. Dosage reductions associated with decrease in weight and BMI, negatively associated with metabolic outcomes such as fasting plasma glucose levels.de Kuijper et al. 2014The Netherlands [27]RCTadditional reporting of [23](Includes participants also reported in de Kuijper et al. ,2013 [24] and de Kuijper et al.,, 2014 [23]),Plasma measurements included prolactin, testosterone (only in male participants), 25-OH vitamin D, PTH, and bone turnover markers, ie, bone alkaline phosphatase (BALP), aminopropeptide type I collagen (PINP), and C-telopeptide type I collagen (CTX).Both complete discontinuation and dosage reduction led to decrease in prolactin plasma levels and to increase in levels of CTX, the bone resorption marker. Dose reductions associated with a significant decrease in 25-OH vitamin D levels, with less weight loss and higher BMI compared with those who had completely discontinued. More weight loss associated with less difference in baseline/follow-up CTX levels and with less difference in baseline/follow-up 25-OH vitamin D levels. Hassler et al. 2007Germany [28]RCTn: 39(Includes 31 participants also reported in de Hassler et al., 2011 [29])mean age: 36.4 (SD 10.4)gender: $54\%$ maleethnicity: $100\%$ whiteID: severe 28 ($72\%$)moderate 9 ($23\%$)mild 2 ($5\%$)InpatientIntervention: Random allocation of withdrawal of medication after a 6 week period of open treatmentMedication: zuclopentixolDuration: Sudden discontinuationLength of follow up: 12 weeks after discontinuationPrimary outcome: MOASSecondary outcome: withdrawal symptoms, extrapyramidal signs, vital signs, weight, and routine laboratory tests of prolactin and serum levels of zuclopenthixol were conducted. The placebo group was associated with more aggressive behaviour as indicated by outcomes observed by external raters. Hassler et al. 2011Germany [29]RCTadditional reporting of [28]n: 31(The participants were also reported in Hassler et al., 2007 [28])mean age: 38.4 (adults)gender: $55\%$ maleethnicity: $100\%$ whiteID = not reportedInpatientIntervention: Prospective follow up of an RCT in which participants who remained on medication were compared to those participants who discontinued during a 2 year periodMedication: zuclopentixolDuration: Not reportedLength of follow up: VariableMOASDASCGI-IBody weightPatients still treated with zuclopentixol after 2 years ($$n = 21$$) benefitted, compared to the patients who discontinued ($$n = 10$$)For continually treated patients, no adverse events, side-effects, or treated extrapyramidal symptoms were reported. They lost on average 1.8 kg body weight. Patients who discontinued zuclopentixol on average gained 2.6 kg body weight. Heistad et al. 1982USA [30]RCTn: 100mean age: 28.5 (13 to 65)ethnicity: not reportedgender: $54\%$ maleID: > $50\%$ profoundInpatientIntervention: 5 separate arms to assess effect of withdrawal. Details not reported. Patients selected were rank ordered by current drug dose, and one member of each successive pair was randomly assigned to either the drug-placebo or the placebo-drug sequence. Medication: thioridazineDuration: variableLength of follow up: 4 to 5 weeks after discontinuationUnvalidated adapted behaviour coding system, NOSIE, AIMSTime-sampling of behaviour showed significant increase in self-stimulation and active negative behaviour and decreased work and life skills while receiving placebo. Most patients’ behaviour was better while on active medication, some showed significant improvement when medication was temporarily discontinued. Favourable long-term progress among those who had medication restored was greater for patients whose behaviour had worsened to the greatest degree during the placebo (discontinuation) trial. McNamara et al. 2017U.K. [31]RCTn = 22mean age: 43 (21 to 68)gender: $68\%$ maleethnicity: not reportedID = not reportedCommunityIntervention: Treatment in the intervention group was gradually reduced over a 6-month period and then maintained at the same level for a further 3 months. In the control group, baseline level of medication was maintained throughout the 9-month period. Medication: risperidoneDuration: Reduced over 6 monthsLength of follow up: 6 and 9 months after planned discontinuationFeasibility outcomes: the Number and proportion of general practices/Community learning disability teams that progressed from initial approach to recruitment of participants and the Number and proportion of recruited participants who progressed through the various stages of the study. Clinical outcomes:MOAS, ABC, PAS-ADD, The ASC, DISCUS, the CSRI, use of other interventions to manage challenging behaviour, use of PRN medication and level of psychotropic medication use. Of the 22 participants randomised (intervention, $$n = 11$$; control, $$n = 11$$), 13 ($59\%$) achieved progression through all four stages of reduction. Follow-up data at 6 and 9 months were obtained for 17 participants (intervention, $$n = 10$$; and control, $$n = 7$$; $77\%$ of those randomised).No clinically important changes in participants’ levels of aggression or challenging behaviour reported. Ramerman et al. 2019The Netherlands [32]RCTn:25(11 participants also reported in Kuijper et al., 2018 [33] and Ramerman et al. ,2019 [34])mean age: 30gender: $76\%$ maleethnicity: not reportedID:mild $52\%$ moderate $24\%$ severe $24\%$profound $0\%$InpatientIntervention: In the discontinuation group, Risperidone was gradually replaced by a placebo over 14 weeks, while the control group maintained their existing dosage. Medication: risperidoneDuration: Reduced over 14 weeksLength of follow up: 8 weeks after planned discontinuationABCCGS-I, SCOPA-AUT, Epworth Sleepiness Scale, AIMS, Barnes, Unified Parkinson’s Disease Rating ScalePhysical Health parameters- weight, BP, lipids, waist circumference, pulse, Prolactin, testosterone, Number of participants Successfully deprescribedIn the discontinuation group, $82\%$ completely withdrew from risperidone. No significant change in irritability, compared with the continuation group, although there was Groupa Time effects on stereotypical behaviour in favour of the continuation group. Significant GroupaTime effects were also found for weight, waist, body mass index, prolactin. Levels and testosterone levels, with beneficial effects for the discontinuation group.2 participants had severe dyskinesiaSmith et al. 2002U.K. [22]RCTadditional reporting of [21](Participants also reported in Ahmed et al., 2000 [21])ABS, ABC DISCUSdirect observationHigh Yule’s Q-value results pre- and post-baseline were found, indicating that clients were highly responsive to staff interaction. Yule’s Q-value did not significantly increase following drug withdrawal. Key: AIMS Abnormal Involuntary Movement Scale, ABC Aberrant Behavior Checklist, ABS Agitated Behaviour Scale, BARNES Barnes Akathisia Rating Scale, BFCRS Bush-Francis Catatonia Rating Scale, BP Blood Pressure, CARS Childhood Autism Rating Scale CGAS: Children’s Global Assessment Scale (CGAS) CGI: Clinical Global Impressions, CSM Committee on Safety of Medicines, CPRS Comprehensive Psychopathological Rating Scale, DAS Disability Assessment Schedule, DISCUS Dyskinesia Identification System Condensed User Scale, DISCO Dyskinesia Identification System-Coldwater, ECG Electrocardiogram, FBC Full Blood Count, HbA1c Glycated Haemoglobin, Kiddie SAD-PL Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetyime Version, LFTs Liver Function Tests, MOAS Modified Overt Aggression Scale, NOSIE Nurses’ Observation Scale for Inpatient Evaluation, PAS-ADD Psychiatric Assessment Schedule for Adult with Developmental Disability, PBS Positive Behaviour Support, PTH Parathyroid Hormone, RAND-36 measure of health related quality of life, U + Es Urea and elelctrolytes, UPDRS Unified Parkinson’s Disease Rating Scale, SCOPA-AUT Scales for Outcomes in Parkinson’s Disease - Autonomic DysfunctionTable 5Summary of included non-randomised controlled trialsAuthor/Year, CountryStudy DesignParticipants (n, age, gender, ethnicity, level of intellectual disabilities)SettingIntervention (including medication that was targeted, Duration of deprescribing intervention and length of follow up)Outcome MeasuresSummary of FindingsAman et al. 1985 New Zealand [35]Prospective non randomised controlled designn: 24 mean age: Not reported gender: $71\%$ male ethnicity: not reported ID = severe / profound $100\%$InpatientIntervention: Participants received their full antipsychotic dosage during the first week, a half dosage during the second week, and no medication thereafter for 4 weeks. The control group were not taking psychotropic medicines prior to the studyMedication: trifluoperazine, thioridazine, periciazine, haloperidolDuration: Over one weekLength of follow up: 8 weeks after discontinuationDISCOThe antipsychotic medication group was rated as having higher total dyskinesia scores. However, examination of three Group by Time interactions indicated that the symptom scores rose more rapidly for the control group. Carpenter et al. 1990 USA [36]Prospective non randomised controlled designn: 10 mean age: 30 (18 to 53) gender: $90\%$ male ethnicity: $70\%$ black$30\%$ white ID = borderline $10\%$ mild $50\%$ moderate $10\%$ severe $30\%$InpatientIntervention: Reduction of antipsychotic medication by 25–$100\%$,; no medication changes in control groupMedication: chlorpromazine, Thioridazine, haloperidolDuration: Not reportedLength of follow up: Not reportedPerformance on a discrimination task requiring matching of colours presented sequentially on a computer screenGroup that had their medicines deprescribed achieved better scores than control group. Gerrard et al. 2019U.K. [37]Prospective non randomised controlled designn: 54 (may include participants also reported in Gerrard 2020 [38]) mean age: Not reported gender: $50\%$ male ethnicity: not reported ID = mild 16 ($30\%$) moderate 16 ($30\%$) severe 20 ($37\%$) profound 2 ($3\%$)CommunityIntervention:The experimental group were considered for deprescribing with input from specialist PBS team, while the control group underwent unsupported medication challenge. Medication: amisulpride $5\%$, aripiprazole $29\%$, olanzapine $5\%$, quetiapine $9\%$, risperidone $52\%$Duration: VariableLength of follow up: VariableNumber of patients who: agreed to initiation of a reduction schedule agreed to subsequent reductions had medication reviews discontinued medication Number of patients restarted on medicationNumber of patients achieving 25, $50\%$ or $75\%$ reductionNumber of patients who had medication increasedThere was a significantly higher success rate for medication reduction and discontinuation when PBS assessment and intervention was provided. At each stage of the process, initiating a reduction schedule, there is a difference between the two groups, pointing to greater success with the support of PBS.Complete discontinuation: $60\%$ in PBS group $15\%$ in non PBS groupAt least $50\%$ reduction: $20\%$ in PBS group, $7\%$ in non PBS groupReprescribing or dose increases: 1 person in PBS group $66\%$ In non PBS groupSwanson et al. 1996 USA [39]Prospective non randomised controlled designn:80 mean age: 38 gender: $61\%$ male ethnicity: not reportedID =Moderate: $1\%$Severe: $16\%$Profound: $80\%$Unknown: $3\%$InpatientIntervention: antipsychotic dose reduced by $10\%$ every 3 months until discontinuedMedication: risperidoneDuration: VariableLength of follow up:6 months postdiscontinuationDISCUSABCTransient increase in average DISCUS score in antipsychotic only group after withdrawal with return to baseline 6 months after discontinuation. In group antipsychotic plus anticonvulsant no change in scores reported. Transient increase in average ABC during antipsychotic withdrawal in those also prescribed anticonvulsants. Wigal et al. 1993 USA [40]Prospective non randomised controlled designn: 56 (may include participants also reported in Wigal et al., 1994 [41]) mean age: 33 gender: $64\%$ male ethnicity: not reported ID = severe/profound $96\%$InpatientIntervention: Medication review and dose reduction programme Four groups compared increase in antipsychotic dose (IN; $$n = 5$$), no change in antipsychotic dose (NC; $$n = 14$$), reduction in antipsychotic dose of < $25\%$ (SD; $$n = 21$$), and reduction in antipsychotic dose of ≥$25\%$ (25D, $$n = 16$$)Medication: antipsychoticDuration: VariableLength of follow up:10 monthsDISCUSProportion of participants with dyskinesiaNumber of participants discontinuing and decreasing dosageNo difference in DISCUS score between groups at baseline;DISCUS score at follow-up was increased in NC, SD, and 25D groups—greatest increase observed in 25D group;DISCUS score at follow- up decreased in the IN group; significant correlation observed between degree of dose reduction and DISCUS score ($r = 0$·51; $p \leq 0$·001); proportion with dyskinesia increased from $30\%$ at baseline to $60\%$ at follow-up in the 25D group, did not change in the SD or NC groups, and fell from 60 to $20\%$ in the IN group. Wigal et al. 1994 USA [41]Prospective non randomised controlled designn:43 (may include participants also reported in Wigal et al., 1993 [40]) mean age: 24 gender:$67\%$ male ethnicity: not reportedID = Severe/Profound: $86\%$InpatientIntervention: Medication review and dose reduction programmeMedication: antipsychoticsDuration: VariableLength of follow up:10 monthsRates of dyskinesia$63\%$ of discontinuation group and $29\%$ of dose reduction group developed dyskinesia. No dyskinesia reported in participants in no change, increase or unmedicated group. Zuddas et al. 2000 Italy [42]Prospective non randomised controlled designn:10 mean age: 12.3 (7 to 17) gender: $70\%$ male ethnicity: not reported ID = mild 2 Moderate 5Severe 3CommunityIntervention: Following open label treatment with risperidone, three patients discontinuedMedication: risperidoneDuration: Medication tapered over 3 to 4 weeksLength of follow up: 5 monthsCARSCPRSCGIC-GASKiddie SAD-PLIntellectual functioning was measured using the Raven progressive matrices. Physical and neurological examinations, vital signs and measurement of body weight were carried out for all patients at baseline, weekly for the first month and monthly thereafter.3 participants discontinued risperidone, 6 months after withdrawal, atypical antipsychotic was represcribed. ( risperidone × 2, olanzapine × 1)Patients who discontinued risperidone showed progressive behaviour deterioration. Key: AIMS Abnormal Involuntary Movement Scale, ABC Aberrant Behavior Checklist, ABS Agitated Behaviour Scale, BARNES Barnes Akathisia Rating Scale, BFCRS Bush-Francis Catatonia Rating Scale, BP Blood Pressure, CARS Childhood Autism Rating Scale, CGAS Children’s Global Assessment Scale (CGAS), CGI Clinical Global Impressions, CSM Committee on Safety of Medicines, CPRS Comprehensive Psychopathological Rating Scale, DAS Disability Assessment Schedule, DISCUS Dyskinesia Identification System Condensed User Scale, DISCO Dyskinesia Identification System-Coldwater, ECG Electrocardiogram, FBC Full Blood Count, HbA1c Glycated Haemoglobin, Kiddie SAD-PL Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetyime Version LFTs: Liver Function Tests, MOAS Modified Overt Aggression Scale, NOSIE Nurses’ Observation Scale for Inpatient Evaluation, PAS-ADD Psychiatric Assessment Schedule for Adult with Developmental Disability, PBS Positive Behaviour Support, PTH Parathyroid Hormone, RAND-36 measure of health related quality of life, U + Es Urea and elelctrolytes, UPDRS Unified Parkinson’s Disease Rating Scale, SCOPA-AUT Scales for Outcomes in Parkinson’s Disease - Autonomic DysfunctionTable 6Summary table of included pre post studiesAuthor/Year, CountryStudy DesignParticipants (n, age, gender, ethnicity, level of intellectual disabilities)SettingIntervention (including medication that was targeted, Duration of deprescribing intervention and length of follow up)Outcome MeasuresSummary of FindingsBrahm et al. 2003 USA [43]Prospective Pre post design (no control)n: 18mean age: 42.7 (27 to 57) gender: $8\%$ male ethnicity: not reported ID = moderate/severe/profoun d: $100\%$Not reportedIntervention: Following warning of QTC with Thioridazine, 18 patients reviewed, antipsychotic medication reduced and QTc prolongation assessed Medication: Thioridazine, mesoridazine Duration: VariableLength of follow up: 8 weeks post discontinuationECG15 participants discontinued thioridazine, increases in QTc prolongation times in five male patients after discontinuation of thioridazine, three patients slight increases and two patients more marked increases. Branford D 1996 U.K. [44]Retrospective Pre post design (no control)n: 198 mean age: 43 (18 to 82) gender: $66\%$ male ethnicity: not reported ID = borderline $1\%$ mild $13\%$ moderate: $30\%$ severe: $56\%$$47\%$ Inpatient$53\%$ CommunityIntervention: Medication review and dosage reduction programme Medication: thioridazine,chlorpromazine, zuclopentixol, haloperidol Duration: Mostly over 3 monthsLength of follow up: 12 monthsNumber reducing or discontinuing antipsychotic medication; challenging behaviour reports123 patients underwent a reduction of antipsychotics. $16\%$ of the total cohort of 198 were withdrawn from antipsychotics, $28\%$ maintained on reduced dosage of antipsychotics. Out of the 123 undergoing reduction, 31 ($25\%$) of 123 discontinued antipsychotic, 56 ($46\%$) of 123 reduced dose, 27 ($22\%$) of 123 same dose, and 9 ($7\%$) of 123 increased dose; 31 ($25\%$) of 123 no deterioration, 52 ($42\%$) of 123 deterioration in behaviour, and 40 ($33\%$) of 123 not reportedde Kuijper et al. 2018 The Netherlands [33]Prospective Pre post design (no control)n: 129 (includes an unspecified number of participants also reported in de Kuijper et al., 2014 [23] and Ramerman et al., 2019 [32])mean age: 49 (11.5–84.2) gender: $67\%$ male ethnicity: not reported ID = mild $13\%$ moderate $24\%$ severe $44\%$ profound $16\%$ unspecified $3\%$CommunityIntervention: antipsychotic reduced over 14 weeksMedication: Not reported (see study [45])Duration:14 weeksLength of follow up: 6 months following planned discontinuationPrimary outcome measure: Complete discontinuation at 16 weeksSecondary outcome measures: Complete discontinuation at 28 and 40 weeks, ABC, CGI-I, CGI-S$61\%$ had completely discontinued antipsychotics at 16 weeks, $46\%$ at 28 weeks, and $40\%$ at 40 weeks. CGI-I: at 16 weeks $6\%$ of participants had shown improvement and $9\%$ worsening in behaviour; at 28 weeks, these percentages were 9 and $15\%$, and at 40 weeks 21 and $7\%$, respectively. At 28 weeks those who had not achieved complete discontinuation had significantly more often worsening in behaviour according to the CGI-I than those who had successfully discontinued.de Kuijper et al. 2018 The Netherlands [45]Prospective Pre post design (no control) additional reporting of [33](includes an unspecified number of participants also reported in de Kuijper et al., 2014 [23] and Ramerman et al., 2019 [32])Primary outcome measure: Complete discontinuation at 16 weeksSecondary outcome measures: Completediscontinuation at 28 and 40 weeks, ABC, Barnes, AIMS Number of times participants experienced new health problems Number of consultations by participants with their physician Number of new medication prescriptions or dosage changes Number of new nonpharmaceutical treatments. Number of changes in living circumstances and life events$61\%$ had completely discontinued antipsychotics at 16 weeks, $46\%$ at 28 weeks, and $40\%$ at 40 weeks. ABC total scores increased in $49\%$ of participants with unsuccessful discontinuation at 16 weeksParticipants who achieved complete discontinuation had less-severe parkinsonism and lower incidence of health worsening during the study period compared with participants with incomplete discontinuation. A lower incidence of complete discontinuation was associated with higher ABC score, higher akathisia score and more frequent worsening of health. Ellenor et al. 1977 USA [46]Retrospective Pre post design (no control)n: 208 mean age: not reported gender: not reported ethnicity: not reported ID = mild/moderate $20\%$Severe/profound $80\%$InpatientIntervention: Pharmacist involvement in a behavioural review committee with aim of deprescribing psychotropic medicines over a two year programmeMedication: anti anxiety/ antidepressants, antipsychotics, sedative/hypnotics, miscellaneous medication for behaviour managementDuration: VariableLength of follow up: VariableABS (adaptive behaviour scales) Number of prescriptions and changes in dosages of medicinesABS scores reported for 54 participants revealed a slight increase in adverse behaviours for all three groups; medication reduced, medication stopped and control group who had not been assessed by the behaviour review committee. Through discontinuance of medicationa $50\%$ reduction’ in the use of antianxiety-antidepressant agents, $17.5\%$ reduction in antipsychotic agents, $57.6\%$ reduction in sedativehypnotics and a $64.7\%$ reduction in miscellaneous agents was reported. Of the total 183 drugs discontinued, 153 of these, or $83\%$, were discontinued without being replaced with a pharmacologically equivalent agent. In addition, of the 313 medications being administered to patients for behavior control at the completion of the two years, 124 of these, or $39.6\%$, were being administered at lower dosages Of the 313 drugs administered at the end of the study period, 87 medications were being administered at higher dosages or had been added to the patient’s drug regimen. Thus, while $39.6\%$ were receiving lower dosages, $28\%$ received. Higher dosages. The remaining $33\%$ received the same dosage. Ferguson et al. 1982 USA [47]Prospective Pre post design (no control)n: 250 mean age: not reported (adolescents and adults) gender: not reported ethnicity: not reportedID = not reportedInpatientIntervention: Introduction of interdisciplinary teams medication reviews with a goal to deprescribe antipsychotic medication typically by 25–$50\%$ per 30 day periodMedication: antipsychoticsDuration: VariableLength of follow up: Not reportedNumber of individuals receiving neuroleptic drugs, mean daily drug dose, Number of individuals receiving dosage increases or decreases, number of individuals able to be maintained on lowered dosages or no drug at allData-based reviews resulted in decreased numbers of individuals receiving antipsychotic drugs, lower mean daily dosages, and less frequent dosage increases. $97\%$ of the individuals receiving drug discontinuation or dosage decreases were not placed back on a drug or did not receive dosage increasesFielding et al. 1980 USA [48]Retrospective Pre post design (no control)n: 192 mean age: 35 (SD 14.5) gender: $52\%$ male ethnicity: not reportedID = severe/profound$86\%$InpatientIntervention: Two phasesPhase one: subjects participated in a 50-day assessment period consisting of 20 days during which they received their normal psychotropic medication followed by 30 days during which they received no medication. Medication was not tapered. At the end of the 30 days of non- medication, prescriptions were discontinued for those who did not show an increase in challenging behaviours. The 50-day assessment was repeated for individuals who remained on psychotropic medication. Phase two: 92 subjects who were unable to discontinue psychotropic were exposed to 30 days of $25\%$ dose reduction which was repeated depending on adverse behaviours. Doses were also increased if necessaryMedication: The most commonly prescribed medications were Mellaril and Thorazine. Other drugs used less often included Haldol, Trilafon, Quide, Navane, and Prolixin. Duration: 30 days for phase 1, Variable for phase 2Length of follow up: nearly 2 yearsDaily number of incidents of adverse behaviours Number of participants who discontinued or changed dose of psychotropic medicines$60\%$ of participants who had been taking medications no longer needed them as no increase in frequency of episodes of behaviours that challenge. All but eight of the 68 residents whose medication gradually was reduced under phase two have achieved permanent dosage reductions while maintaining rates of maladaptive behavior comparable to those observed while medicated. While maladaptive behaviors increased slightly for some, they decreased or remained stable for the majority. Findholt et al. 1990 USA [49]Retrospective Pre post design (no control)n: 208 mean age: not reported gender: not reported ethnicity: not reported ID = severe / profound: majorityInpatientIntervention: Behaviour and Medication review committee reviewed medication of participants at least every 6 monthsMedication: antipsychotics, antidepressants, anxiolyticsDuration: VariableLength of follow up: VariableNumber of patients taking antidepressants, anxiolytics and antipsychotics and Number of patients receiving polypharmacy (defined in study as 2 or more psychotropic medicines)Cost savings based on medicine pricesApril 1979 out of a total population of 590 persons, 208 ($41\%$) were receiving antipsychotic medications, 69 ($14\%$) were on antidepressants, and 67 ($13\%$) were taking anxiolytics, with 52 residents on polypharmacy. March of 1987, with a total population of 436, these Numbers decreased to 52 ($12\%$) on antipsychotics, 9 ($2\%$) on antidepressants, 11 ($3\%$) on anxiolytics, and 3 receiving polypharmacy. Cost savings for four most prescribed medicines $119.77 per day. Gerrard 2020 U.K. [38]Retrospective Pre post design (no control)n: 66 (includes an unspecified number of participants also reported in Gerrard et al., 2019 [37]) mean age: not reported gender: $50\%$ male ethnicity: not reportedID = not reportedCommunityIntervention: Pharmacist and PBS nurse reviewed patients with view to deprescribing in conjunction with views of patient, carers and familiesMedication: Included risperidone, olanzapine, quetiapine, amisulpride, aripiprazole,benzodiazepinesDuration: VariableLength of follow up: VariableNumber of medicines stopped, Number of medicines restarted. For antipsychotic prescriptions- FBC, U + Es, LFT, TFT, Lipids, Glucose/HbA1c, prolactin, BP, weight, pulse and ECG24 psychotropic medications were stopped; 20 of these were with PBS support. A further 22 people were undergoing the challenge which was not complete at end of study. Ten medications needed to be restarted post-discontinuation or increased post-reduction, with eight being in the unsupported clinic. On average, each person required a minimum of five reviews to fully undertake the challenge. The majority of medications stopped were antipsychotics,. Over half these prescriptions were for risperidone, which reflects the clinical practice that this antipsychotic was the preferred choice in behavioural intervention. Side effect burden reduced by $71\%$ with a reduction of $50\%$ of the starting dose or more. The main issues that improved were sedation, weight gain and postural hypotension. Howerton et al. 2002 USA [50]Prospective Pre post design (no control)n: 159 mean age: not reported gender: $65\%$ male ethnicity: not reported ID = mild 57 moderate 31 severe 39profound 21borderline 5none 6CommunityIntervention: Evaluation of an interdisciplinary review team addressing polypharmacyMedication: Typical and atypical antipsychotics, anticonvulsants, SSRIs, antidepressants, lithiumDuration: VariableLength of follow up: 3 monthsMedicines stopped and startedDecrease in polypharmacy, discontinuation of unnecessary anticonvulsants. Thioridazine use was reduced by $63\%$, haloperidol by $72\%$, and chlorpromazine by $100\%$. Lithium was discontinued in 18 patients. Inoue et al. 1982 Canada [51]Retrospective Pre post design (no control)n: 251 mean age: not reported gender: not reported ethnicity: not reported ID = borderline $2.5\%$ mild $13.1\%$ moderate: $33\%$ severe:$31.3\%$ profound: $16.2\%$ unspecified $3.6\%$InpatientIntervention: Implementation of a pharmacy patient review service to address overprescribing of psychotropic medicines over 5 years. Medication: antipsychotics $72\%$, anxiolytics $16\%$, sedative/hypnotics-$11\%$, antidepressants $9\%$, and others (e.g. lithium) $1\%$Duration: VariableLength of follow up: VariableNumber of psychotropic medicines discontinued Number of psychotropic medicines with dose changesBy the end of the five year period, 135 psychotropic medication orders for 121 patients were discontinued. The dosage reductions (25–$75\%$; mean $48.6\%$) were made for 91 medication orders. Janowsky et al. 2006 USA [52]Retrospective Pre post design (no control)n: 138 (may include participants also reported in Janowsky 2008 [53]) mean age: 48 (18 to 81) gender: $60\%$ male ethnicity: not reported ID = severe to profound $100\%$InpatientIntervention: Medication review and dosage reduction programme Medication: Typical and atypical antipsychoticsDuration: Not reportedLength of follow up: 10 yearsNumber discontinuing antipsychotic medication$55\%$ Successfully discontinued antipsychotic medication$36\%$ relapsed on withdrawal requiring dose increases or represcribing. Janowsky 2008 USA [53]Retrospective Pre post design (no control)n: 57(may include participants also reported in Janowsky 2006 [52]) mean age: 52 (30 to 78) gender: $65\%$ male ethnicity: not reported ID = severe to profound $100\%$InpatientIntervention: Medication review and dosage reduction programme Medication: haloperidol ($$n = 24$$), thioridazine ($$n = 20$$), chlorpromazine ($$n = 7$$), thiothixine ($$n = 5$$), and loxapine ($$n = 1$$)Duration: Not reportedLength of follow up: Up to 15 yearsNumber discontinuing antipsychotic medicationNumber of episodes of challenging behaviour4 ($8\%$) of 49 discontinued antipsychotic medication and 45 ($92\%$) of 49 could not discontinue antipsychotic medication; 2 ($4\%$) of 49 no deterioration in behaviour and 47 ($96\%$) of 49 experienced behavioural relapse. Jauernig et al. 1995 Australia [54]Retrospective Pre post design (no control)n: 25 mean age: not reported gender: not reported ethnicity: not reportedID = not reportedInpatientIntervention: Medication review and dosage reduction programme involving maximum monthly dose reduction of $25\%$Medication: thioridazine,chlorpromazine, haloperidol, fluphenazine, trifluoperazineDuration: VariableLength of follow up: 2 yearsNumber reducing or discontinuing antipsychotic medication; Number of episodes of challenging behaviour3 ($12\%$) discontinued antipsychotic medication, 19 ($76\%$) underwent dose reduction, and 3 ($12\%$) no change in dose; challenging behaviour frequency at follow-up lower than in baseline in all 3 patients ($100\%$) whose antipsychotic had been discontinued and in 15 patients ($79\%$) of 19 who underwent dose reduction. LaMendola et al. 1980 USA [55]Retrospectives Pre post design (no control)n: not reported mean age: not reported gender: not reported ethnicity: not reportedID = not reportedInpatientIntervention: Medication review and dosage reduction programme over 4 yearsMedication: includedantipsychotics and benzodiazepines Duration: not reportedLength of follow up: not reportedPercentage of patients prescribed psychotropic medicines, percentage of patients prescribed major and minor tranquilisers. Patients prescribed psychotropic medication decreased from 34 to $21\%$, percentage prescribed major tranquillisers fell from 27 to $20\%$ and minor tranquilisers were no longer used having accounted for $5\%$ of patients. Lindsay et al. 2004 USA [56]Prospective Pre post design (no control)n: 14mean age: 9.7 (5–13) gender: $93\%$ male ethnicity: not reported ID = $100\%$ borderline to moderateCommunityIntervention: After a* mean exposure of 8.9 months because of excessive weight gain, or excessive appetite, or insufficient clinical response, antipsychotic medication stoppedMedication: risperidoneDuration: sudden discontinuationLength of follow up: 24 monthsBody weightStandardised weight at 12 and 24 months after discontinuation of risperidone was not distinguishable from standardized weight before risperidone was initially prescribedLuchins et al. 2004 USA [57]Retrospective Pre post design (no control)n: 95 mean age: 32 (18 to 73) gender: $60\%$ male ethnicity: not reported ID = mild / moderate: 70 severe / profound: 25InpatientIntervention: Interdisciplinary team programme to review psychotropic medication with a view to reduce or discontinueMedication: antipsychoticsDuration: VariableLength of follow up: VariableDosage changes of antipsychotics and other psychotropic medicines. Unvalidated behaviour rating toolReduction of antipsychotics associated with improvement in behaviour.41 participants were receiving an alternative psychotropic medicine at the end of the study period, with 5 of them receiving two such drugs concurrently. The alternative drugs used were as follows: lithium ($$n = 26$$) carbamazepine ($$n = 9$$) buspirone ($$n = 9$$), and propranolol ($$n = 2$$). The prescribing of these other psychotropic medicines were associated with a reduction in the prescribing of antipsychotic medicines,Marholin et al. 1979 USA [58]Prospective Pre post design (no control)n: 6mean age: 35 (27 to 53) gender: $100\%$ male ethnicity: not reportedID = severe $100\%$InpatientIntervention: antipsychotics were withdrawn and readministered using a double-blind B-A-B (drug-placebo-drug) design. Medication: phenothiazine antipsychoticsDuration: Sudden discontinuation for 23 daysLength of follow up: 48 daysObservations on the ward and during workshop tasksFindings highly individualised and mixedWhen chlorpromazine was withdrawn and reinstated, reversible changes occurred in at least one category of behavior for all subjects. Matthews et al. 2003 U.K. [59]Retrospective Pre post design (no control)n: 77mean age: 45.5 (16 to 81) gender: $51\%$male ethnicity: not reported ID = mild $22\%$ moderate: $22\%$ severe / profound: $39\%$ unspecified: $17\%$CommunityIntervention: Retrospective case note analysis to observe effects of discontinuationMedication: thioridazineDuration: not reportedLength of follow up: Not reportedSignificant adverse events on /following Thioridazine withdrawalOver $50\%$ of those on regular thioridazine experienced adverse events during or following drug withdrawal. Adverse events were significantly associated with the duration of previous thioridazine prescription. Problems encountered included reemergence of psychosis or mood disturbance, escalation of arousal, aggression, anxiety, self-injury, sexual disinhibition, and ritualised behaviours. Further details of adverse effects not reported. Marcoux 1985 USA [60]Prospective Pre post design (no control)n: not reported mean age: not reported gender: not reported ethnicity: not reportedID = not reportedInpatientIntervention: Interdisciplinary team programme to review psychotropic medication with a view to reduce or discontinueMedication: chlorpromazine, piperidine, mesoridazine, thioridazine, piperazine,fluphenazine, perphenazine, prochlorperazine, tripfluoperazine, haloperidol, thiothixene, molindoneDuration: Not reportedLength of follow up: Not reportedantipsychotic dosagesantipsychotic dosages decreased at a projected annual rate of $17\%$ and no significant withdrawal reactions reported. This dosage decrease has saved the Institution approximately $2800 to $3200 in medication costs after a 10-month period. May et al. 1995 USA [61]Prospective Pre post design (no control)n: 23 mean age: 42 (24–62) gender: $100\%$ male ethnicity: not reported ID = severe/ profound: $100\%$InpatientIntervention: antipsychotic dose reduced by $10\%$ every 3 months until discontinuedMedication: risperidoneDuration: VariableLength of follow up:3–4 yearsNumber of incidents of challenging behaviourThree groups to describe changes in challenging behaviour:Transient worsening ($$n = 9$$; $39\%$ Progressive improvement ($$n = 5$$; $22\%$) Persistent worsening ($$n = 9$$; $39\%$).Newell et al. 2000 USA [62]Prospective Pre post design (no control)n:6(may include participants also reported in Newell et al., 2001 [63] and Newell et al., 2002 [64])mean age: 36.8 (14 to 50)gender:$67\%$ male ethnicity: not reported ID = mild moderate 4 severe 1 profound 1InpatientIntervention: antipsychotic dose reduced by $25\%$ every 3 months until discontinuedMedication: haloperidol, thioridazine, mesoridazineDuration: VariableLength of follow up: 6 months to 2 years post discontinuationVideo analysis of lip movement DISCUSDyskinetic movements increased during antipsychotic withdrawal followed by a reduction post-discontinuation. Newell et al. 2001 USA [63]sn:26(may include participants also reported in Newell et al., 2000 [62] and Newell et al., 2002 [64])mean age: 34.9 (18 to 52)gender:$69\%$ male ethnicity: not reportedID = mild 1 moderate 5 severe 12 profound 8InpatientIntervention: antipsychotic dose reduced by $25\%$ every 2 to 4 months until discontinued Medication: haloperidol, thioridazine, chlorpromazine, mesoridazine, lozapine, trifluperazineDuration: VariableLength of follow up: 12 months post discontinuationDISCUSMean total DISCUS increased significantly during antipsychotic withdrawal, returning to baseline. Prevalence: baseline $31\%$ during withdrawal $85\%$ follow up $38\%$.Newell et al. 2002 USA [64]Prospective Pre post design (no control)n:20(may include participants also reported in Newell et al., 2000 [62] and Newell et al., 2001 [63]) mean age: 36.6 (SD 8.6) gender:$75\%$ male ethnicity: not reported ID = severe / profound $100\%$InpatientIntervention: antipsychotic dose reduced by $25\%$ every 3 months until discontinuedMedication: haloperidol, thioridazine, chlorpromazine, lozapine, trifluperazineDuration: VariableLength of follow up: 12 months post discontinuationPostural stabilityDISCUSPostural stability changed significantly during course of medication withdrawal and tended to return to baseline values at follow-up; mean total DISCUS increased significantly from baseline during antipsychotic withdrawal before returning to baseline values at follow up. Ramerman et al. 2019 The Netherlands [34]Prospective Pre post design (no control)n: 128(includes participants also reported in de Kujper et al., 2014 [33] and Ramerman et al., 2019 [32]) mean age: 48 (10–68) gender: $71\%$ male ethnicity: not reported ID = mild $15.6\%$ moderate $21.9\%$ severe $46.9\%$profound $15.6\%$CommunityIntervention: Antipsychotic reduced over 14 weeks, $12.5\%$ of the baseline dosage every two weeks data combined from two studies and part of clinical care. Medication: risperidone $23.4\%$ olanzapine $8.5\%$, quetiapine $1.6\%$, clozapine $2.3\%$, aripiprazole $0.8\%$, pipamperone $34\%$, haloperidol $5.4\%$, pericyazine $4\%$, zuclopentixol $5.5\%$, levomepromazine $2.3\%$, pimozide$5.5\%$Duration: 14 weeksLength of follow up: 6 months following planned discontinuationPrimary outcome measure: healthrelated quality of life RAND-36 Secondary outcome measures: ABC, UPDRS, SCOPAAUTPhysical well-being showed an increase in the group that had achieved complete discontinuation. Social functioning showed a decrease in the group that incompletely discontinued, which recovered at follow-up. Mental wellbeing decreased at 16 weeks, but recovered at follow up, regardless of complete or incomplete discontinuation. Shankar et al. 2019 U.K. [65]Retrospective Pre post design (no control)n: 71mean age: not reported gender: not reported ethnicity: not reported ID = not reportedCommunityIntervention: Usually dose changes were 10–$25\%$ of baseline dose reduced every 6–8 weeksMedication: antipsychoticsDuration: VariableLength of follow up: 3 monthsNumber reducing or discontinuing antipsychotic medicationNumber of patients requiring hospital admission or change in placement$46.5\%$ ($\frac{33}{71}$) discontinued antipsychotic medication$11.3\%$ ($\frac{8}{71}$) reduced over $50\%$ of antipsychotic dosageAt three months follow-up no one required hospital admission or change in placement. Spreat et al. 1993 USA [66]Pre post design (no control)n:86mean age: not reported gender: not reported ethnicity: not reportedID = not reportedInpatientIntervention: Medication reduction trialsMedication: haloperidol,mesoridazine, thioridazine,thioxanthene, chlorpromazine,thrifluoperazine,mo lindone,fluphenazine, chlorpromthixeneDuration: VariableLength of follow up:12 months post discontinuationChanges in antipsychotic prescribing> $50\%$ dose reduction or discontinuation: 14 ($16\%$)≤$50\%$ dose reduction: 26 ($30\%$)No change or increased dose: 46 ($53\%$).Stevenson et al. 2004 U.K. [67]Retrospective Pre post design (no control)n: 119mean age:44 (18–72) gender: $58\%$ male ethnicity: Not reportedID = mild $27.7\%$Moderate: $21.8\%$Severe: 18 ($15.1\%$Profound:$7.6\%$Not reported: $27.7\%$CommunityIntervention: Medication withdrawal programme following CSM advice. Medication: ThioridazineDuration: VariableLength of follow up: VariableNumber of people withdrawn from antipsychoticNumber of new prescriptionsNumbers needingextra carer support, Numbers of placement breakdown, family problems, admissions to hospital$7.6\%$ completely withdrew from antipsychotic medicines, and $48.7\%$ experienced onset/deterioration in problem behaviours or mental illhealth. The cost to the intellectual disabilities psychiatric service (over and above that of routine psychiatric care) was £258,050.10 people required increased levels of carer support to be provided; seven were excluded from a day centre placement, one person experienced a placement breakdown and moved to a new home, and six experienced considerable family problems.14 hospital admissions to an intellectual disabilities psychiatric assessment and treatment unitKey: AIMS Abnormal Involuntary Movement Scale, ABC Aberrant Behavior Checklist, ABS Agitated Behaviour Scale, BARNES Barnes Akathisia Rating Scale, BFCRS Bush-Francis Catatonia Rating Scale, BP Blood Pressure, CARS Childhood Autism Rating Scale, CGAS Children’s Global Assessment Scale (CGAS), CGI Clinical Global Impressions, CSM Committee on Safety of Medicines, CPRS Comprehensive Psychopathological Rating Scale, DAS Disability Assessment Schedule, DISCUS Dyskinesia Identification System Condensed User Scale, DISCO Dyskinesia Identification System-Coldwater, ECG Electrocardiogram, FBC Full Blood Count, HbA1c Glycated Haemoglobin, Kiddie SAD-PL Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetyime Version LFTs: Liver Function Tests, MOAS Modified Overt Aggression Scale, NOSIE Nurses’ Observation Scale for Inpatient Evaluation, PAS-ADD Psychiatric Assessment Schedule for Adult with Developmental Disability, PBS Positive Behaviour Support, PTH Parathyroid Hormone, RAND-36 measure of health related quality of life, U + Es Urea and elelctrolytes, UPDRS Unified Parkinson’s Disease Rating Scale, SCOPA-AUT Scales for Outcomes in Parkinson’s Disease - Autonomic DysfunctionTable 7Summary table of included case studiesAdams et al. 2017U.K. [68]Case studyn: 1 age: 30 gender: male ethnicity: not reportedID = mildCommunityIntervention: Two psychotropic medicines and a beta blocker were deprescribed separatelyMedication: olanzapine, carbamazepine, propranololDuration: 2 yearsLength of follow up: ongoing; 2 years from when deprescribing process beganDiscontinuation or reduction of dose of psychotropic medicinesGeneral wellbeingWeightOlanzapine and carbamazepine were stoppedPropranolol was reduced and deprescribing process ongoing. He is less tired, more alert, and better able to express himself. He has expanded his activities and increased his access to the community. He can cope better with changes to his routine. His behaviours are well managed by the behavioural strategies in place, and he has now been discharged by the psychiatrist to the GP.Weight reduced form 82 kg to 56 kg which was within recommendedBMI Bastiampillai et al. 2014Australia [69]Case studyn: 1 age: 28 gender: male ethnicity: not reportedID = moderateInpatientIntervention: Following warning from CSM in UK, thioridazine withdrawn and patient changed to risperidone. Medication: risperidoneDuration: not reportedLength of follow up: not reportedBehaviour and mental health symptomatologyDelusions and hallucinations reported within 2 weeks of stopping Thioridazine, hospitalised for 2 ½ years, unresponsive to several other Antipsychotics, prescribed clozapine and went into remissionBrahm et al. 2009USA [70]Case studyn: 1 age: 53 gender: maleethnicity: whiteID = moderateInpatientIntervention: deprescribingMedication: ziprasidoneDuration: Not reportedLength of follow up: Not reportedEpisodes of inappropriate sexual behaviourEpisodes of inappropriate sexual behaviour increased from 2 to 3 per month prior to discontinuation to 21 episodes the following month post discontinuationBranford D 2019U.K. [14]Case studies × 31. n: 1 age: 35 gender: male ethnicity: not reportedID = not reported2. n: 1age: not reported gender: not reported ethnicity: not reportedID = not reported3. n: 1age: not reported gender: male ethnicity: not reportedID = not reported1. Community2. Community3. Community1. Successful discontinuation, quality of life observations 2. Successful discontinuation 3. Successful discontinuation, quality of life observations 1. Antipsychotic discontinued post discontinuation he was more lively, wanting to go on more outings and tackle new activities. Staff aware to offer active support to meet his needs and his grabbing behaviours are understood.2. Chlorpromazine discontinued3. Antipsychotic discontinued Patient is now reported to be very positive. He enjoys walks, his self-confidence has gone up and his life is changing. He is cooking for himself and is keen to find work. Connor 1998 USA [71]Case studyn: 1 age: 11 gender: not reported ethnicity: not reportedID = moderateCommunityIntervention: deprescribingMedication: thioridazineDuration: 3 weeksLength of follow up: 12 weeksAIMSWithin 1 week of discontinuation patient developed new onset multiple involuntary movements consisting of jaw grinding, oral dyskinesias, bilateral hand rolling, vermiform tongue movements. and bilateral choreiform movements of his digits. When methylphenidate that was being co prescribed was also discontinued the movement disorder resolved. Dillon 1990USA [72]Case studyn: 1age: 7 yrs. 11 months gender: male ethnicity: not reportedID = borderlineCommunityIntervention: deprescribingMedication: clonidineDuration: 4 weeksLength of follow up: not reportedAdverse behavioursWhen withdrawn from clonidine over 4 weeks multiple self-destructive behaviours involving the theme of suffocation were reportedFaisal et al. 2021Ireland [73]Case studyn:1 age:13 gender: female ethnicity: not reportedID = moderateCommunityIntervention: deprescribingMedication: risperidoneDuration: unclearLength of follow up: unclearOverall clinical presentation, BFCRSIn first week following risperidone discontinuation nursing staff observed gradual change in behaviour, insomnia, increased salivation, mutism, echopraxia, immobility. Catatonic symptoms occurred over 8 weeks following discontinuation followed by admission to paediatric high dependency unit. Responded to im lorazepam, Resolution of catatonic symptoms after 7 weeks in hospitalGhaziuddin et al. 1990USA [74]Case studyn: 1 age: 34 gender: female ethnicity: not reportedID = moderateInpatientIntervention: deprescribingMedication: diazepamDuration: 6 weeksLength of follow up: 6 monthsChallenging behaviour mental health symptomatology dose of medication10 days after discontinuation of diazepam resembling mania reported. Improvement noted when diazepam represcribed. Lee et al. 2019U.K. [75]Case studyn: 1 age: early 40s gender: femaleethnicity: not reportedID = moderateCommunityIntervention: Flexible medication reduction in collaboration with PBS framework Involving an initial $25\%$ reduction with further changes dictated by behavioural data, the impact of any side effects, the opinions of care staff and of family membersMedication: risperidoneDuration: 6 monthsLength of follow up: not reportedDose of medication challenging behaviourReduction slowed down in response to increase in grabbing behaviours. Risperidone stoppedPBS supported medication reduction reduced challenging behaviourMcLennan 2019Canada [76]Case studyn: 1 age: 15 gender: male ethnicity: white ID = moderateCommunityIntervention: deprescribing 6 psychotropic medicines,Medication: quetiapine, lamotrigine, clonidine, olanzapine, sertraline, and ziprasidoneDuration: whole process over approx.18 monthsLength of follow up: not reportedNumber of medicines stoppedQuetiapine, lamotrigine, clonidine, olanzapine, sertraline successfully discontinued ziprasidone addedtrazadone prn added for sleepziprasidone associated with unsuccessful attempt to deprescribeKey: a Possibility of potential overlap of participants with other included studies by the same author(s)AIMS Abnormal Involuntary Movement Scale, ABC Aberrant Behavior Checklist, ABS Agitated Behaviour Scale, BARNES Barnes Akathisia Rating Scale, BFCRS Bush-Francis Catatonia Rating Scale, BP Blood Pressure, CARS Childhood Autism Rating Scale CGAS: Children’s Global Assessment Scale (CGAS) CGI: Clinical Global Impressions, CSM Committee on Safety of Medicines, CPRS Comprehensive Psychopathological Rating Scale, DAS Disability Assessment Schedule, DISCUS Dyskinesia Identification System Condensed User Scale, DISCO Dyskinesia Identification System-Coldwater, ECG Electrocardiogram, FBC Full Blood Count, HbA1c Glycated Haemoglobin, Kiddie SAD-PL Kiddie Schedule for Affective Disorders and Schizophrenia, Present and Lifetyime Version LFTs: Liver Function Tests, MOAS Modified Overt Aggression Scale, NOSIE Nurses’ Observation Scale for Inpatient Evaluation, PAS-ADD Psychiatric Assessment Schedule for Adult with Developmental Disability, PBS Positive Behaviour Support, PTH Parathyroid Hormone, RAND-36 measure of health related quality of life, U + Es Urea and elelctrolytes, UPDRS Unified Parkinson’s Disease Rating Scale, SCOPA-AUT Scales for Outcomes in Parkinson’s Disease - Autonomic DysfunctionTable 8Summary of quality appraisal of included studiesAuthor/YearKey Sources of BiasSummary of quality assessment for RCTs1Research Units on PediatricPsychopharmacology Autism Network.2005 [20]• parent raters• process for randomisation, recruitment and sampling unclear• short follow up period2Ahmed et al. 2000 [21]• selection bias, not blinded, no allocation concealment, process for randomisation, recruitment and sampling unclear• no reporting of PRN prescribing and administration, non-pharmacological interventions, level of support, co morbidities, level of ID• baseline characteristics of experimental and control groups uneven• short follow up period3de Kuijper et al. 2014 [23]• selection bias, not blinded, no allocation concealment, process for randomisation, recruitment and sampling insufficient.• no reporting of other psychotropic medication prescribing, PRN prescribing and administration, non-pharmacological interventions, level of support, co morbidities• short follow up period4de Kuijper, G., et al. 2013 [24]• side arm of previous study• outcomes are statistically significant but unclear if clinically significant.• lack of evaluation of confounding factors e.g. changes in diet and exercise• measurements and results were not AP specifically reported5de Kuijper et al. 2014 [25]• side arm of previous study• confounding factors that could have affected the results include linking effects to the actual AP eg risperidone has greater effect on prolactin than others in the sample, olanzapine has a greater effect on weight gain.• measurements and results were not AP specifically reported6Haessler et al. 2007 [28]• recruitment, randomisation and blinding process unclear• no power calculation• baseline comparability unclear• short follow up period• unclear if outcomes were discontinuation effects or reduced effects of placebo• no tapering• no reporting of other psychotropic medication prescribing, PRN prescribing and administration, non-pharmacological interventions, level of support, co morbidities7Hassler et al. 2011 [29]• see no 6 Haessler, F., et al. 2007• small sample• not blinded8Heistad et al. 1982 [30]• no power calculation• rating scales not specified• rate of discontinuation unclear• process of randomisation unclear• simultaneous withdrawal of antiparkinsonian medication,• no reporting of PRN prescribing and administration, non-pharmacological interventions, level of support, co morbidities• short follow up period9McNamara et al. 2017 [31]• significantly underpowered• trial finished prematurely and reported as pilot10Ramerman et al. 2019 [32]• no allocation concealment,• no power calculation• no reporting of other psychotropic medication prescribing, PRN prescribing and administration, non-pharmacological interventions, level of support, co morbidities11Smith et al. 2002 [22]• See no 2 Ahmed, Z., et al. 2000Summary of quality assessment for non randomised controlled trials (CTs)1Aman et al. 1985 [35]• subjective outcome measurements• sampling and recruitment process unclear• no power calculation• short follow up period2Carpenter et al. 1990 [36]• selection method unclear• exposure inadequately ascertained• causality inadequately ascertained• short follow up period3Gerrard et al. 2019 [37]• recruitment and allocation process unclear• length of follow up not reported.4Swanson et al. 1996 [39]• selection bias• control group inadequately matched• inadequate blinding• statistics or statistical tests inadequately reported or inappropriate• institutional setting• intervention poorly defined5Wigal et al. 1993 [40]• selection bias• statistics or statistical tests inadequately reported or inappropriate• missing baseline information• intervention poorly defined6Wigal et al. 1994 [41]• selection bias• statistics or statistical tests inadequately reported or inappropriate• missing baseline information• intervention poorly defined7Zuddas et al. 2000 [42]• no power calculation and small number of participants• sampling and recruitment unclear• confounding factors include psychological, behavioural and environmental interventionsSummary of quality assessment for non randomised no control Pre Post Studies (PPSs)1Brahm et al. 2003 [43]• missing baseline information• variable deprescribing schedules2Branford 1996 [44]• patients living with relatives, those in unsupervised accommodation, and those in accommodation where staff were unwilling to engage excluded from study• selection bias• use of unvalidated measures or non-standard assessment tools• missing baseline information• intervention poorly defined• selective reporting or incomplete3de Kuijper et al. 2018 [33]• sampling and recruitment unclear• rater reliability4de Kuijper et al. 2018 [33]• as above5Ellenor et al. 1977 [46]• intervention poorly defined• outcomes measures unclear6Ferguson et al. 1982 [47]• duration of intervention variable• length of follow up not reported7Fielding et al. 1980 [48]• unvalidated outcome measures8Findholt et al. 1990 [49]• high turnover of medical staff delivering the intervention9Gerrard 2020 [38]• author / researcher is the clinician delivering the intervention10Howerton et al. 2002 [50]• differing referral rates from the various primary care providers• poor follow up rates11Inoue et al. 1982 [51]• limited baseline information12Janowsky et al. 2006 [52]• selection bias• missing baseline information• intervention poorly defined13Janowsky et al. 2008 [53]• selection bias• missing baseline information• intervention poorly defined14Jauernig et al. 1995 [54]• selection bias• use of unvalidated measures or non-standard assessment tools• missing baseline information• intervention poorly defined15LaMendola et al. 1980 [55]• intervention poorly defined• duration of intervention and length of follow up not reported• missing baseline and outcomes information16Lindsay et al. 2004 [56]• poorly defined methodology• small sample size• inconsistent weighing scales• no BMI measurements• missing data• no reporting of dietary modification, environmental and behavioural interventions17Luchins et al. 2004 [57]• poor reporting of duration of intervention and length of follow up18Marcoux 1985 [60]• intervention poorly defined19Marholin et al. 1979 [58]• selection method unclear• causality not adequately ascertained• short follow up20Matthews et al. 2003 [59]• duration of intervention and length of follow up missing• outcomes poorly reported21May et al. 1995 [61]• small sample size• selection bias• use of unvalidated measures or non-standard assessment tools• statistics or statistical tests inadequately reported or inappropriate• missing baseline information• selective reporting or incomplete outcome data22Newell et al. 2000 [62]• small sample size• selection bias• use of unvalidated tools• missing baseline and outcome data23Newell et al. 2001 [63]• selection bias24Newell et al. 2002 [64]• selection bias• inadequate blinding• use of unvalidated measures or non-standard assessment tools• statistics or statistical tests inadequately reported or inappropriate• missing baseline information25Ramerman et al. 2019 [32]• weak methodology of combining studies with different designs26Shankar et al. 2019 [65]• unvalidated outcome tools27Spreat et al. 1993 [66]• selection bias• institutional setting• missing baseline information• intervention poorly defined28Stevenson et al. 2004 [67]• weak methodology• use of non standardised assessment tools• subjective outcome measurementsSummary of quality assessment of case studies1Adams and Sawhney 2017 [68]• selection method unclear2Bastiampillai et al. 2014 [69]–3Brahm et al. 2009 [70]–4Branford 2019 [14]• selection method unclear5Connor D 1998 [71]6Dillon J 1990 [72]• outcome and causality inadequately ascertained7Faisal et al. [ 73]•8Ghaziuddin et al. 1990 [74]–9Lee et al. 2019 [75]• selection method unclear10McLennan J 2019 [76]– Included studies were carried out in nine countries, in both inpatient ($$n = 31$$) and community settings ($$n = 24$$). One study did not report setting. Details of participant characteristics and numbers were incompletely reported in several studies, and in studies conducted by the same researchers, there was lack of clarity regarding duplication of participants [34, 37, 77].The total number of participants across all studies where reported was 3292. The percentage of participants reported to have severe/profound intellectual disabilities varied across study types ranging from $49\%$ for RCTs, $62\%$ for non-randomised controlled studies and $72\%$ for pre post studies without randomisation control. One case study reported the participant to have severe/profound intellectual disabilities. Furthermore, the level of intellectual disability was incompletely or not reported in $33\%$ of studies and the amount and type of support provided to participants was not reported in any of the studies. Ethnicity was reported in only five studies. The most frequently deprescribed psychotropic medicines across all studies were typical and atypical antipsychotics. Aside from one RCT, the prescribing and administration of pro re nata (PRN) medication for the management of behaviours that challenge was incompletely reported [31]. Intervention approaches ranged from sudden discontinuation to gradually tapering dosage over 28 weeks. Sixteen studies reported the deprescribing intervention as integral to or supported by the wider multidisciplinary team [37, 38, 44, 46–51, 54, 55, 57, 65, 66, 75]. However, there were no data reported regarding working across organisation boundaries such as between primary and secondary care and no data reporting specific non pharmacological interventions to support deprescribing although for three studies the deprescribing interventions were in the context of a Positive Behaviour Support (PBS) framework [37, 38, 75]. Evidence of pharmacists working within the multidisciplinary team (MDT) was reported in 11 studies [37, 38, 44, 47, 49, 51, 54, 57, 60, 65, 75] and pharmacist non-medical prescribers delivering the interventions were reported in three studies, although the same pharmacist prescriber was involved in all three [37, 38, 75]. Follow up ranged from immediately after medication was reduced or discontinued to 15 years. For 22 studies, follow up was variable or not specified. Outcomes were measured using a range of standardised rating tools and questionnaires. Input from patients, carers, and family and models of co-production in developing multidisciplinary deprescribing interventions were not reported. The reporting of shared decision-making approaches involving patients, carers, and clinicians within deprescribing interventions were reported in three studies (all within a Positive Behavioural Support (PBS) framework) [37, 38, 75, 78]. Quality of Life outcomes were only reported in one pre post study [34] and one paper reporting three case studies [14]. Across all study types there was incomplete reporting of rates of complete psychotropic discontinuation, at least $50\%$ psychotropic dose reduction, represcribing, behavioural changes and emergence of adverse effects. Where reported in RCTs using a tapering approach to deprescribing, rates of complete deprescribing ranged from 33 to $84\%$ [20, 21, 23, 31, 32]. Relapse rates due to worsening of behaviour ranged from $62.5\%$ to no worsening. In the non randomised group of studies, Gerrard et al. [ 37] reported up to $60\%$ complete discontinuation with a further $50\%$ achieving a $50\%$ dose reduction with only one person requiring represcribing This contrasted to findings by Zuddas et al. [ 42] who reported that all three people who achieved discontinuation displayed behavioural deterioration requiring represcribing. ## Randomised control trials (RCTs) Seven RCTs evaluated the effects of deprescribing antipsychotic medicines [20, 21, 23, 30–32, 79], three on typical antipsychotics, three on atypical antipsychotics, and one on both types. Four studies were conducted in community settings [20, 23, 31, 32], two studies were carried out in an inpatient settings [30, 79] and one study included a mix of both [21]. Sample sizes ranged from 22 to 100 participants, with participant ages, where reported, ranging from 5 years to 78 years, with all 7 studies reporting outcomes for adults, 4 studies reporting outcomes for adolescents (ages 10--19 years [80]) and two studies reporting outcomes for children. The majority of participants were male ranging from 48 to $87\%$ across RCTs. Length of follow up period varied from 4 weeks to 9 months following discontinuation or maximum dosage reduction. Primary outcome measures were firstly the changes in frequency and intensity of episodes of behaviours that challenge at follow up (we report follow up as time after planned complete discontinuation or maximum dosage reduction) and secondly, numbers of participants who reduced or stopped their antipsychotic medication. ## Changes in behaviours that challenge Assessment of the effects of deprescribing antipsychotics on behaviours that challenge was a primary outcome in all seven RCTs. Deprescribing antipsychotic medication was associated with a reduction in behaviours that challenge irrespective of whether the antipsychotic was tapered over 14 or 28 weeks in an RCT by de Kuijper et al. [ 23] This study involving 98 participants in community settings reported firstly that higher ratings of extrapyramidal and autonomic symptoms at baseline were associated with less improvement of behavioural symptoms after discontinuation; and secondly, higher baseline Aberrant Behavior Checklist (ABC) scores were associated with an increased likelihood of incomplete discontinuation [23]. Authors of studies where antipsychotic doses were reduced over 6 months [31] or 4 months [21] reported no clinically important changes in participants’ levels of aggression or behaviours that challenge at 9 months and 1 month respectively after planned discontinuation. Furthermore in a study by Ramerman et al. [ 32] study no change in irritability was reported when risperidone was reduced over 14 weeks in 86 participants compared to placebo. In a study of the effects of withdrawal of zuclopentixol by Hassler et al. [ 79] behaviours that challenge increased at 12 weeks after sudden discontinuation of zuclopenthixol in 20 participants compared to the 19 participants that continued to be prescribed the antipsychotic. Heistad et al. [ 30] also reported increases in behaviours that challenge in participants undergoing deprescribing of thioridazine in a series of 5 separate groups within an RCT. Rates of relapse of behaviours that challenge were reported to be higher at 5 weeks follow up when risperidone was discontinued over 3 weeks in 38 adolescents and children in an RCT by the Pediatric Psychopharmacology Autism Network research units [20]. Relapse rates were $62.5\%$ for gradual placebo substitution and $12.5\%$ for continued risperidone [20]. The deprescribing interventions of the three RCTs reporting overall increase in behaviours that challenge involved sudden discontinuation [30, 79] or tapering over the short time of 3 weeks. This compares to antipsychotics deprescribed over 14 to 28 weeks in studies reporting no change or a reduction in behaviours that challenge [21, 23, 31, 32]. In addition the follow up periods in the three RCTs [20, 30, 79] reporting increases in behaviours that challenge were shorter; 4 to 12 weeks compared to 4 weeks to 12 months in studies reporting no change or a reduction in behaviours that challenge [21, 23, 31, 32]. Two [30, 79] of the three RCTs reporting increases in behaviours that challenge were conducted in inpatient settings. Three [23, 31, 32] of the four studies reporting more favourable results regarding behaviours that challenge, were carried out in community settings, the fourth study [21] involving participants in both community and inpatient settings. The studies reporting less favourable effects on behaviours that challenges involved larger percentages of participants with severe or profound intellectual disabilities ranging from 34 to $72\%$ [20, 30, 79]compared to 24 to $63\%$ [23, 32] in two of the four studies reporting more favourable outcomes although two studies did not report level of intellectual disability [21, 31]. ## Reduction /discontinuation completion outcomes Four studies [21, 23, 31, 32] used a study design involving tapering of the dose of antipsychotic dose three [21, 23, 32] of which reported numbers of participants achieving complete withdrawal. Ahmed et al. [ 21] reported $33\%$ of 36 participants achieved discontinuation with a further $19\%$ achieving and maintaining at least a $50\%$ reduction at one month follow up. de Kuijper et al. [ 23] reported $37\%$ of 98 participants achieved complete discontinuation with significant improvements in behaviours that challenge at 12 weeks follow up. Secondly, they reported re-prescribing at follow up after an initially discontinuing in $7\%$ of participants. Ramerman et al. [ 32] reported $82\%$ of the 11 participants in the deprescribing group, completely withdrew from risperidone. ## Other outcomes Four studies reported outcomes of physical and mental health and wellbeing. One study by de Kuijper et al. [ 24, 81] and another by Ramerman et al. [ 32] reported positive effects of deprescribing antipsychotics on physical health parameters. de Kuijpers et al. [ 24, 81] reported a mean decrease of 4 cm waist circumference, of 3.5 kg weight, 1.4 kg/m2 BMI, and 7.1 mmHg systolic blood pressure at 12 weeks follow up after planned discontinuation, in 98 participants following complete discontinuation of antipsychotics over 14 or 28 weeks. Ramerman et al. [ 32] reported favourable group time effects on weight, waist, body mass index, prolactin levels and testosterone levels in 11 participants who completely discontinued risperidone. However, this study was underpowered and follow up was limited to 8 weeks. Both complete discontinuation and dosage reduction of antipsychotics were reported by de Kuijper et al. [ 24] to lead to a decrease in prolactin plasma levels and an increase in levels of C- telopeptide type 1 collagen (CTX), the bone resorption marker [24]. Ahmed et al. [ 21] reported an association of typical antipsychotic reduction with increased dyskinesia. However the follow up time for this study was 4 weeks compared to the study by de Kuijper et al. [ 24]which had a follow up period of 12 weeks and the study by Ramerman et al. [ 32] which had an 8 week follow up period. Conversely, Hassler et al. [ 79] reported weight gain in participants who discontinued zuclopentixol [79] in an inpatient setting. Two [24, 32, 81] of the three studies reporting positive physical health outcomes were carried out in community settings and the third study [21] involved participants in both hospital and community settings. Four studies [35, 39–41] reported changes in dyskinesia scores following the deprescribing of antipsychotics. Aman and Singh [35] examined the effects of deprescribing typical antipsychotics on dyskinesias comparing a deprescribing group to a group that were not prescribed antipsychotics. The evidence was inconclusive although the deprescribing group was rated as having higher total dyskinesia scores. Swanson et al. [ 39] reported transient increases in average Dyskinesia Identification System Condensed User Scale (DISCUS) scores after risperidone discontinuation with return to baseline 6 months after discontinuation. Wigal et al. [ 40] reported larger increases in DISCUS scores associated with greater dosage reductions of antipsychotics. Another study by the same authors measured the rates of dyskinesias during their medication review and dose reduction programme [41]. They reported that $63\%$ of participants who discontinued antipsychotics and $29\%$ of those who were receiving reduced dosages developed dyskinesias. In participants who were not medicated or where there was no change or increase in dosage, no dyskinesias were reported. All four studies were carried out in inpatient settings and most of the participants had severe or profound intellectual disabilities, ranging from 86 to $100\%$. Nine studies [34, 38, 45, 56, 62–65, 67] reported physical health and wellbeing findings, one of which also reported mental health and wellbeing outcomes. Ramerman et al. [ 34] reported improved physical health amongst those who completely discontinued antipsychotics, while social functioning and mental wellbeing initially deteriorated in those who incompletely discontinued; however, this was temporary, and they recovered at 6 months after planned discontinuation. In addition, they reported that participants who had completely discontinued had temporary decreases in mental wellbeing. Similar findings were reported by de Kuijper et al. [ 45] who reported a positive association between complete antipsychotic discontinuation with less-severe parkinsonism and lower incidence of health worsening compared with participants with incomplete discontinuation. Shankar et al. [ 65] reported no placement breakdowns or hospital admissions following antipsychotic deprescribing at 3 months follow up. This contrasts to Stevenson et al. [ 67] who reported $12\%$ of participants were admitted to psychiatric assessment and treatment unit and $8\%$ of participants required increased carer support following deprescribing of thioridazine. Two studies reported weight loss following deprescribing; Linsday et al. [ 56] reported the weight of 14 children returned to baseline at 12 and 24 months following discontinuing risperidone and Gerrard [38] reported a reduction in weight gain following deprescribing. Newell et al. [ 62–64] reported transient withdrawal dyskinesia in three studies monitoring participants during the reduction of typical antipsychotics. ## Integrated synthesis of RCTs The evidence from RCTs regarding the effects of deprescribing on behaviours that challenge at follow up was mixed. The length of follow up was inadequate for the majority of studies with four studies reporting follow up periods of between four and eight weeks [20–22, 30, 32] and a further two studies reporting follow up at 12 weeks [23, 24, 28, 79, 81] and therefore it could not be established if successful deprescribing could be maintained in those studies reporting positive effects or no change on behaviours that challenge. The evidence suggests that discontinuing or reducing the dosage of antipsychotics can have positive effects on physical health such as the reversal of antipsychotic markers for metabolic syndrome. The several subclasses of antipsychotics and variable doses at baseline may limit the robustness of this evidence. Methodological limitations across all RCTs included the use of small sample sizes and limited reporting of information about blinding procedures and methods to ensure allocation concealment. Two studies did not make use of blinding [21, 23]. The treating physician was involved in the sampling and recruitment of participants in two RCTs leading to possible selection bias [21, 32]. ## Nonrandomised controlled studies Seven nonrandomised controlled studies evaluated the effects of deprescribing antipsychotic medicines. Two studies were conducted in community settings [37, 42] and five studies were carried out in inpatient settings [35, 36, 39–41]. Sample sizes ranged from 6 to 80 participants, with participant ages, where reported, ranging from 7 years to 53 years, with 4 studies reporting outcomes for adults, one study reporting outcomes for adolescents (ages 10–19 years [80]) and one study reporting outcomes for children. The majority of participants were male ranging from 50 to $90\%$ across studies. Length of follow up period varied from 8 weeks in one study to between 5 months and 12 months following discontinuation or maximum dosage reduction in those studies reporting. Two studies reported nonspecific variable follow up periods. ## Behaviours that challenge Two studies reported on changes in episodes or severity of behaviours that challenge. Zuddas et al. [ 42] reported progressive deterioration of behaviours in the 3 out of 10 adolescents and children participants who discontinued risperidone. A study by Swanson et al. [ 39] reported transient increases in ABC scores in 21 participants, $96\%$ of whom had severe or profound intellectual disabilities, who discontinued risperidone and were co prescribed antiepileptic medication. However, this was not reported in the 19 participants who discontinued risperidone in the absence of antiepileptic medication. From 12 studies reporting on the effects of deprescribing psychotropic medicines on behaviours that challenge the findings are mixed [33, 44, 46, 48, 52–54, 57–59, 61, 67]. Branford [44] reported no deterioration in behaviours that challenge in $25\%$ of 123 participants who underwent a reduction of antipsychotics. However, $42\%$ did show a deterioration in behaviours that challenge, and for $33\%$, changes in behaviour were not reported. de Kuijper et al. [ 33] reported at 16 weeks post planned discontinuation, $6\%$ of participants had shown improvement and $9\%$ had a worsening of behaviour; at 28 weeks, these percentages were 9 and $15\%$, and at 40 weeks 21 and $7\%$, respectively. They also concluded that at 28 weeks, those who had not achieved complete discontinuation had significantly more worsening of behaviour than those who had successfully discontinued. Ellenor et al. [ 46] reported ABS scores for 54 participants which showed a slight increase in behaviours that challenge for all three groups; medication reduced, medication stopped and control group. Fielding et al. [ 48] reported that all but eight of 68 participants whose antipsychotic medication was reduced, achieved permanent dosage reductions while maintaining rates of behaviours that challenge similar to those observed prior to deprescribing. They also found that behaviours that challenge decreased or remained stable for the majority although they slightly increased for some. In two studies by Janowsky et al. [ 52, 53] $40\%$ of 138 participants with severe or profound intellectual disabilities and $96\%$ of 49 participants with severe or profound intellectual disabilities were reported to experience a relapse in behaviours that challenge. Jauernig et al. [ 54] reported a lower frequency of behaviours that challenge at follow-up compared to baseline in all 3 patients ($100\%$) whose antipsychotic had been discontinued and in 15 patients ($79\%$) of 19 who underwent dose reduction. Luchins et al. [ 57] reported an improvement in behaviour associated with the reduction in prescribing of antipsychotics. Marholin et al. [ 58] reported reversible changes in behaviours that challenge when chlorpromazine was withdrawn suddenly and then restarted 23 days later in 6 participants with severe intellectual disabilities. Matthews and Weston [59]reported over $50\%$ of 77 participants who were on regular thioridazine experienced behaviours that challenge during or following discontinuation. Adverse events were significantly associated with the duration of previous thioridazine prescription. May et al. [ 61] evaluated the effects of deprescribing risperidone in people with severe and profound intellectual disabilities and reported transient worsening of behaviours that challenge in $39\%$, persistent worsening in $39\%$, and progressive improvement in $22\%$ of participants. Stevenson et al. [ 67] reported that $48.7\%$ experienced onset or deterioration in behaviours or mental ill-health following the deprescribing of thioridazine. ## Reduction /discontinuation outcomes Two studies reported outcomes regarding numbers of participants who had their psychotropic medicines deprescribed. Zuddas et al. [ 42] reported that three children or adolescents discontinued risperidone although all three required the represcribing of an antipsychotic within 6 months following discontinuation. A study by Gerrard et al. [ 37] comparing two groups of participants, reported a higher success rate for psychotropic medication reduction and discontinuation when this was carried out within a PBS framework. The authors reported that participants in the non-PBS group were more likely to have their medication increased following an initial reduction. Support was delivered by staff using a PBS framework for a minimum of three months post discontinuation or medication reduction. One patient required a medication increase or restart when supported by PBS. This compared to $66\%$ of participants in the non-PBS group. However, evidence is limited by unequally matched groups in terms of intellectual disability [37] and the follow up times were variable. Nineteen studies [33, 38, 43, 44, 46–55, 57, 60, 65–67] reported lower prescribing rates, complete discontinuation, or reduced dosages of psychotropic medicines,14 of which reported an evaluation of a clinical service involving multidisciplinary medication reviews with varying time periods for follow up [38, 44, 46–51, 54, 55, 57, 60, 65, 66]. Nine of the studies reported evaluations of medication reviews involving pharmacists [38, 44, 46, 47, 49, 51, 54, 60, 65]. A medication review programme by Branford [44] reported $16\%$ of 198 adult participants withdrawn from antipsychotics and $28\%$ maintained on reduced dosage of antipsychotics at 12 months follow up. One hundred and twenty-three of the 198 participants underwent a reduction of their antipsychotics and $25\%$ discontinued antipsychotics while $46\%$ were receiving reduced dosages at 12 months follow up. Gerrard et al. [ 38] reported that 24 psychotropic medications were stopped within their retrospective study; 20 of these were with PBS support and ongoing deprescribing continued at the time of publication of the study in 2020. de Kuijper et al. [ 33] reported $61\%$ had completely discontinued antipsychotics at 16 weeks, $46\%$ at 28 weeks, and $40\%$ at 40 weeks. However, 32 % of participants who initially withdrew at 16 weeks were represcribed antipsychotics at 28 weeks follow up and $13\%$ who withdrew at 28 weeks were represcribed antipsychotics at 40 weeks follow up. Studies by Findholt et al. [ 49] and Howerton et al. [ 50] reported a decrease in polypharmacy. A retrospective review by Luchins et al. [ 57] reported the reduction of antipsychotic prescribing was associated with prescribing other psychotropic medicines such as carbamazepine, lithium, and buspirone. ## Pre post study designs Twenty-seven pre-post studies evaluated the effects of deprescribing psychotropic medicines, with all studies reporting interventions involving antipsychotics and 6 studies reporting on interventions on more than one class of psychotropic medication [38, 46, 49–51, 55] including anxiolytics ($$n = 3$$), antidepressants ($$n = 4$$), sedatives/hypnotics ($$n = 2$$), benzodiazepines ($$n = 2$$), anticonvulsants ($$n = 1$$) and lithium ($$n = 2$$) in addition to antipsychotics [46, 49–51, 55, 77]. Eight studies were conducted in community settings,17 studies were carried out in inpatient settings, one study involved a mix of both, and one study did not report setting. Sample sizes ranged from 6 to 250 participants, with participant ages, where reported, ranging from 5 years to 84 years, with 14 studies reporting outcomes for adults, 6 studies reporting outcomes for adolescents (ages 10--19 years [80]) and one study reporting outcomes for children. The majority of participants were male ranging from 50 to $100\%$ where reported. Length of follow up period varied from 8 weeks to 15 years following discontinuation or maximum dosage reduction with 12 studies reporting variable follow up or not reporting follow up periods. ## Integrated synthesis of non randomised controlled and pre post studies The non randomised controlled studies and pre post studies, although they have less methodological rigour, offered similar evidence to the RCTs through their use of evaluations of clinical services and longer follow up periods. In addition, the non-randomised controlled studies report more extensively on dyskinesias. One non randomised controlled study [37] and 14 pre post studies [38, 44, 46–51, 54, 55, 57, 60, 65, 66] evaluated clinical services providing deprescribing interventions, making use of a multidisciplinary model rather than the traditional medical model when reducing medication. Studies by Gerrard et al. [ 37, 38] and a case study by Lee et al. [ 75] reported that deprescribing outcomes for a range of psychotropic medicines were more successful alongside PBS compared to patients undergoing deprescribing interventions without this framework. The external validity of the pre post studies was limited due to their lack of control or comparison group which would have improved the methodology. Well over half ($67\%$) were conducted in inpatient settings. However, the reporting of deprescribing of psychotropic medicines other than antipsychotics allows for some tentative conclusions about the de-prescribing of psychotropics other than antipsychotics. Outcome from these studies suggest that deprescribing interventions within a multidisciplinary model may be associated with successful outcomes in terms of reducing and discontinuing psychotropic prescribing which could be maintained over a longer-term basis. ## Case studies The effects of deprescribing on several classes of psychotropic medications were reported in 13 case studies [14, 68–76, 82] (3 separate case studies were reported in one paper). Five of these studies [14, 68, 75, 76] reported an association between successful discontinuation and improved quality of life. Another set of seven studies [69–74, 82] reported a range of adverse effects including delusions, hallucinations, inappropriate sexual behaviours, transient dyskinesias, self-harm, mania, aggression and catatonia following the deprescribing intervention. Lee et al. [ 75] described using flexible medication reduction within a PBS framework resulting in the discontinuation of risperidone. ## Discussion We are mindful that some studies included psychotropic medicines that are no longer prescribed e.g. thioridazine, and some are rarely prescribed e.g. chlorpromazine. However the focus of our review was the psychotropic deprescribing process rather than evidence of effectiveness of deprescribing individual medicines. Hence the findings from these studies will still be relevant and add to the evidence base of the effects of deprescribing psychotropic medicines in people with intellectual disabilities. Overall the evidence from RCTs indicated that deprescribing interventions for antipsychotic medicines prescribed for the management of behaviours that challenge in people with intellectual disabilities may lead to a reduction in dosage and may be discontinued under some circumstances. Reducing and discontinuing antipsychotics may have positive health outcomes on physical health parameters. This is particularly important in this population as people with intellectual disabilities experience health inequalities with more co morbidities and reduced life expectancy [83]. Findings demonstrating the successful reduction in dosage and discontinuation of psychotropic medicines was also found in the other study designs. Although of reduced methodological rigour, the longer follow up times and the inclusion of other classes of psychotropic medicines in addition to antipsychotics of these studies, added to the evidence base. However, these positive findings need to be considered in the context of a lack of high quality RCTs. Negative effects of deprescribing should be acknowledged. Firstly, RCTs reported relapsing of behaviours that challenge, although this evidence is limited by variable periods of follow up, and secondly dyskinesias were reported by four non randomised controlled studies [35, 39–41] and three pre post studies [62–64]. Furthermore, initial deprescribing is sometimes reversed with the represcribing of psychotropic medicines at follow up and therefore caution is needed when synthesising evidence from studies with a wide range of follow up periods. A variety of reasons were given for represcribing which included increases in episodes and intensity of behaviours that challenge, restrictiveness of setting and staff training [21, 30]. Physical discomfort is associated with behaviours that challenges [84]. Evidence suggests that people with intellectual disabilities are more susceptible to movement side effects of antipsychotic drugs [85]. As dyskinesias may be exacerbated by discontinuation of psychotropic medication this raises concerns that tapers will sometimes be aborted due to irritability and agitation that may be secondary to discontinuation effects rather than a relapse of symptoms related to the medicine’s efficacy. Aside from represcribing psychotropic medicines, outcomes regarding the consequences of relapsing behaviours that challenge such as placement breakdown, hospital admission and increase in required carer support was limited to two pre post studies [65, 67]. Lack of consensus regarding optimal follow up time periods was a consistent theme across all included studies affecting the heterogeneity of the methodologies. This impacted on synthesizing the evidence regarding positive outcomes. Although the findings from our systematic review suggests that deprescribing interventions in people with intellectual disabilities prescribed psychotropic medication, may lead to dosage reductions and the discontinuation of these medicines, it remains unclear how to optimise the circumstances for this to take place. However, despite the limitations to the evidence base, it seems likely that planning before initiating the deprescribing process may be helpful. This could include staff training to ensure that other interventions for any transient increases in behaviours that challenge can be optimised together with plans for addressing the emergence or the worsening of dyskinesias. The evaluation of stakeholder experiences to understand barriers and enablers of this process may provide clarity. There is a large variation in clinical practice of prescribers regarding discontinuation of psychotropic medication, both in terms of the deprescribing process and the individuals who are identified as suitable for deprescribing, This may be partially related to environmental factors as setting culture and attitudes of staff towards off-label antipsychotic medication use in people with intellectual disabilities [86]. How these decisions are made will likely impact on the success of deprescribing interventions. In summary our findings suggest it is likely that there may be several factors affecting successful outcomes of the psychotropic deprescribing process. Enablers of the deprescribing process may be the views and clinical practice of clinicians, embracing a multidisciplinary approach, pre-planning of the deprescribing process including how to address emergence or worsening of movement effects, availability and quality of staff training, and stakeholder attitudes, including those of the individuals who are prescribed these medicines, towards the deprescribing process. Barriers to this process may be the perceived negative effects of the deprescribing process, limited knowledge of discontinuation effects of the individual psychotropic medicines, lack of input from carers and lack of understanding of the experiences of people whose psychotropic medicines have been reduced. Our review found there was a lack of evidence in the literature of shared decision making between people with intellectual disabilities and the healthcare team. The lack of literature about quality of life is reflected in our review as we were unable to report extensively on this outcome. ## Strengths and weaknesses of the methodology of included studies With reference to reporting on medications, firstly, studies focused upon deprescribing one class or one specific type of medicine and did not address the co-prescribing of other psychotropic medicine, or an increase or decrease in dosage of these medicines. Secondly, studies did not report complete details regarding polypharmacy, where participants were co prescribed several medicines or were taking medicines available without prescriptions. This may have led to drug interactions and adverse drug reactions which could affect the outcomes of studies. Thirdly, the reporting of physical health medication and the prescribing and administration of PRN medication for the management of behaviours that challenge was missing from the included studies. Furthermore, there was frequent incomplete reporting of concurrent non-pharmacological treatments such as behavioural, psychological, and environmental interventions. ## Strengths and weaknesses of the methodology of the systematic review A weakness of our methodology was that the second reviewer was restricted to independently screening $20\%$ of titles, abstracts and full text papers and extracting data from $20\%$ of included studies due to limited resources. Within this review, we focused upon deprescribing all psychotropic medicines in people with intellectual disabilities, rather than just antipsychotics which has been previously examined [15]. The adverse effects and effects associated with discontinuation may vary between classes of psychotropic medicines and within classes. Further, it should be noted that while the inclusion of single case studies and retrospective studies allowed for a more inclusive synthesis of the literature, studies of this type are prone to bias. ## Implications for clinicians and policymakers Evidence based guidelines for prescribing psychotropic medicines in people with intellectual disabilities tend to focus on antipsychotics. There is a need to evaluate all psychotropic prescribing, including PRN, in people with intellectual disabilities to ensure that medication is being optimised and appropriate interventions are implemented within the multidisciplinary framework when addressing the management of behaviours that challenge. We did not find evidence of involvement of patients, carers, and family within the development and process of deprescribing interventions. Whilst we have reported evidence suggesting that a multidisciplinary approach may be appropriate, policy makers and clinicians should be mindful to: (a) co-produce deprescribing interventions with people with intellectual disabilities to ensure they reflect their needs, which will, (b) help empower individuals to make informed decisions about their healthcare, and (c) facilitate full stakeholder engagement in shared decision making. It was noted that it was unclear as to how this was included within the intervention process within the included studies [14, 87, 88]. Co-production acknowledges that people with “lived experience” of using services are best placed to advise on what support and services will make a positive difference to their lives (Social Care Institute for Excellence, 2018). Medicines optimisation is a patient centred framework forming part of routine clinical practice supporting patients to achieve best possible outcomes from their medicines by providing evidence based choices and ensuring medicines are as safe as possible [89]. Deprescribing should form part of medicines optimisation and incorporate routine monitoring to help improve health outcomes as evidence suggests that people with intellectual disabilities have poorer health outcomes [90]. Embracing a multidisciplinary approach and co-producing robust effective deprescribing processes with all stakeholders at the individual and service level may contribute to improved health outcomes reducing exposure to adverse effects. Routine monitoring within the medicines optimisation framework must address not only the effectiveness and adverse effects of medicines, but also discontinuation effects and possible relapses facilitating prompt medication review. ## Implications for future research Studies addressing quality of life measures would address the absence of this in the literature. We recommend that future research should focus on studies addressing confounding factors that we have highlighted above, namely the lack of reporting of other co-administered interventions. These interventions include the prescribing of other classes psychotropic medicines which were not the subject of the deprescribing intervention, the prescribing of PRN medication, psychological, environmental, and behavioural interventions. The length of the follow up period may have a significant impact on whether deprescribing can be deemed as successful in sustaining long term reduced reliance on psychotropic medication and temporary discontinuation effects and the re-emergence of behaviours that challenges. We therefore suggest longer follow up periods are needed within future studies. We also recommend that future research should also consider the feasibility of deprescribing all classes of psychotropic medicines in routine clinical practice in a range of settings, and with children, adolescents, and adults. Further studies of stakeholder experiences to identify enablers of deprescribing and best practice in involving people with intellectual disabilities and their carers in decisions about their medicines would be welcome. Recruitment and sampling challenges will need to be addressed in future research ensuring balance and reporting of age, gender, ethnicity, and level of intellectual disability within both inpatient and community settings. Further research to increase the knowledge of discontinuation symptoms of the various psychotropic medicines would be helpful when planning psychotropic deprescribing. Further studies looking at enablers and barriers of the psychotropic deprescribing process, including addressing the impact of attitudes towards deprescribing of clinicians and carers on the success of deprescribing interventions, would be welcome as this could potentially influence initial decisions to implement deprescribing in individuals affecting outcomes. Finally, it would be helpful if future studies exploring psychotropic deprescribing consistently reported outcomes regarding complete discontinuation, or greater dosage reduction, rate of represcribing, improvement and deterioration of behaviour and emergence of adverse effects. ## Other information Protocol and registration: The review protocol was registered on 19th December 2019 with PROSPERO, the international prospective register of systematic reviews (registration number CRD CRD42019158079). https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019158079. Amended versions of the protocol were published on 7th July 2020, 17th September 2020, 30th September 2020 and 22nd March 2021. For further details regarding amendments please see link above. ## Supplementary Information Additional file 1. ## References 1. 1American Psychiatric Association D and Association AP. Diagnostic and statistical manual of mental disorders. 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--- title: Hyperglycemia induces PFKFB3 overexpression and promotes malignant phenotype of breast cancer through RAS/MAPK activation authors: - Xiao Cheng - Xiupeng Jia - Chunnian Wang - Shangyan Zhou - Jiayi Chen - Lei Chen - Jinping Chen journal: World Journal of Surgical Oncology year: 2023 pmcid: PMC10044395 doi: 10.1186/s12957-023-02990-2 license: CC BY 4.0 --- # Hyperglycemia induces PFKFB3 overexpression and promotes malignant phenotype of breast cancer through RAS/MAPK activation ## Abstract ### Background Breast cancer is the most common tumor in women worldwide. Diabetes mellitus is a global chronic metabolic disease with increasing incidence. Diabetes mellitus has been reported to positively regulate the development of many tumors. However, the specific mechanism of hyperglycemic environment regulating breast cancer remains unclear. PFKFB3 (6-phosphofructose-2-kinase/fructose-2, 6-bisphosphatase 3) is a key regulatory factor of the glycolysis process in diabetes mellitus, as well as a promoter of breast cancer. So, we want to explore the potential link between PFKFB3 and the poor prognosis of breast cancer patients with hyperglycemia in this study. ### Methods Cell culture was utilized to construct different-glucose breast cancer cell lines. Immunohistochemistry was adopted to analyze the protein level of PFKFB3 in benign breast tissues, invasive ductal carcinoma with diabetes and invasive ductal carcinoma without diabetes. The Kaplan–Meier plotter database and GEO database (GSE61304) was adopted to analyze the survival of breast cancer patients with different PFKFB3 expression. Western blot was adopted to analyze the protein level of PFKFB3, epithelial–mesenchymal transition (EMT)-related protein and extracellular regulated protein kinases (ERK) in breast cancer cells. Gene Set Cancer Analysis (GSCA) was utilized to investigate the potential downstream signaling pathways of PFKFB3. TargetScan and OncomiR were utilized to explore the potential mechanism of PFKFB3 overexpression by hyperglycemia. Transfections (including siRNAs and miRNA transfection premiers) was utilized to restrain or mimic the expression of the corresponding RNA. Cell functional assays (including cell counting, MTT, colony formation, wound-healing, and cell migration assays) were utilized to explore the proliferation and migration of breast cancer cells. ### Results In this study, we demonstrated that the expression of PFKFB3 in breast cancer complicated with hyperglycemia was higher than that in breast cancer with euglycemia through cell experiment in vitro and histological experiment. PFKFB3 overexpression decreased the survival period of breast cancer patients and was correlated with a number of clinicopathological parameters of breast cancer complicated with diabetes. PFKFB3 promoted the proliferation and migration of breast cancer in a hyperglycemic environment and might be regulated by miR-26. In addition, PFKFB3 stimulated epithelial-mesenchymal transition of breast cancer in a hyperglycemic environment. In terms of downstream mechanism exploration, we predicted and verified the cancer-promoting effect of PFKFB3 in breast cancer complicated with hyperglycemia through RAS/MAPK pathway. ### Conclusions In conclusion, PFKFB3 could be overexpressed by hyperglycemia and might be a potential therapeutic target for breast cancer complicated with diabetes. ## Introduction Cancer is the second leading cause of death in human and often leads to panic worldwide [1]. According to the Global Cancer Observatory (https://gco.iarc.fr/), nearly 19.29 million people were diagnosed with cancer for the first time and nearly 10 million died from it in 2020. Among them, the number of breast cancer cases were 2.26 million ($11.7\%$) and increased by $0.1\%$ compared with the same period of 2018. Breast cancer-related deaths accounted to 685,000 in 2020, ranking fifth ($6.9\%$) among all cancer-related deaths and increased by $0.3\%$ compared with the same period of 2018 [2]. Breast cancer is also the most common cause of cancer-related deaths in women, accounting for $15.5\%$ of female cancer-related deaths in 2020. As we all know, surgery combined with chemoradiotherapy is the primary treatment of breast cancer [3]. The emergence of hormone therapy and molecular targeted therapy in recent years is also an important milestone in the development of cancer therapy. However, proper targeting molecules remains urgently needed. Diabetes is a chronic metabolic disorder characterized by persistent hyperglycemia, among which type 2 diabetes accounts for about $95\%$ and is the most common metabolic disease at present. According to the International Diabetes Federation (https://diabetesatlas.org/), the number of diabetics reached 536.6 million worldwide in 2021, accounting for $9.8\%$ of the total population and 6.7 million of them died from diabetes. As the largest developing country, the proportion of diabetics in China reached $10.6\%$ of the nation’s total population in 2021, higher than the global average level. In addition, the number of people with diabetes is predicted to reach 783.2 million in 2045, accounting for $11.2\%$ of the population globally. Insulin resistance which prevents the body from using endogenous insulin effectively, has long been recognized as a cause of type 2 diabetes. A review published in Nature in 2019 by Gerald I. Shulman, a diabetes expert, suggested that the root cause of hyperglycemia is the increase of liver glycogen due to the abnormal white adipose tissue (WAT) degradation [4]. The relationship between cancer and diabetes, two major threats to human health, has been explored for decades. In 1924, German biochemist Otto Warburg first proposed the concept of Warburg effect, pointing out that tumor cells mainly meet the material needs of vigorous metabolism and rapid proliferation through glycolysis rather than tricarboxylic acid cycle, even though there is no lack of aerobic environment [5]. Statistical evidence indicated that hyperglycemia increased the incidence of multiple tumors and was associated with poor prognosis of cancer patients. 5-year overall survival was lower in tongue squamous cell carcinoma or stage IIIB-IV non-small cell lung cancer patients with diabetes than that in patients without diabetes [6, 7]. Studies have shown that hyperglycemic environment indirectly promoted the metastasis of tongue squamous cell carcinoma, pancreatic cancer, or gastric cancer by activating PKM2, HIF-1α, or ENO1, respectively [6, 8, 9]. A previous cancer prevention research based on one million Americans found that breast cancer patients with diabetes accounted for $16\%$ of all cancer patients with diabetes [10]. Besides, according to the latest report, $18\%$ of breast cancer patients also had diabetes [11]. Breast cancer patients with diabetes had a 24 to $44\%$ higher risk of death than those without diabetes [12, 13]. Therefore, it is urgent to find appropriate targeting molecules for breast cancer patients with diabetes. PFKFB3 (6-phosphofructose-2-kinase/fructose-2, 6-bisphosphatase 3), also known as PFK2, IPFK2 or iPFK-2, is a member of the fructose-2-kinase 6-phosphate/fructose-2, 6-diphosphatase family (PFKFB1-4) [14]. As a bifunctional protein, PFKFB3 can promote the synthesis and degradation of fructose-2, 6-bisphosphate (F2, 6BP, a key regulator of glycolysis) [15]. PFKFB3 is the most expressed PFKFB family gene in proliferating cells and cancer cells [16]. PFKFB3 is overexpressed in multiple solid tumors, including breast cancer, gastric cancer, colorectal cancer and pancreatic cancer [17–19]. Besides, the expression of PFKFB3 was reported to promote lymph node metastasis and increase the tumor node metastasis (TNM) stage [20]. The PFKFB3 promoter contains four response elements that bind to hypoxia-inducible factor (HIF-1α) [21], progesterone receptor (PR) [17], estrogen receptor (ER) [22] and serum response factor (SRF) [23] to facilitate gene transcription. In addition, insulin [19], inflammatory cytokines [24], transforming growth factor-β1 (TGF-β1) [25], lipopolysaccharide [26], and some other growth factors can also promote the expression of PFKFB3. The combination of PFKFB3 and PIM2 has been reported to increase the glucose level in breast cancer cells (glucose detection kit, Sigma, GAGO20) [27], so what role does PFKFB3 play in breast cancer patients with diabetes? What are the possible mechanisms? In our study, PFKFB3 expression was firstly confirmed to be enhanced by mediums with high glucose concentration and the knockdown of PFKFB3 could inhibit the malignant phenotype of breast cancer. Then, the mechanisms of PFKFB3 upregulation by high glucose concentration and PFKFB3 promoting the malignant phenotype of breast cancer were explored by online databases. Cell experiment in vitro and histological experiment were also adopted to verify the results based on online databases. In general, we deduced that hyperglycemia might upregulate PFKFB3 expression by inhibiting miR-26 to promote the malignant phenotype of breast cancer. ## Clinical samples Paraffin-embedded sections of 40 cases of benign breast tissue, 80 cases of breast invasive ductal carcinoma with diabetes and 80 cases of breast invasive ductal carcinoma without diabetes were obtained from the Department of Histopathology of Ningbo Clinical Pathology Diagnosis Center (Ningbo, Zhejiang, China). The samples selected were all from the patients with breast tissue resection from 2016.01 to 2021.06. Breast cancer patients with no other underlying diseases that might affect the results of our study were included. All patients with diabetes had been diagnosed and fasting blood glucose was higher than 7.0 mmol/L at admission. Invasive lobular carcinoma and other less common types of breast cancer were excluded. The clinicopathological parameters were obtained from Electronic Medical Records and the pathological results. This study was reviewed by the ethics committee of Ningbo Clinical Pathology Diagnosis Center and was conducted in full accordance with the Declaration of Helsinki (Code of Ethics of the World Medical Association). ## Immunohistochemistry (IHC) The expression level of PFKFB3 protein in the obtained 200 breast tissues was analyzed by immunohistochemistry using an UltraSensitive-SP kit (Maixin-Bio, Fuzhou, China). The operation was completely in accordance with the kit’s instructions. The specific schedule was as follows: primary antibody incubation time was 14–16 h (4 ℃); secondary antibody incubation time was 1 h (room temperature). Rabbit PFKFB3 polyclonal antibody (Cat No: 13763–1-AP) was purchased from Proteintech Group Inc. (Chicago, USA). The dilution concentration (1:200) recommended in the instructions for use has been verified by pre-test. The expression level of PFKFB3 was assessed by multiplying the staining intensity (0–3 points) and the percentage of nucleus-cytoplasmic staining cells (1: 0–$25\%$, 2: 26–$50\%$, 3: 51–$75\%$, 4: 76–$100\%$). A score of 0–6 was defined as low expression and a score of 7–12 as high expression [28]. The results of biopsy staining were synthesized after independent evaluation by chief physicians of the breast pathology subspecialty in the department of histopathology. ## Online databases GEO (Gene Expression Omnibus, GSE61304, https://www.ncbi.nlm.nih.gov/geo/) and Kaplan–Meier plotter database were utilized to analyze the survival of breast cancer patients with different PFKFB3 expression. The data set GSE61304 contained the clinical parameters (including age, tumor grade, TNM stage, and survival period) and gene expression profile of 58 breast cancer patients, so the survival analysis based on it were relatively reliable and representative. TargetScan (https://www.targetscan.org/vert_80/), OncomiR (http://www.oncomir.org), and miRcode (http://www.mircode.org/index.php) were utilized to explore the potential mechanism of PFKFB3 overexpression by hyperglycemia. As an integrated platform for genomic, pharmacogenomic, and immunogenomic gene set cancer analysis, Gene Set Cancer Analysis (GSCA, http://bioinfo.life.hust.edu.cn/GSCA/#/expression) could be utilized to conduct the gene set enrichment analysis (GSEA) of multiple cancers. Moreover, the potential downstream signaling pathways of PFKFB3 could be investigated by GSEA to better reveal the mechanism. ## Cell culture Human breast cancer cell lines BT474 and MCF-7 were purchased from ATCC (the American Type Culture Collection, Rockville, MD). The routine conditions for both cell cultures were RPMI 1640 glucose-free medium (Invitrogen, USA) containing $10\%$ fetal bovine serum and $1\%$ antibiotics (penicillin and streptomycin, FBS, Invitrogen, USA), cell incubator containing $5\%$ CO2 at 37 ℃. Special treatments were: the mediums with 5.5 mM, 15 mM, or 25 mM glucose were confected with sterile glucose solution to culture the two breast cancer cell lines continuously until the cells were passed for 8 times, so as to simulate different blood glucose environments in human body and construct different-glucose breast cancer cell lines (cells cultured with different glucose concentrations were hereinafter referred to as BT474/MCF-7–$\frac{5.5}{15}$/25 mM (mmol/L)). ## Western blot (WB) Protein levels of PFKFB3, E-cadherin, N-cadherin, Vimentin, total/phosphorylated-ERK $\frac{1}{2}$ (t/p-ERK$\frac{1}{2}$), and β-actin were tested by western blot as narrated before [29]. The specific schedule was as follows: transmembrane (90 min, 300 milliampere); blocking with milk (45 min); primary antibody incubation time was 2 h; secondary antibody (1:10,000) incubation time was 1 h. Mouse monoclonal antibody against β-actin (1:50,000, Cat No.: 66009–1-Ig), E-cadherin (1:4000, Cat No.: 60335–1-Ig), N-cadherin (1:4000, Cat No.: 66219–1-Ig), Vimentin (1:50,000, Cat No.: 60330–1-Ig) and rabbit polyclonal antibodies against PFKFB3 (1:1000, Cat No.: 13763–1-AP), total-ERK $\frac{1}{2}$ (t-ERK$\frac{1}{2}$, 1:1000, Cat No.: 11257–1-AP), and phosphorylated-ERK $\frac{1}{2}$ (p-ERK$\frac{1}{2}$, 1:3000, Cat No.: 28733–1-AP) (all from Proteintech Group, USA) were adopted. ## Transfection As previously narrated in our laboratory, the specific operations were as recommended by the guidelines [29]. Lip3000 (Invitrogen, USA) were selected as transfection reagents as recommended. All small interfering RNAs (siRNAs), including siPFKFB3-1, siPFKFB3-2, and siNC (negative control), and miRNA transfection primers (miR-26-mimics-NC, miR-26-mimic, miR-26-inhibitor-NC, and miR-26-inhibitor) were all designed and supplied by GenePharma (Shanghai, China). Specific siRNA or miRNA were siPFKFB3-1, 5′-GGAGACACAUGAUCCUUCATT-3′; siPFKFB3-2, 5′-GCAUCGUGUACUAC CUGAUTT-3′; siNC, 5′-UUCUCCGAACGUGUCACGUTT-3′; miR-26-mimic-NC, 5′-CAGUACUUUUGUGUAGUACAA-3′; miR-26-mimic: 5′-UUCAAGUAAUCC AGGAUAGGCU-3′; miR-26-inhibitor-NC, 5′-CAGUACUUUUGUGUAGUACAA -3′; miR-26-inhibitor, 5′-AGCCUAUCCUGGAUUACUUGA A-3′. ## Cell functional assays The cell counting assay, MTT assay and colony formation assay (all as narrated earlier [30]) were used to assess the cellular proliferation. The wound-healing assay and cell migration assay (as narrated earlier [30]) was used to assess the cellular metastasis. In the cell counting assay, 1 × 105 cells were taken as the initial value and planted into 6-well plate, the number of cells were counted and recorded at the same time for five consecutive days. In the MTT assay, 2000 cells (MCF-7) or 5000 cells (BT474) were taken as the initial value and planted into 96-well plate, the absorbance of cells at 570 nm (OD570nm) was monitored 72 h later. In the cell colony formation assay, 1500 MCF-7 cells or 2500 BT474 cells were taken as the initial value and planted into 6-well plate. Culture plates were collected and colony images were taken to count the number of cell colony containing more than 100 cells after 2 weeks. In the wound-healing assay, a 20-μl pipette suction was used to draw a straight line when the cells in the 6-well plate grew to $90\%$ density and then the shed cells were washed off with PBS. The micrographs of wounds were taken with an Olympus microscope (Olympus, Tokyo, Japan) at 0 h and 24 h respectively to compare cell migration in different groups. In the migration assay, 1 × 105 cells were taken as the initial number and mixed with medium (lack of FBS). Then the cellular mixture was added into the upper chambers, medium with $5\%$ FBS was added into the lower chambers meanwhile. Images of cells in the upper chambers were collected 24 h after dyed with $0.1\%$ crystal violet (A100528; Sangon Biotech) for 10 min. ## Statistical analysis All experiments were repeated for more than three times and the average was finally showed in this study. The survival analyses were conducted with Kaplan–Meier curve. The correlation analysis of PFKFB3 expression and clinical parameters of breast cancer patients were performed with Pearson’s chi-squared test in SPSS 26.0. Unpaired two-tailed t tests was utilized to process the experimental results. $P \leq 0.05$ was considered statistically significant. ## PFKFB3 was correlated with the prognosis of breast cancer (BC) patients and its expression could be enhanced by hyperglycemia The PFKFB3 expression level of benign breast tissues and invasive ductal carcinoma with/without diabetes were compared with IHC based on the multi-center cases we collected to ensure the reliability of the results. The representative images of PFKFB3 in benign breast tissues and breast invasive ductal carcinoma with/without diabetes were shown in Fig. 1A. The results showed that the expression level of PFKFB3 in non-diabetic invasive ductal carcinoma was higher than that in benign breast tissue ($P \leq 0.001$), while lower than that in diabetic invasive ductal carcinoma patients ($$P \leq 0.031$$) (Table 1). PFKFB3 expression was significantly upregulated in breast cancer tissues. Likewise, the association of PFKFB3 expression with clinicopathological parameters from breast cancer patients were analyzed with correlation analysis and the results showed that PFKFB3 expression was relevant to blood glucose level ($P \leq 0.001$), lymph node metastasis ($$P \leq 0.005$$), and tumor stage ($P \leq 0.001$) (Table 2). The Kaplan–Meier plotter database was utilized to study the effect of PFKFB3 expression level on the survival of breast cancer patients. According to Fig. 1B, high PFKFB3 expression was correlated with poor progression-free survival (PFS, HR = 1.28, $$P \leq 0.011$$) and overall survival (OS, HR = 1.15, $$P \leq 0.0074$$). The survival analysis was also conducted with GEO database (GSE61304) to verify the results based on Kaplan–Meier plotter database and consistent results were obtained (Fig. 1C, $$P \leq 0.002$$). The results in Fig. 1D showed that PFKFB3 expression level was evidently enhanced with the glucose concentration of mediums increased while the expression of housekeeping protein (β-actin) was similar, suggesting that high glucose environment promoted the expression of PFKFB3 in breast cancer. Fig. 1PFKFB3 could be enhanced by hyperglycemia and was correlated with the prognosis of BC patients. A Protein levels of PFKFB3 in benign breast tissues and invasive ductal carcinoma with or without diabetes were detected by IHC. The magnification of the photographs was 200. B The Kaplan–Meier plotter database and C GEO database (GSE61304) were utilized to compare the PFS and OS of breast cancer patients with different PFKFB3 expression levels. D The breast cancer cell lines (BT474 and MCF-7) were cultured in 1640 mediums with different concentrations of glucose (5.5 mM, 15 mM, or 25 mM) and WB was performed to evaluate PFKFB3 expression level. , $P \leq 0.05$; , $P \leq 0.01$; *, $P \leq 0.001$Table 1Expression of PFKFB3 in benign breast tissues and invasive ductal carcinoma (with and without diabetes)PFKFB3 expressionPχ2GroupnLow, n (%)High, n (%)Benign breast tissues4031 (77.5)9 (22.5)Invasive ductal carcinoma without diabetes8046 (57.5)34 (42.5)< 0.001a14.731Invasive ductal carcinoma with diabetes8022 (27.5)58 (72.5)0.031b4.639aBenign breast tissues and Invasive ductal carcinoma without diabetesbInvasive ductal carcinoma with and without diabetesTable 2Association of PFKFB3 expression with clinicopathological parameters from breast cancer patientsPFKFB3 expression(n (%))Pχ2ParameternLowHighAge (years)0.6180.249 ≤ 50834142 > 50773542Blood glucose (mmol/L)< 0.00125.664 ≤ 7.0805426 > 7.0802258Tumor size (cm)0.5010.452 ≤ 2633231 > 2974453Lymph node metastasis0.0058.035 No784632 Yes823052Grade0.9540.003 I–II1266066 III341618Stage< 0.00138.202 I–II926329 III–IV681355P < 0.05 was considered statistically significant ## PFKFB3 overexpression might activate epithelial–mesenchymal transition and RAS/MAPK pathways of breast cancer in a hyperglycemic environment Given the crucial role of PFKFB3 in breast cancer progression, GSEA (Gene Set Enrichment Analysis) was performed to further investigate the downstream signaling pathways. According to Fig. 2A, RAS/MAPK ($P \leq 0.001$) and EMT ($P \leq 0.001$) were the most significant common pathways of enrichment in breast cancer. Briefly, the results indicated that PFKFB3 expression level might be correlated with the activation of RAS/MAPK and EMT pathways in breast cancer. According to Fig. 2B, PFKFB3 knockdown decreased the expression of N-cadherin, Vimentin and p-ERK$\frac{1}{2}$, while increased the expression of E-cadherin when the fluctuation of t-ERK$\frac{1}{2}$ and β-actin was not obvious. In other words, PFKFB3 knockdown inhibited the EMT and RAS/MAPK pathway of breast cancer in a hyperglycemic environment. Fig. 2PFKFB3 might activate epithelial–mesenchymal transition and RAS/MAPK pathways of breast cancer in a hyperglycemic environment. A GSEA (Gene Set Enrichment Analysis) was performed to investigate the downstream signaling pathways. Pathway lists of PFKFB3 screened out by GSEA were shown on the left, the box plots of EMT (upper) and RAS/MAPK pathway (lower) were shown on the right. B Protein levels of PFKFB3, E-cadherin, N-cadherin, Vimentin, p-ERK$\frac{1}{2}$, and t-ERK$\frac{1}{2}$ in BT474-25 mM or MCF-7-25 mM cells after transfected with siPFKFB3-1, siPFKFB3-2 or siNC were detected using western blot. β-actin was detected as control. , $P \leq 0.05$; *, $P \leq 0.01$ ## PFKFB3 downregulation restrained the proliferation and migration of breast cancer in a hyperglycemic environment Besides, to investigate the role of PFKFB3 in the biological behaviors of breast cancer such as proliferation and migration in hyperglycemic environment, cell functional experiments were carried out. The results of cellular proliferation experiment (Fig. 3A: cell counting assay; Fig. 3B: MTT assay; Fig. 3C: colony formation assay) showed that the mitotic ability, cell viability and the capacity to form colonies were significantly decreased after PFKFB3 knockdown in breast cancer cells; the results of cellular migration experiment (Fig. 3D: wound-healing assay and Fig. 3E: migration assay) showed that the migration ability of breast cancer cells was significantly decreased after PFKFB3 knockdown. Briefly, PFKFB3 knockdown could remarkably suppress the proliferation and migration of breast cancer in a hyperglycemic environment. Fig. 3PFKFB3 promoted proliferation and migration of breast cancer cells in a hyperglycemic environment. BT474-25 mM and MCF-7-25 mM cells were transfected with siPFKFB3-1, siPFKFB3-2, or siNC. A The cell counting assay. B MTT assay. C The cell colony formation assay. D The wound-healing assay. E The migration assay ## Hyperglycemia might promote PFKFB3 expression by miR-26 downregulation in breast cancer TargetScan database was utilized to explore the potential mechanism of PFKFB3 overexpression by hyperglycemia. According to Fig. 4A, miR-26 was the most conserved microRNA that regulates PFKFB3. In other words, miR-26 was the most possible upstream regulatory factor of PFKFB3. In OncomiR database, we also found that the correlation between PFKFB3 and miR-26 is relatively high, ranking only second to miR-106 (Fig. 4B). In miRcode database, the consistent result was obtained that PFKFB3 and miR-26 have a high degree of combination conservatism in primates or mammals (Fig. 4C). According to Fig. 4D, PFKFB3 expression was significantly enhanced in breast cancer cells transfected with miR-26-inhibitor, while miR-26-mimic decreased PFKFB3 expression. Besides, we also found that miR-26-inhibitor could promote the epithelial–mesenchymal transition of breast cancer cells, while miR-26-mimic had the opposite effect. Based on the results above, we could deduce that PFKFB3 overexpression by hyperglycemia might be by the way of miR-26 downregulation in breast cancer. Fig. 4Hyperglycemia might promote PFKFB3 expression by miR-26 downregulation in breast cancer. A TargetScan database, B OncomiR database, and C miRcode database were utilized to predict that miR-26 was a reliable upstream microRNA of PFKFB3. D Protein levels of PFKFB3, E-cadherin, N-cadherin, and Vimentin in BT474-25 mM or MCF-7-25 mM cells after transfected with miR-26-mimic, miR-26-inhibitor, or corresponding-NC were detected by western blot. β-actin was detected as control. , $P \leq 0.05$; , $P \leq 0.01$ ## miR-26 downregulation accelerated the proliferation and migration of breast cancer in a hyperglycemic environment Cell functional experiment was conducted to better reveal the effect of miR-26 on breast cancer. In cell counting assay (Fig. 5A), MTT assay (Fig. 5B) and colony formation assay (Fig. 5C), the proliferation was significantly promoted by miR-26-inhibitor, while miR-26-mimic had the opposite effect. In wound-healing assay (Fig. 5D) and migration assay (Fig. 5E), miR-26-inhibitor enhanced the migration of BT474 and MCF-7 cultured in medium with 25 mM glucose, while miR-26-mimic had the opposite effect. Briefly, miR-26 inhibited the proliferation and migration of breast cancer cells in a hyperglycemic environment. Fig. 5miR-26 downregulation accelerated the proliferation and migration of breast cancer in a hyperglycemic environment. BT474-25 mM and MCF-7-25 mM cells were transfected with miR-26-mimic, miR-26-inhibitor, or corresponding-NC. A The cell counting assay. B MTT assay. C The cell colony formation assay. D The wound-healing assay. E The migration assay. , $P \leq 0.01$ ## Discussion With the alteration of human living standard and lifestyle, metabolic syndrome represented by hyperglycemia has gradually become a serious global health concern. According to statistics, about $10\%$ of the world’s population is diabetic. Besides, as a well-known global chronic killer, one person dies of diabetes every 5 s and the damage seems to be more severe in developing countries [31]. Could there be a link between this invisible damage and the more familiar visible damage of cancer? The hyperglycemic environment has been proved to promote the occurrence and development of gastric cancer, colorectal cancer, hepatocellular carcinoma, pancreatic cancer, and lung cancer through a variety of signaling pathways [8]. As the most common tumor in women, breast cancer have also been found to be adversely affected by hyperglycemia in occurrence and progression [13, 32, 33]. Metformin is the first-line drug for the treatment of diabetes that can effectively decrease the blood glucose level [34], what is more exciting is that it can enhance the therapeutic effect of cancer treatment [35]. This discovery plays a positive role in improving the prognosis of breast cancer patients with hyperglycemia, which also provides new sights for cancer treatment: inhibiting tumor biological behaviors by blocking or attenuating glycolysis activity. As we all know, the rate-limiting step in glycolysis determines the metabolic efficiency of carbohydrates. Fructose-6-phosphate is converted to fructose-1, 6-bisphosphate under the unidirectional catalysis of 6-phosphofructokinase-1 (PFK-1), which is irreversible and therefore an essential rate-limiting step in glycolysis. PFK-1 is therefore one key enzyme of glycolysis process [36]. Allosteric activators including AMP, ADP and fructose-2, 6-bisphosphate (FRU-2, 6-P2) can bind to PFK-1 to increase the activity of PFK-1, among which FRU-2, 6-P2 is the most effective one [37, 38]. Meanwhile, the protein encoded by PFKFB3 can promote the synthesis of FRU-2, 6-P2 to increase its concentration in the microenvironment, which indirectly enhances glycolysis. In ALK (anaplastic lymphoma kinase)-positive non-small cell lung cancer, PFKFB3 is a downstream molecule of ALK-STAT3 signaling pathway that positively regulates the glycolysis level and plays a carcinogenic role in tumor cells [39]. The PFKFB3/AKT/ERCC1 (Excision repair cross-complementation group 1) pathway has been reported to promote the progression of hepatocellular carcinoma by enhancing DNA repair in the process of glycolysis [40]. The Kaplan Meier plotter is a powerful database to explore the correlation between the expression of particular gene and survival in more than 30,000 samples from 21 tumor types including breast cancer. The results of survival analysis presented in this study are based on Kaplan–Meier plotter database with strong reliability. Our results suggested that PFKFB3 was overexpressed and promoted the proliferation as well as migration in breast cancer with diabetes. MicroRNA has become a novel research focus in recent years and miRNAs targeting PFKFB3 deserves further study. In this research, we found that miR-26 was the most probable upstream regulatory factor of PFKFB3 by comprehensive analysis of TargetScan and OncomiR online databases, which was further verified in MiRcode database. The tumor suppressive effect of miR-26/PFKFB3 has been confirmed in osteosarcoma, in which miR-26b inhibits the proliferation and metastasis of osteosarcoma cells and stimulates cell apoptosis by inducing PFKFB3 downregulation. The concentration of glycolysis-related molecules such as GLUT-1 also decreases correspondingly [41]. In addition, miR-26/PFKFB3 was also shown to play a similar role in gastric cancer patients with diabetes [42]. In our study, mediums with different concentration of glucose were utilized to confirm the induction of high glucose on PFKFB3 expression. Our results also indirectly indicated that high glucose upregulated PFKFB3 expression by miR-26 downregulation. To further explore the function of PFKFB3 in breast Cancer, GSEA was conducted to screen out several pathways with statistical significance in GSCA. Our results indicated that the promoting effect of PFKFB3 on epithelial-mesenchymal transformation in breast cancer is compelling and the RAS/MAPK is especially a statistically recognized possible pathway. MAPK is a well-known signaling pathway in cellular molecular biology that regulates cellular biological behaviors [43, 44]. Abnormally activated MAPK/ERK pathway have been found in a variety of tumors [45]. The MAPK/ERK pathway has been reported to negatively affect the prognosis of breast cancer and is associated with the adriamycin-resistance of breast cancer [46, 47]. As previously mentioned, metformin, a first-line drug for diabetes, was previously found to inhibit the development of breast cancer and improve the survival of breast cancer patients after immunotherapy [35]. Interestingly, while exploring the specific mechanism of metformin in decreasing blood glucose level and even cancer inhibition, some researchers found that MAPK signaling pathway could be inhibited by metformin and pancreatic aquaporin 7 (AQP7) was then reactivated to allow insulin secretion [48]. These results suggested that MAPK pathway may function in the regulation of glycolysis, which is corresponded with our results in this study. The mechanism of poor prognosis in breast cancer patients with hyperglycemia is complex. Hyperglycemic environment has been demonstrated to trigger the HIF1 pathway by upregulating the expression of HIF1-ɑ, which ultimately leads to anti-apoptotic cell response. Excessive secretion of insulin can stimulate the synthesis of insulin-like growth factor (IGF-1), which can promote cell mitosis and inhibit apoptosis [49]. In addition, insulin resistance leads to an increase in free estrogen, which has been linked to postmenopausal breast cancer, by inhibiting the production of sex hormone-binding proteins [50]. In this study, we proved the cancer-promoting effect of PFKFB3 in hyperglycemic breast cancer cells by regulating PFKFB3 expression starting from glycolysis pathway, but there are still some limitations: first, it might be inappropriate to simulate the hyperglycemic environment in the human body with hyperglucose mediums; second, the regulatory effects of PFKFB3 on RAS/MAPK pathway should be confirmed by co-immunoprecipitation assay; last, an hyperglycemic animal model should be established to further verify the results in vitro. ## Conclusion In conclusion, our study confirmed that PFKFB3 expression was correlated with the glucose level, lymph node metastasis, and tumor stage of breast cancer patients. 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--- title: 'The Association between Previous Antibiotic Consumption and SARS-CoV-2 Infection: A Population-Based Case-Control Study' authors: - Matan Dugot - Eugene Merzon - Shai Ashkenazi - Shlomo Vinker - Ilan Green - Avivit Golan-Cohen - Ariel Israel journal: Antibiotics year: 2023 pmcid: PMC10044412 doi: 10.3390/antibiotics12030587 license: CC BY 4.0 --- # The Association between Previous Antibiotic Consumption and SARS-CoV-2 Infection: A Population-Based Case-Control Study ## Abstract Background: The susceptibility to SARS-CoV-2 infection is complex and not yet fully elucidated, being related to many variables; these include human microbiome and immune status, which are both affected for a long period by antibiotic use. We therefore aimed to examine the association of previous antibiotic consumption and SARS-CoV-2 infection in a large-scale population-based study with control of known confounders. Methods: A matched case–control study was performed utilizing the electronic medical records of a large Health Maintenance Organization. Cases were subjects with confirmed SARS-CoV-2 infection ($$n = 31$$,260), matched individually (1:4 ratio) to controls without a positive SARS-CoV-2 test ($$n = 125$$,039). The possible association between previous antibiotic use and SARS-CoV-2 infection was determined by comparing antibiotic consumption in the previous 6 and 12 months between the cases and controls. For each antibiotic consumed we calculated the odds ratio (OR) for documented SARS-CoV-2 infection, $95\%$ confidence interval (CI), and p-value using univariate and multivariate analyses. Results: The association between previous antibiotic consumption and SARS-CoV-2 infection was complex and bi-directional. In the multivariate analysis, phenoxymethylpenicillin was associated with increased rate of SARS-CoV-2 infection (OR 1.110, $95\%$ CI: 1.036–1.191) while decreased rates were associated with previous consumption of trimethoprim-sulfonamides (OR 0.783, $95\%$ CI: 0.632–0.971) and azithromycin (OR 0.882, $95\%$ CI: 0.829–0.938). Fluroquinolones were associated with decreased rates (OR 0.923, $95\%$ CI: 0.861–0.989) only in the univariate analysis. Previous consumption of other antibiotics had no significant association with SARS-CoV-2 infection. Conclusions: Previous consumption of certain antibiotic agents has an independent significant association with increased or decreased rates of SARS-CoV-2 infection. Plausible mechanisms, that should be further elucidated, are mainly antibiotic effects on the human microbiome and immune modulation. ## 1.1. The Pandemic The coronavirus disease 2019 (COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread globally with enormous worldwide impact, and has therefore been declared a pandemic by the World Health Organization (WHO). As of 3 February 2022, the WHO has recorded more than 750 million confirmed cases, including 6.8 million deaths [1]. A recent study concluded that the true number of fatal cases was three times higher [2]. The presentation of SARS-CoV2 infection has an exceptional interindividual variability, ranging from asymptomatic infection in nearly half of the cases to a very severe course with the need of intensive care and leading to long-term complications and deaths [3,4]. ## 1.2. Risk Factors for SARS-CoV-2 Infection SARS-CoV-2 infection has been a major public health problem for about three years with a huge burden on society. It is therefore crucial to elucidate variables that are associated with the infection and its severity, in order to target prevention measures, such as vaccination, social isolation, and special attention to hygiene practices. Demographic factors, including older age, male gender, low socioeconomic status (SES), and ethnicity play a role on the rate and severity of the infections [5,6,7,8], as do certain underlying conditions, including smoking [9], obesity [10], vitamin D deficiency [11], and several underlying diseases [12,13,14,15,16,17]. ## 1.3. Human Microbiome and COVID-19 The human microbiome, defined as all the microorganisms that exist inside and on the surface of human body, lives in symbiosis with human systems and plays a key role in host metabolism, physiology, immunology, and even brain function. Imbalance, called dysbiosis, of the human respiratory and intestinal microbiome has been observed in patients with COVID-19 [18]. Examination of the respiratory microbiome of 507 humans, including patients hospitalized with COVID-19, non-COVID patients, and healthy controls, documented that COVID-19 patients had dysbiosis of their upper respiratory microbiome with reduced diversity that correlated with disease severity [19]. Intubated patients had specific lung microbiota with prominent staphylococci [19]. Likewise, several studies found dysbiosis of the intestinal microbiome in patients with COVID-19 with reduction in the Firmicutes phylum and other changes that were correlated with SARS-CoV-2 positivity [20] and disease severity [21,22]. Moreover, the “signature” of the human microbiome has been related to complications, prolonged disease, and mortality from COVID-19 [18,23,24]. ## 1.4. Antibiotic Treatment Antibiotics, a class of medications with diverse mechanisms of action and spectrum of antimicrobial activity, are very commonly used in clinical practice. In addition to their activity on the culprit pathogens, antibiotics have significant and prolonged effects on the human microbiome, mainly by reducing microbial diversity and having a selective pressure which promotes the growth of resistant bacteria [25,26,27]. The reduced diversity and imbalanced composition of the microbiota affects its important functional attributes to host metabolism and physiology, including the gut and systemic immunity [25,28]. Several antibiotic agents have also direct immune modulation activity [29,30,31,32,33,34]. These unintended effects of antibiotic treatment might affect the susceptibility to infectious diseases [27,35,36]. ## 1.5. Aim of Study We therefore aimed to examine the association between previous antibiotic treatment and SARS-CoV-2 infection in a large-scale population-based study, with control of known confounders. Elucidation by the class of antibiotics and the specific antibiotic agents was also performed to shed light on potential pathogenic mechanisms. This pioneering study might lead to new insights on the pathogenic mechanisms involved in SARS-CoV-2 infection. ## 2.1. Study Population This population-based case–control study was conducted at Leumit Health Services (LHS), a large Healthcare Maintenance Organization (HMO) in Israel serving 724,129 persons during the study period from 1 March 2020 to 31 December 2020. LHS has a comprehensive computerized database, which is continuously updated concerning the demographics, medical visits, laboratory tests, hospitalizations, and medication prescriptions of the registered subjects. Prescription records are available from 1998 and include confirmation of purchase by the individual patients. All LHS members have similar general health insurance and equal access to health services. Diagnoses are according to the International Classification of Diseases-9 or 10 (ICD-9 or ICD-10), depending on the date of diagnosis. The validity of the registry has been previously examined and confirmed as high [37]. ## 2.2. Definitions Cases were defined as individuals who had a positive RT-PCR test for SARS-CoV-2 during the period. According to the national criteria for SARS-CoV-2 examination, RT-PCR testing was performed by physician referral after exposure to a patient with confirmed SARS-CoV-2 infection or due to presenting symptoms suggesting COVID-19. Nasopharyngeal swabs were examined for SARS-CoV-2 by a real time RT-PCR assay with internal positive and negative controls, using the COBAS SARS-CoV-2 $\frac{6800}{8800}$ (Roche Pharmaceuticals, Basel, Switzerland). None of our participants had a SARS-CoV-2 infection before the beginning of the study period, nor had any of them been vaccinated against COVID-19 before or during the study period. Controls were defined as individuals without a positive SARS-CoV-2 test during the same study period, which were matched to the cases (see below). Levels of SES were defined according to the Israeli Central Bureau of Statistics classification to 20 levels. Previous antibiotic treatment was defined as prescription and purchase of an antibiotic agent by the individual during a defined period before the date of the SARS-CoV-2 RT-PCR test (cases) or the same index date (controls). ## 2.3. Study Design We conducted a case–control study. For each SARS-CoV-2-positive patient we selected four individuals without a positive SARS-CoV-2 test, matched carefully for age, gender, smoking, family status, ethnic sector, SES, body mass index (BMI), height, overweight category, smoking status, pulse, systolic and diastolic blood pressure, hypertension, and diabetes mellitus. The controls were also matched for selected laboratory results that might affect the rate of SARS-CoV-2 infection, including serum creatinine, estimated glomerular filtration rate [eGFR], and hemoglobin A1C (HbA1C). The strict matching of the control individuals to the cases by multiple relevant demographic, clinical and selected laboratory variables, using advanced tracking methodologies and advanced digital system, minimized the probability of confounding. We did not control for specific less common diseases that might affect susceptibility to SARS-CoV-2 infection, such as asthma, heart diseases, chronic obstructive pulmonary disease (COPD), inflammatory bowel disease (IBD), dementia, underlying malignancy, and attention deficit-hyperactivity disorder (ADHD). These were therefore entered to and examined in the multivariate model to determine the variables that were independently related to SARS-CoV-2 infection. The controls were assigned the same date as the index case. Exposure to antibiotic medications was obtained from each subject’s electronic record. All antibiotic agents purchased during the study period by the cases and controls were identified according to the ATC codes. To avoid the scenario that the antibiotics were prescribed for symptoms of SARS-CoV-2 infection, antibiotic use was examined until 15 or 30 days before the date of the SARS-CoV-2 PCR testing. We examined the possible association between previous antibiotic treatment and SARS-CoV-2 infection by comparing antibiotic consumption between the cases and control groups. ## 2.4. Statistical Analysis Statistical analyses were performed using R statistical software version 3.6 (R Foundation for statistical computing). Assumptions were two-sided, with a predefined α of <0.05. Socio-demographic characteristics between the groups of cases and controls were assessed using the Fisher exact test for categorical variables, and the two-tailed Wilcoxon Mann–Whitney U for continuous variables. The primary outcome was the probability of having SARS-CoV-2 infection, examined as related to antibiotic consumption. This was assessed using a logistic regression model to calculate the odds ratios [OR], the corresponding $95\%$ confidence interval [CI], and the significance. Specific diseases that were not matched for and showed different rates between the cases and control groups were entered into and examined with a multivariate analysis, together with previous antibiotic consumption. The Benjamini–Hochberg (BH) procedure was utilized to control the false discovery rate for multiple testing. The study protocol was approved by the Shamir Medical Center Review Board and the Research Committee of LHS (0129-20-LEU-31.5.2020). ## 3.1. Study Population The study population included two groups: 31,260 individuals who had a SARS-CoV-2 infection during the study period which was confirmed by a SARS-CoV-2 positive RT-PCR test and comprised the cases, and a 125,039-individual control group without a positive SARS-CoV-2 positive result during the same study period that were matched 4:1 with the control group. ## 3.2. Demographic Characteristics of the Study Groups Table 1 shows the demographic characteristics of the groups, documenting a very strict matching with no real demographic differences between the cases and control groups regarding the gender, age, family status, ethnicity, and SES. Details are presented in Table 1. ## 3.3. Clinical Characteristics of the Study Groups Table 2 reports the basic clinical variables of the groups. As can be seen, the matching was exact with no actual differences between the cases and control groups regarding the smoking status, BMI, categories of overweight, height, pulse, systolic and diastolic blood pressures, hypertension, and diabetes mellitus. The details are presented in Table 2. Specific diseases were not matched for; thus, those significantly affecting the susceptibility to SARS-CoV-2 infections were entered in a multivariate analysis to determine the independence of the association between antibiotic consumption and SARS-CoV-2 infection. ## 3.4. Laboratory Characteristics of the Study Groups Table 3 shows the basic laboratory results in the cases and control groups. These document the rigorous matching with no evident differences between the groups regarding the levels of serum creatinine, eGFR, and the various serum lipids. Details are presented in Table 3. ## 3.5. Previous Antibiotic Consumption and SARS-CoV-2 Infection Antibiotics were frequently used in individuals of both groups. The association between antibiotic consumption during several defined periods before the SARS-CoV-2 testing and SARS-CoV-2 infection was examined by comparing antibiotic consumption between the groups of cases and matched controls. Highly significant associations were documented, as some antibiotic agents were associated with decreased rates of SARS-CoV-2 infection, others with increased rates, and others without a significant association, as demonstrated in Table 4A–C. The significant associations were similar among the three periods of previous antibiotic consumption. Previous consumption of trimethoprim-sulfamethoxazole was associated consistently and significantly with the highest reduction (25–$50\%$) in the rates of SARS-CoV-2 infection. Fluoroquinolones (mainly ciprofloxacin) and azithromycin consumptions were associated with a milder decrease (8–$15\%$) in SARS-CoV-2 infection, which reached consistent statistical significance. The orally administered phenoxymethylpenicillin was the only antibiotic agent whose previous consumption was associated with a significant increased rate of about $10\%$ of SARS-CoV-2 infection. ## 3.6. Multivariate Analysis To examine whether the significant association found between certain antibiotic agents and SARS-CoV-2 infection were independent, they were examined in a multivariate model, together with underlying diseases that were not matched for and showed different rates between the cases and control groups. As can be seen in Table 5, three antibiotic agents were independently associated with SARS-CoV-2 infection. ## 4.1. New Findings and Their Discussion In this population-based large-scale study we documented a novel finding that previous consumption of several antibiotic agents was significantly associated with reduced or increased odds for SARS-CoV-2 infection. By establishing a control group strictly matched for demographic and clinical variables and by performing a multivariate analysis with underlying diseases that affect SARS-CoV-2 infection rate, the independent association of certain antibiotic agents with SARS-CoV-2 infection was proven. To the best of our knowledge, this is the first and most comprehensive study to examine this association. Our findings are in line with previous studies that demonstrated major and prolonged effects of antibiotic treatment on the human microbiome [25,27,35] and that the composition of the microbiome had a considerable impact on the rates of SARS-CoV-2 infection and the severity of COVID-19 [18,19,21,23]. In particular, as the respiratory tract is the entry site of SARS-CoV-2 and the major area of COVID-19, changes in the respiratory microbiome in particular had a major effect on the course of COVID-19 [19,37,38]. Specific “microbial signatures” of the respiratory tract have been linked to COVID-19 [39]. Our findings add an additional specific facet to these observations. These findings are within the general broader perspective of the relations of the microbiome and the rates of viral infections [40,41,42,43] and their severity [42]. A comprehensive review has shown that dysbiosis of the human microbiome is related to increased rates of viral infections and even suggested advanced manipulations to target gut microbiome [40]; the use of probiotics has also been suggested [43]. The decisive mechanisms involved and the gut–lung axis have been explored using a mouse model. Bifidobacterium pseudolongum NjM1-enriched gut microbiota of mice protected against influenza by acetate production and inflammasome-mediated signaling of interferon production [41]. A detailed study of the oropharyngeal metabolome in pediatric patients with or without influenza A virus pneumonia demonstrated significantly higher levels of sphingolipid and propanoate metabolites between patients with influenza pneumonia and healthy controls [44]. Due to the observational nature of this study, we defined our finding as an association, not necessarily a causality. The matched control group and the logistic regression were designed to control for variables associated with the risk of SARS-CoV-2 infection, but additional unknown variables may play a role. It is also possible, for example, that behavioral differences exist between the cases and control groups, for example, higher/lower adherence to hygiene measures or social distancing during the pandemic. However, the finding that some antibiotic agents were associated with decreased rates of SARS-CoV-2 infection while others with increased rates makes this possibility unlikely, although still conceivable. It seems very unlikely that the infectious disease for which antibiotics were prescribed affected the susceptibility to SARS-CoV-2 infection several weeks or months afterwards. It is feasible that the present findings will stimulate further studies, based mostly on prospective research, to determine the causality. The susceptibility to SARS-CoV-2 infection is complex and not yet fully elucidated, being likely attributed to a complex interaction between host and environmental risk factors [7,10,12,13,14,15,17,45]. We found that previous consumption of four antibiotic agents—phenoxymethylpenicillin, azithromycin, trimethoprim-sulfamethoxazole (TMP/SMX) and fluoroquinolones—had significant bi-directional associations with SARS-CoV-2 infection rates. It is conceivable that two main general mechanisms, and the interplay between them, are plausible explanations for the influence of previous antibiotic therapy on the susceptibility to SARS-CoV-2 infection and COVID-19. One major mechanism is probably the effects of antibiotic therapy on the human microbiome, which are significant and long-term and have a secondary impact on the immune response [19,25,27,35]. This is the most likely mechanism by which phenoxymethylpenicillin is associated with a significant and independent increased susceptibility to SARS-CoV-2 infection. This narrow-spectrum penicillin is commonly used and its bactericidal activity encompasses mainly Gram-positive bacteria, aerobic and anaerobic, that are the most common inhabitants of the human microbiome of the upper respiratory tract [19,39]. As the upper respiratory tract microbiome is an important gatekeeper of respiratory infections, phenoxymethylpenicillin-induced dysbiosis and reduced diversity of the local microbiome might increase the susceptibility to SARS-CoV-2 infection. This is in concert with previous observations that dysbiosis of the human respiratory microbiome increased the risk of SARS-CoV-2 infection and severe course of COVID-19 [19,37,38,39], and of other viral infections of the upper respiratory tract such as influenza [38,40]. The other antibiotic agents that were associated with changing rates of SARS-CoV-2 infections were allied with a significant and independent decrease in the infection. A plausible mechanism for such influence is the immune modulation effects exerted by certain antimicrobial agents [28,30,32,34], in addition to the changes in the immune response secondary to the antibiotic-induced altered microbiome. Azithromycin is well-known for its considerable effects on the immune system [29,30,46]. Its confirmed immunomodulatory effects include reduced production of pro-inflammatory cytokines such as interleukins-8 (IL-8), IL-6, tumor necrotic factor alpha (TNF-α) and matrix metalloproteinases. Azithromycin also modulates macrophage and T-helper functions, causes alterations in autophagy and reduces oxidative stress [29,30,46]. Because of its profound immunomodulatory effects, azithromycin has been proposed for the prevention and treatment of asthma and other inflammatory conditions [29,30]. Furthermore, previous studies also reported antiviral activities of azithromycin, probably by inhibiting the endosome acidification during viral replication and affecting the un-coating step of viral infection [29,47]. Because of these properties, and as inflammation plays a major role in the severity of COVID-19, azithromycin has been suggested as a treatment for this infection, alone or with hydroxychloroquine [29,46], but several studies did not document efficacy of azithromycin in patients with COVID-19 and it is currently not recommended as a treatment for the disease. We have found that previous consumption of azithromycin is associated with reduced odds of SARS-CoV-2 infection. While the effects of antibiotic therapy on the human microbiome is prolonged, the duration of its immunomodulatory effects is unclear. It should be noted that azithromycin has an excellent tissue penetration and it accumulates within tissues and cells, particularly macrophages, with tissue concentrations about 50-fold greater than plasma concentration [29,41]. Its biologic half-life is long, estimated at 35–40 h in humans administered a single dose of 500 mg [29,46]. TMP/SMX (called also cotrimoxazole) was also associated with a significant and independent reduced odd of SARS-CoV-2 infection. In addition to its antimicrobial effects on the human microbiome, TMP/SMX also blocks the stimulation of the formyl peptide receptors, which are expressed on human neutrophils and monocytes, and thus inhibits cytokine production and exerts anti-inflammatory properties [48]. Attempts were therefore made to use TMP/SMX in patients with COVID-19, with reduced severity and mortality in a retrospective analysis [48], case series [49], and in an interim analysis of a controlled study [50]. These effects of TMP/SMX might explain its association with reduced rates of SARS-CoV-2 infection. Fluoroquinolones were associated with a significantly decreased rate of SARS-CoV-2 infection in the univariate analysis but not in the multivariate model, which included diseases that are related to the risk of the infection. This probably implies that the fluoroquinolones were more often used in patients with underlying medical conditions. Multiple immune modulatory activities of fluroquinolones are well-documented, including a decrease in cytokine release and attenuation of the inflammatory response [31,32,33]. They also have anti-viral activities, including against SARS-CoV-2, by binding to its protease [51]. Because of these properties, fluoroquinolones have been proposed as adjuncts in the treatment of SARS-CoV-2-associated pneumonia [52]. Bronchial asthma was associated with a reduced rate of SARS-CoV-2 infection, as we have reported and discussed previously in a large-scale study that has focused on this condition [53]. ## 4.2. Strengths and Limitations The main strength of our study is its being large-scale, population-based and performed on real-world data, with the ability to collect comprehensive demographic and clinical information to build a stringently matched control group and to perform a multivariate analysis with potential confounders. The advanced digital systems enabled us to determine individual antibiotic consumption prior to the SARS-CoV-2 infection by utilizing cutting-edge tracking methodologies. The big sample size was sufficient to reach statistical significance while controlling for confounders. The study has several limitations. The major one relates to its observational and retrospective methodology, with the possibility that some unrecognized biases could have affected the results. We therefore used the term association, not causality, to describe our findings. Our analysis of antibiotic consumption was based on the acquisition of the antibiotic agent from the pharmacy, but we could not confirm the actual antibiotic use by the individual, and the duration of use. As our population was largely Caucasian, our findings may not be automatically generalized to other ethnic populations. Because the study is based on a single HMO, although nation-wide, a selection bias is possible. ## 5. Conclusions This population-based comprehensive study documented a new finding that previous consumption of several antibiotic agents was significantly and independently associated with reduced or increased rates of SARS-CoV-2 infection. Two plausible mechanisms, and the interplay between them, are the prolonged antibiotic effects on the human microbiome and on the immune response. Further studies, preferably prospective, are needed to replicate the finding in other populations, determine the causality, and elucidate the mechanisms involved. ## References 1. 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--- title: 'Gut Microbiota Composition Can Predict Colonization by Multidrug-Resistant Bacteria in SARS-CoV-2 Patients in Intensive Care Unit: A Pilot Study' authors: - Jorge García-García - Patricia Diez-Echave - María Eugenia Yuste - Natalia Chueca - Federico García - Jose Cabeza-Barrera - Emilio Fernández-Varón - Julio Gálvez - Manuel Colmenero - Maria Elena Rodríguez-Cabezas - Alba Rodríguez-Nogales - Rocío Morón journal: Antibiotics year: 2023 pmcid: PMC10044413 doi: 10.3390/antibiotics12030498 license: CC BY 4.0 --- # Gut Microbiota Composition Can Predict Colonization by Multidrug-Resistant Bacteria in SARS-CoV-2 Patients in Intensive Care Unit: A Pilot Study ## Abstract The SARS-CoV-2 infection has increased the number of patients entering Intensive Care Unit (ICU) facilities and antibiotic treatments. Concurrently, the multi-drug resistant bacteria (MDRB) colonization index has risen. Considering that most of these bacteria are derived from gut microbiota, the study of its composition is essential. Additionally, SARS-CoV-2 infection may promote gut dysbiosis, suggesting an effect on microbiota composition. This pilot study aims to determine bacteria biomarkers to predict MDRB colonization risk in SARS-CoV-2 patients in ICUs. Seventeen adult patients with an ICU stay >48 h and who tested positive for SARS-CoV-2 infection were enrolled in this study. Patients were assigned to two groups according to routine MDRB colonization surveillance: non-colonized and colonized. Stool samples were collected when entering ICUs, and microbiota composition was determined through Next Generation Sequencing techniques. Gut microbiota from colonized patients presented significantly lower bacterial diversity compared with non-colonized patients ($p \leq 0.05$). Microbiota in colonized subjects showed higher abundance of Anaerococcus, Dialister and Peptoniphilus, while higher levels of Enterococcus, Ochrobactrum and Staphylococcus were found in non-colonized ones. Moreover, LEfSe analysis suggests an initial detection of Dialister propionicifaciens as a biomarker of MDRB colonization risk. This pilot study shows that gut microbiota profile can become a predictor biomarker for MDRB colonization in SARS-CoV-2 patients. ## 1. Introduction The new coronavirus, denominated “Severe Acute Respiratory Syndrome Coronavirus 2” (SARS-CoV-2), has been the genesis of the 2019 Coronavirus Disease (COVID-19), a pandemic menace to health worldwide [1]. According to the World Health Organization (WHO), the ongoing outbreak has spread to more than 215 countries, with more than 600 million officially reported cases and over 6 million confirmed deaths [2]. Considering the symptoms, disease severity has been classified into mild, moderate, severe, and critical [3]. Even though most of the COVID-19 cases are mild to moderate, it has been estimated that 10–$15\%$ of them progress to severe, and 15–$20\%$ of these to critical, even requiring treatment in intensive care units (ICU) [4]. Critically ill patients may show evidence of respiratory failure, septic shock, and/or multiple organ dysfunction [2,3]. SARS-CoV-2 has also aggravated another pandemic, the antimicrobial resistance (AMR), also known as a «hidden pandemic». AMR has been acknowledged as one of the most serious hazards for global health, economic, and social wellness, being estimated to be the direct cause of over 1 million deaths in 2019 [5,6]. Furthermore, different studies have revealed an association between both pandemics [7,8]. Thus, it has been reported that an increment of critically ill patients have been prescribed empirical antimicrobial therapy that may not be appropriate and could also contribute to the global increase of resistant infections [9,10]. Moreover, it is well described that admission to ICUs, including in the cases of SARS-CoV-2 patients, is associated with an intestinal dysbiosis including a remarkable reduction of phylogenetic diversity in the gut microbiota [11]. This imbalanced environment permits pathogenic microorganisms such as Clostridium difficile, Pseudomonas aeruginosa, Candida species, vancomycin-resistant enterococci (VRE), and other multidrug-resistant bacteria (MDRB) to colonize the intestine [11,12,13,14,15]. Patients colonized by MDRB have worse prognoses and increased risk for septic shock, organ failure, prolonged ICU stays, and mortality [16]. The management of the gut dysbiosis in critically ill patients is nowadays considered a key factor to increase survival rates in ICU patients, and for this reason it has, nowadays, become a trending topic in clinical research. Consequently, different strategies have been implemented to prevent gut dysbiosis and therefore MDRB colonization in critically ill patients, e.g., selective digestive tract decontamination (SDD), use of probiotics, prebiotics, combinations of both (synbiotics), faecal microbiota transplantation (FMT), and microbiome-modulating agents that regulate the metabolism of microbiota [17,18,19]. Even though it has been reported that gut dysbiosis is a common feature in SARS-CoV-2-positive patients treated in the ICU [20], their specific microbiome composition could determine the risk of developing MDRB colonization. Accordingly, in-depth examination of gut microbiota composition in both groups of patients, colonized or not, could help to establish key valuable prognostic targets, thus allowing a better management of these patients. Thus, the present pilot study aims to evaluate whether the initial gut microbiota compositions of SARS-CoV-2 positive patients in ICUs could have an impact on the colonization and establishment of MDRB. ## 2.1. Study Cohort Description A total number of 17 patients with SARS-CoV-2 infection admitted to the ICU were enrolled for this pilot study (Table 1). Among them, 8 patients suffered MDRB colonization during their stay inside ICU facilities. According to the establishment and development of MDRB colonization, we divided the total number of patients into two groups, colonized and non-colonized patients. When the screening for MDRB colonization was performed, $75\%$ of the patients were colonized by carbapenemase-producing bacteria, while the rest were colonized by Escherichia coli or non-fermenter bacteria (Table 2). Moreover, one patient colonized by carbapenemase-producing bacteria presented two colonizing bacteria: E. cloacae and S. marcenses. The general clinical characteristics of non-colonized and colonized groups are shown in Table 1. Most patients enrolled were male ($65\%$), being similarly distributed in the two groups even though most of the women were included in the non-colonized group. Regarding the comorbidities in both groups, hypertension was the most common comorbidity ($41\%$), followed by obesity ($30\%$). However, separately, the colonized group showed a higher incidence rate of comorbidities than non-colonized patients, the duration of stay in the ICU being similar in both groups. Additionally, two severity indexes at hospitalization were calculated, SOFA and APACHE. The results revealed no statistical differences for these indexes between both groups of patients, although a trend to show higher values in the APACHE index was observed when colonized patients were considered. On the other hand, the biochemical parameters evaluated revealed that the colonized group displayed a significant increase in LDH levels compared to non-colonized patients ($p \leq 0.05$). In addition, MDRB colonization also presented a tendency for promoting higher percentage of fever ($63\%$) or antibiotic treatment ($75\%$) at the time of ICU access, and mortality once inside ICU ($38\%$), when compared with non-colonized patients. ## 2.2. Lower Gut Microbiota Diversity Is Associated with a High Risk of Colonization by MDRB in Critical SARS-CoV-2 Patients To evaluate the impact of the initial gut microbiota composition in the development of MDRB colonization in SARS-CoV-2 patients, alpha and beta diversity analyses were firstly performed (Figure 1). Alpha diversity evaluation revealed that colonized patients showed a significantly lower bacterial diversity. In fact, the MDRB colonization was associated with a significant decrease in the observed species and an increment in the Inv. Simpson index (evenness) ($p \leq 0.01$ and $p \leq 0.05$ respectively), whereas no significant modification was observed when Shannon index (richness) was considered (Figure 1A). Additionally, principal coordinate analysis (PCoA) plots for Bray–Curtis and both unweighted and weighted UniFrac distances were employed to establish the beta diversity. Accordingly, PCoA plots indicated that the two groups were very closely clustered, with no significant separation (Figure 1B–D). Therefore, the beta diversity analysis indicated that both groups of patients displayed similar structures of microbial communities. ## 2.3. SARS-CoV-2 Associated Shifts in the Initial Gut Microbiota Are Related to MDRB Colonization in ICU Facilities The bacterial microbiota composition in critical SARS-CoV-2 patients at the time of entering the ICU showed that the most abundant phyla in all samples were Bacteroidota, Bacillota, and Pseudomonadota. Of note, the abundance of these phyla did not show significant differences between non-colonized and colonized patients (Figure 2A). However, when the composition at genus level was evaluated, significant differences were found between both groups. Among the most notable results, non-colonized patients showed greater counts of Enterococcus, Ochrobactrum, and Staphylococcus (Figure 2B), whereas a significant increase in the abundance of Anaerococcus, Dialister, and Peptoniphilus was observed in those patients suffering colonization. Interestingly, the analysis of those genera with an abundance lower than $1\%$ also revealed significant differences between non-colonized and colonized patients (Figure 2C). Thus, the non-colonized group presented a higher abundance of Clostridium and Escherichia, while the colonized group had more ph2 and Streptococcus, although a trend was only obtained with the latter ($$p \leq 0.085$$). ## 2.4. Identification of Specific Bacteria as Possible Markers for Predicting MDRB Colonization in SARS-CoV-2 Patients in ICUs Once it had been established that MDRB colonization in SARS-CoV-2 patients during their stay in the ICU facilities could be associated with several modifications in the relative abundance of some bacteria genera, we further tried to identify some other taxa that could help to predict colonization at the time of entering the ICU. With this aim, ASVs from all samples were analysed to determine core taxa along with the specific bacteria of each group (Figure 3A). Venn diagram analysis showed that both groups of study share 72 core taxa, while only one bacterium (Jonquetella antrophi) was identified as specific for the colonized group. Additionally, differential abundance taxa between non-colonized and colonized samples were identified by employing a volcano plot. An increase in the abundance of some bacteria, including Prevotella timonensis, Anaerococcus vaginalis, and Dialister propionicifaciens seems to be associated with MDRB colonization, whereas there was a significant decrease in the abundance of Ochrobactrum, Corynebacterium, and Prevotella bivia in comparison with non-colonized patients (Figure 3B). Finally, ASVs with significantly higher relative abundances in each sample from both groups were determined based on a linear discriminant analysis Effect Size algorithm (LEfSe). Compared by groups, non-colonized patients showed a significantly greater abundance of Varibaculum cambriense, Citrobacter europaeus, and Proteus bacterium_R49 members as biomarker taxa, while in the colonized group Dialister propionicifaciens was shown as an MDRB biomarker (Figure 3C). ## 2.5. Correlation between Initial Gut Microbiota Composition and Clinical Variables of Non-Colonized and Colonized Patients The previous results have shown the existence of differences among initial gut microbiota compositions of patients that would end in being colonized or not. To improve the study, we have analysed the correlation between the gut microbiota composition and the clinical variables of the patients. Gut microbiota from non-colonized patients presented only three strong positive correlations (Porphyromonas, Bacteroides uniformis, and Prevotella timonensis) (Figure 4A). In general, for most of the taxa, there was a higher presence of negative correlation with the studied clinical variables. Specifically, the presence of Corynebacterium in non-colonized patients was strongly related to lower mortality. On the other hand, the results revealed that stool microbiota from colonized patients was more positively correlated with the clinical variables such as comorbidities, APACHE index, and LDH value (Figure 4B). ## 3. Discussion Infections caused by MDRB colonization constitute a crucial challenge in patients treated in the ICU [21,22,23]. This problem has been aggravated by the COVID-19 pandemic, in part due to the increase in the number of antibiotic prescriptions in ICUs, being, some of them, unnecessary or not fully justified [24]. In fact, although the data about MDRB colonization in COVID-19 are scant, the empirical treatment with broad spectrum antibiotic therapy and biologics that target and inhibit cytokines, such as IL-1 and IL-6, could raise the risk of MDRB colonization in these patients [25,26]. Our results suggest that demographic factors are independently associated with ICU-acquired MDRB in SASR-CoV-2 patients. The severity index at hospitalization and days in the ICU were also identified as possible independent risk factors for ICU-acquired MDRB in SARS-Cov-2 subjects. Ceccarelli G. et al. stated that risk factors such as comorbidities, mechanical ventilation, and a longer stay in the ICU were responsible for carbapemenase producers infections [27]. Even though a higher number of patients should be evaluated, our results agree with their observations as acquisition of MDRB in the ICU was lightly associated with different comorbidities, symptoms at hospitalization, biochemistry parameters, and mortality rate. These patients also presented a higher necessity for antibiotic treatment. This fact could increase the chances of colonization, as the association between antibiotic treatment and MDRB colonization is well described [28]. Moreover, recent studies have shown that antibiotic treatment in SARS-CoV-2 positive patients was positively correlated to MDRB colonization [29,30]. On the other hand, previous studies have reported that the severity index at hospitalization and days in the ICU are dependent factors for MDRB colonization in SARS-CoV-2 patients compared with virus -free patients [31]. However, the relationship between these factors and SARS-Cov-2 ICU-acquired MDRB has not yet been addressed. Therefore, these findings suggest that the severity and duration of stay at the ICU may be associated with a more frequent use of antibiotics in colonized SARS-CoV-2 patients. Additionally, the dependent factors to colonized status in these patients, including different comorbidities, biochemistry parameters, and mortality rate, might be explained by the reported gut dysbiosis and the inflammatory status in patients with these comorbidities. This previous gut dysbiosis and/or the inflammation condition in these patients may be aggravated with the SARS-CoV-2 infection and facilitate the MDRB colonization. This adds new information for a possible explanation of the MDRB colonization in these COVID patients, however, different studies support our rationale, since it has been described that intestinal dysbiosis in obese patients constitutes a risk factor for SARS-CoV-2 infection, which could lead to severe symptoms with higher MDRB colonization [32]. Furthermore, it is well known that SARS-CoV-2, along with many other virus infections, modifies gut microbiota composition [33]. In this sense, recent reports in SARS-CoV-2 patients have identified an important gut dysbiosis, with enrichment of opportunistic bacterial and fungal pathogens, and depletion of beneficial symbionts that are positively and inversely correlated with SARS-CoV-2 severity, respectively [34,35]. Consequently, it is plausible to hypothesize that gut microbiota can impact MDRB colonization in SARS-CoV-2 patients. Hence, in this pilot study, the results suggest that initial gut microbiota compositions of positive SARS-CoV-2 patients admitted to ICUs might be one of the factors for the development of MDRB colonization during their stay in these facilities. In fact, the SARS-CoV-2 patients colonized by MDRB presented a significant reduction of bacterial diversity. Taking everything in consideration, and even with the limitations of this pilot study, the results suggest that these SARS-CoV-2 patients with a higher gut dysbiosis can be key in the MDRB colonization, together with other key factors such as treatment with broad-spectrum antibiotics. In this scenario, a greater dysbiosis can lead to colonization by pathogenic organisms as well as to an increase the antibiotic resistance gene burden, and subsequent antimicrobial resistance pathogen invasion. Interestingly, previous studies have described a similar impact on positive SARS-CoV-2 patients. Zuo et al. described that SARS-CoV-2 infection increases the presence of opportunistic pathogens, although the virus presence diminished the microbial diversity compared to healthy patients [36]. Closely related to the above, the microbiota composition of colonized SARS-CoV-2 critically ill patients in the ICU facilities used was characterized by an increase of Anaerococcus, Dialister, and Peptoniphilus. Commonly, these bacteria have been recognized as opportunistic pathogens [37], suggesting that the MDRB colonization could derive from the presence and/or the negative effects of these microorganisms in virus-infected patients. Conversely, non-colonized patients presented a higher abundance of Enterococcus, Ochrobactrum, and Staphylococcus. Enterococci are lactic acid bacteria comprising both pathogenic and gut symbionts. In fact, many studies have indicated that these microorganisms can produce antimicrobial compounds including bacteriocins [38,39]. Interestingly, our results are in line with the previous studies. Concerning Ochrobactrum, they are gram-negative, non-fermenting bacteria classically related to infections both in patients undergoing treatments and subjects outside of a clinical centre with different diseases [40]. However, they have been of low virulence and different studies have indicated that they may be innocuous [41]. Similarly, staphylococci have been widely known as pathogenic microorganisms, being S. aureus species, a classical penicillin resistant bacterium. However, it has been also published that pre-colonization with S. aureus affects the Pseudomonas aeruginosa implantation by competitive inhibition [42]. Thus, its presence can be considered as a defensive mechanism. Additionally, other genus taxa such as Clostridium, Escherichia, and Corynebacterium have also been increased in non-colonized SARS-CoV-2 patients. Interestingly, these bacteria have been associated with beneficial effects in human health. For example, some Clostridium species have been used as probiotics [43] and as enhancers of the mechanism of action of Lactobacillus [44], while for some species of Corynebacterium it has been reported that they can produce specific antimicrobial molecules [45]. Briefly, our findings suggest that the presence of these genera of bacteria would point to a high possibility of MDRB colonization and would facilitate a better prognosis for ICU patients infected by SARS-CoV-2. Aiming to find a precise biomarker for predicting MDRB colonization in SARS-CoV-2 patients, we have determined the existence of the intestinal bacterial species at the time of hospitalization in the ICU of the patients with COVID recruited in our pilot study. Remarkably, the results of gut microbiota of the 8 colonized patients revealed that Jonquetella atrophy was exclusively present in the faecal microbiota of colonized patients. This bacterium belongs to the Synergistetes phylum, which surprisingly is not usually abundant in the normal microbiota [46]. Nonetheless, this bacterium has been identified in pathologic conditions, and it has been also reported as an opportunistic pathogen [47,48]. The comparison between both groups of study showed that colonized patients presented a significantly higher abundance of Dialister propionicifaciens, Anaerococcus vaginalis, and Prevotella timonensis. These bacteria could be involved in the MDRB colonization in these patients because they have been reported to have antibiotic resistance (Dialister propionicifaciens) and have been implicated in inflammatory processes as well as SARS-CoV-2 infections (Prevotella timonensis and Anaerococcus vaginalis) [49,50,51]. Moreover, the determination of a predictive intestinal microbiome biomarker by LDA plot revealed an enrichment of Citrobacter europaeus and Proteus bacterium, belonging to Enterobacterales order, in non-colonized patients. Specifically, it has been published that Enterobacteriaceae members such as Citrobacter produce colicins that inhibit the growth of other species [52]. This fact can explain that a higher abundance of these microorganisms in SARS-CoV-2 patients constitutes a protective factor to MDRB colonization. On the contrary, in colonized patients Dialister propionicifaciens could be crucial in MDRB colonization. As mentioned above, this bacterium is recognized as an opportunistic pathogen [53] and, moreover, it has been positively correlated with SARS-CoV-2 infection [54,55]. However, its impact on MDRB colonization in these patients remains unknown. For the first time, our results point to this bacterium as a possible and novel predictor MDRB biomarker in SARS-CoV-2 infection. Interestingly, previous metagenomic studies have identified this family as genomes harbouring antibiotic resistance genes [56,57]. Taken together, it has been revealed that determining a predictive biomarker for MDRB in these patients requires a multivariable approach. Therefore, a correlation study between gut microbiota and clinical variables was performed (Figure 4). Our possible non-colonization predictors did not appear as they were not the most abundant species of these patients. However, other mentioned taxa such as Prevotella bivia, Ochrobactrum, and Corynebacterium seemed to be associated with a better prognosis, suggesting again its implication at the time of avoiding MDRB colonization (Figure 4A). On the contrary, a higher abundance of Dialister, Methylobacterium, and Porphyromononas was strongly positively correlated with the development of the MDRB colonization in SARS-CoV-2 patients (Figure 4B). Specifically, the possible biomarker for colonization (Dialister propionicifaciens) presented a strong correlation with the presence of comorbidities. As other studies have suggested, an increase in the number of comorbidities at the time of entering the ICU can increase the risk of MDRB colonization [58]. Thus, in the present study, it seems that suffering from certain conditions increases the abundance of Dialister propiniocifaciens and, as a result, raises the chances of being colonized. The findings of this study must be interpreted considering some limitations. Firstly, the number of samples is limited, and although the prevalence of patients colonised by MDRB in our study is high ($50\%$) compared to approximately $5\%$ of patients colonised by other causes, according to our bibliography and our internal data, the limited number of patients means that the results obtained need to be corroborated by a larger study. Another limitation is that it is a single-centre study. Furthermore, it must be taken into account that the patients have received antibiotic treatment, which is one of the most important factors in the production of gut dysbiosis in patients admitted to the ICU. ## 4.1. Patient Population The study was carried out in the ICU of San Cecilio University Hospital of Granada (Southern Spain) which contains up to 20 individual rooms. Patients aged ≥18 years with an ICU stay >48 h between March and May 2021 and who tested positive for SARS-CoV-2 infection were recruited. All patients were directly admitted to the ICU except for two, which were hospitalized prior to entering the ICU. During the period of study, not all patients could be recruited. In fact, only the first two patients from whom informed consent was obtained each week were recruited. Consequently, the participation rate among all COVID patients admitted to the ICU during the study period was $38.6\%$. ## 4.2. Ethics Statement The protocol of this study was approved by the Clinical Research Ethics Committee of Granada (CEIC) (ID of the approval 1133-N-20). All patients gave their consent before being included in the study. ## 4.3. Study Design This is a cross-sectional pilot study. At the time of entering the ICU, rectal swabs were collected for gut microbiota analysis and for standard surveillance for aerobic Gram-negative bacteria colonization. Colonization detection was carried out by culturing rectal swabs on selective chromogenic media CHROMID® CARBA SMART and CHROMID® ESBL plates (bioMérieux, Marcy-l’Étoile, France). All suspected Gram-negative colonies were analysed by Matrix-Assisted Laser Desorption/Ionization Time-Of-Flight mass spectrometry (MALDI-TOF MS) (Bruker Daltonics, Bremen, Germany) for species identification. Antibiotic resistance phenotypes were determined using the Microscan Walkaway 96 plus system (Beckman Coulter International S.A., Nyon, Switzerland) and observing the guidelines of The European Committee on Antimicrobial Susceptibility Testing (EUCAST) (https://www.eucast.org/eucastguidancedocuments (accessed on 1 July 2022)). Depending on MDRB colonization results, patients were classified as non-colonized or colonized. As for SARS-CoV-2 infection, main clinical data was registered from these patients: (i) age and gender; (ii) days since symptoms appeared, days in the ICU, and symptoms upon ICU entrance; (iii) biochemical parameters; (iv) clinical severity determined through sequential organ failure assessment (SOFA) and Acute Physiology and Chronic Health disease Classification System II (APACHE II) as well as comorbidities; and (v) care therapies and outcomes. ## 4.4. SARS-CoV-2 Diagnosis Microbiological diagnosis of SARS-CoV-2 infection was accomplished by detection of SARS-CoV-2 RNA in respiratory samples (oropharyngeal-nasopharyngeal swab, bronchoalveolar lavage, or broncoaspirate), as earlier reported [59]. In brief, total DNA/RNA was extracted from samples by TANBead (Maelstorm 9600, Guadalajara, Spain), and SARS-CoV-2 expression was detected by DIRECT SARS-COV-2 REALTIME PCR KIT with double target: specific for COVID-19 (N gene) and other SARS-related coronaviruses (E gene) regions (Vircell SL, Granada, Spain). ## 4.5. Microbial DNA Extraction, Library Preparation, and Next Generation Sequencing Faecal DNA was isolated according to the method reported by Rodríguez-Nogales et al. [ 60]. Total DNA was amplified utilizing primers targeting regions flanking the variable regions 4 through 5 of the bacterial 16 S rRNA gene (V4–5), gel purified, and examined using multiplexing on the Illumina MiSeq machine (Illumina Inc., San Diego, CA, USA). Amplified products were validated visually by running a high-throughput Invitrogen 96-well-E-gel (Thermo Fisher Scientific, Waltham, MA, USA). Then, PCR reactions from the same samples were pooled in one plate, subsequently cleaned, and normalized with the high-throughput Invitrogen SequalPrep 96-well Plate kit. Lastly, the samples were pooled into a library to be fluorometrically measured prior to sequencing. Next-Generation Sequencing (NGS) techniques were performed to sequence the samples using an Illumina MiSeq machine. Raw data for each sample was employed for further analysis of microbiome composition. ## 4.6. Bioinformatics and Statistical Analysis Bioinformatic analysis of gut microbiota samples was carried out using QIIME2 pipeline (open access, Northern Arizona University, Flagstaff, AZ, USA) [61]. Demultiplexed sequences were loaded into the program and quality control was performed by trimming and filtering, depending on the quality scores of the sequences [62]. Then, denoising was performed by employing DADA2 and amplicons sequence variants (ASVs) were obtained. Taxonomic assignment was calculated against the SILVA reference database [63] and the feature table was filtered to discard both Archaea and Eukaryota features. Statistical analysis of microbiota data was performed in R. QIIME2 objects were loaded into R to carry out the statistical analysis [64]. The phyloseq package was employed to determine alpha and beta diversity as well as relative abundance. Statistical differences for alpha diversity and relative abundance were analysed using the t-student test when samples followed a normal distribution. When samples did not follow this assumption, a Wilcoxon test was performed. Normality was checked by using the Shapiro–Wilk test included in the Nortest package of R. On the other hand, beta diversity differences were calculated with a Permutational Multivariate Analysis of Variance (PERMANOVA) by employing the Adonis function from the Vegan package. Eulerr and MicroViz packages were employed to make the Venn diagram analysis and to represent both heatmaps and correlation plots respectively. Values from correlation plots were calculated with the Pearson coefficient. Finally, the DESeq2 package (version “4.2”) was used to identify differences in taxa expression levels while the microbial package was used to assess possible biomarkers through linear discriminant analysis (LDA) effect size (LEfSe) with an LDA score of 3. For clinical variables, data was represented as mean ± SD when it followed a normal distribution. In contrast, for non-parametric distributions, median and interquartile range were displayed. For categorical variables, percentages were used. ## 5. Conclusions The present pilot study provides valuable information on changes in the gut microbiota in critical patients with the SARS-CoV-2 infection developing MDRB colonization. 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--- title: Integrative Analysis of Blood Transcriptomics and Metabolomics Reveals Molecular Regulation of Backfat Thickness in Qinchuan Cattle authors: - Hengwei Yu - Sayed Haidar Abbas Raza - Yueting Pan - Gong Cheng - Chugang Mei - Linsen Zan journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044415 doi: 10.3390/ani13061060 license: CC BY 4.0 --- # Integrative Analysis of Blood Transcriptomics and Metabolomics Reveals Molecular Regulation of Backfat Thickness in Qinchuan Cattle ## Abstract ### Simple Summary Subcutaneous fat deposition in cattle has become the focus of breeders’ attention because excessive deposition is not conducive to efficient feed conversion. In the present study, based on the results of blood transcriptome sequencing and the detection of metabolites, bioinformatic analysis was used to explore the differential genes and metabolites associated with the subcutaneous fat depot phenotype of beef cattle. In conclusion, the functional genes SMPD3 and CERS1, as well as the metabolite sphingosine 1-phosphate, were identified as an important metabolite and candidate genes to account for the differences in phenotype. These differential genes and the metabolite are thought to have an important reference value for effective breeding to improve beef performance. ### Abstract A crucial goal of reducing backfat thickness (BFT) is to indirectly improve feed conversion efficiency. This phenotype has been reported in certain papers; however, the molecular mechanism has yet to be fully revealed. Two extreme BFT groups, consisting of four Qinchuan cattle, were chosen for this study. We performed metabolite and transcriptome analyses of blood from cattle with a high BFT (H-BFT with average = 1.19) and from those with a low BFT (L-BFT with average = 0.39). In total, 1106 differentially expressed genes (DEGs) and 86 differentially expressed metabolites (DEMs) were identified in the extreme trait. In addition, serum ceramide was strongly correlated with BFT and could be used as a potential biomarker. Moreover, the most notable finding was that the functional genes (SMPD3 and CERS1) and metabolite (sphingosine 1-phosphate (S1P)) were filtered out and significantly enriched in the processes related to the sphingolipid metabolism. This investigation contributed to a better understanding of the subcutaneous fat depots in cattle. In general, our results indicated that the sphingolipid metabolism, involving major metabolites (serum ceramide and S1P) and key genes (SMPD3 and CERS1), could regulate BFT through blood circulation. ## 1. Introduction In many countries, beef is regarded as an edible meat of high quality. It has been dependably proven that breeding plays a decisive role in the improvement of meat quality for domestic livestock [1]. Generally, consumers will pay higher prices for better meat quality grades, which are determined by the longissimus thoracis area, rib thickness, cold left-side weight, and subcutaneous fat thickness [2]. Backfat thickness (BFT) has been a practical indirect predictor of whole-body fat content and can be measured by ultrasound on live animals without requiring slaughter [3]. This process has received much attention because BFT reduction can indirectly improve feed conversion efficiency [4]. Fat deposition in different parts of the animal body have variations in terms of the preference for metabolite utilization, such as propionate and glucose for intramuscular fat and acetate for subcutaneous fat [5]. Recently, an increasing number of studies conducted by multi-omics association analysis aimed to explore the potential molecular mechanisms involved in the melanogenesis pathway [6], fat formation [7], meat quality [6,8], meat discoloration [7], and in intramuscular fat [9]. Moreover, genome-wide testing of the gene expression in human peripheral blood cells revealed that approximately $80\%$ of the genes expressed in the 9 key tissues are also expressed in blood cells [10]. Therefore, it is useful to clarify the relationship between blood metabolome and transcriptome analyses and phenotypes. For example, Samantha [11] showed that subcutaneous rib fat showed a negative correlation with dimethyl sulfone and a negative tendency with acetate and isobutyrate for blood metabolomes in Wagyu-crossbred steers. Another example is that certain genes that are strongly expressed in uterine tissue are also highly expressed in blood [12]. Cattle with a high ultimate pH showed higher levels of plasma cortisol, glucose, lactic acid, creatine kinase, and lactate dehydrogenase than cattle with a low ultimate pH during bloodletting [13]. These studies support a clearer elucidation of phenotypic differences through blood metabolomic and transcriptomic analyses. China has the largest middle-income population and has become the world’s largest consumer of meat [14]. Qinchuan cattle, an important Chinese indigenous cattle breed, are named after the Guanzhong Plain in Shaanxi Province, and are characterized by good meat quality [15]. Consumers are known to recognize Qinchuan cattle as having delicious meat and are openly fond of it. Excessive subcutaneous fat deposition, however, greatly reduces the feed conversion efficiency and growth yield. Furthermore, the molecular mechanism of BFT in Qinchuan cattle remains unclear. Therefore, we hypothesized that metabolite types, concentrations, and gene expression in the blood would be related to the deposition of subcutaneous fat in beef cattle. The current study used mRNA and metabolite sequencing to identify the differences in blood transcriptomes and metabolomes between cattle with a differing backfat thickness. Independently, a number of differentially expressed genes and metabolites were found in both high- and low-BFT individuals. Furthermore, a joint analysis of transcriptome and metabolome approaches was utilized to provide insights into the identification of biomarkers for BFT, as well as to understand the relationship of the traits for BFT, blood metabolite concentrations, and mRNA. The revelation of the molecular mechanism of subcutaneous fat deposition in Qinchuan cattle and the reduction in BFT was found to have significant value for the development and utilization of excellent Chinese local-breed cattle resources, which are required to improve feed conversion rates. ## 2.1. Animals and Phenotypes The cattle included in the trial belonged to breeds of the Shaanxi province, northwest China. In this study, a total of 117 female cattle was used from the same conservation experiment farm of the National Beef Cattle Improvement Center (Xianyang, China). All cows were raised under the same feeding and management conditions, with free access to water. Cattle were measured by an ultrasonic device. The animal sampling and management protocol was implemented by the Technical Specification for Determination of Beef Cattle Production Performance (NY/T2660-2014) of the Ministry of Agriculture of the People’s Republic of China. The tested cattle were tied up and smeared with vegetable oil on ribs 12–13 on the left. They were vertically pressed with an ultrasonic probe at approximately 5 cm below the side of the cattle’s spine to be measured until a clear image appeared on the ultrasonic scanner host; the BFT was then calculated. Finally, two extreme groups of four cattle were selected through this process. ## 2.2. Blood Sample Collection and Preparation Whole blood was collected from the jugular vein of eight cattle into a 5 mL EDTA anticoagulant tube and mixed, separately, upside down. The sample was then quickly transferred to a laboratory (within 2 h) where 300 mL of the blood was taken in by a pipette and then administered into a 2 mL centrifuge tube. Next, 700 µL of trizol was added. The samples were shaken and mixed for 30–60 s, cultured at room temperature for 5 min, then quickly frozen in liquid nitrogen and stored at −80 °C to extract the RNA. Similarly, 1 mL blood was inhaled into a 2 mL centrifuge tube and centrifuged at 4000× g concentration. Then, 200 μg of supernatant was loaded into centrifuge tube, rapidly frozen in liquid nitrogen, and stored at −80 °C to extract the metabolites. ## 2.3. RNA Extraction, Sequencing, and Transcriptome Data Analysis Total RNA extraction, RNA integrity detection, library construction, and RNA-seq were performed by Biomarker Technologies Co., Ltd. (Beijing, China). BMKCloud (www.biocloud.net) was used to analyze the RNA-seq data. Stringent quality control was applied to the raw data and low-quality reads were removed with the following standards: reads with adapters; low quality reads (including reads with an N ratio greater than $10\%$); and the number of bases with Q ≤ 10 quality value (which was greater than $50\%$ of whole reads). In clean reads, Q30 (the proportion of bases with Phred quality values greater than 30 to total bases) was greater than $93.12\%$. Qualified reads were aligned against the bovine reference genome (https://bovinegenome.elsiklab.missouri.edu/downloads/ARS-UCD1.2, accessed on 13 October 2022) using the HISAT2 software package (http://www.ccb.jhu.edu/software/hisat2, accessed on 13 October 2022). Fragments per kilobase of transcript per million fragments mapped (FPKM) was used to quantify the level of gene expression or transcript [16]. Gene expression analysis was performed using the DEseq2 package [17]. The differentially expressed genes (DEGs) were defined on the basis of \|log2(fold change)\| ≥ 1 and FDR < 0.05. Lastly, gene ontology (GO) enrichment analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the clusterProfiler package and the topGO R package. ## 2.4. Metabolites Extraction For the BFT groups (four L-BFT blood serums and four H-BFT blood serums), the frozen blood serum samples stored at −80 °C were defrosted first. Metabolites extraction adhered to the following process: (a) 100 μL of a sample was weighed, adding 500 μL of the extraction solution containing the internal standard (the volume ratio of methanol to acetonitrile = 1:1; internal standard concentration 20 mg/L), and the sample was then vortexed and mixed for 30 s; (b) ultrasound was conducted for 10 min (ice water bath); (c) the sample was left to stand at −20 °C for one hour; (d) 4000× g of the sample was then centrifuged at 4 °C for 15 min; (e) the sample had 500 μL of the supernatant carefully removed into an EP tube; (f) the extract was then dried in a vacuum concentrator; (g) 160 μL of the extract solution was added (acetonitrile to water volume ratio: 1:1) to the dried metabolites to reconstitute; (h) this was vortexed for 30 s, then sonicated in an ice water bath for 10 min; (i) the sample was centrifuged at 4 °C and 4000× g for 15 min; and (g) 120 μL of the supernatant was carefully removed into a 2 mL injection bottle, and 10 μL of each sample was mixed into a quality-controlled sample and then subjected to LC-MS/MS analysis. ## 2.5. LC-MS/MS Analysis Based on the online METLIN database of Progenesis QI software and the identification library built by Biomark, Progenesis QI processed the raw data detected by MassLynx V4.2, performed peak extraction, peak comparison, and other operations, as well as theoretical fragment identification and quality analysis (the deviations were all within 100 ppm). First, a follow-up analysis was performed after normalizing raw peak area information with the total peak area. Principal component analysis and Spearman correlation analysis were used to estimate the repeatability of samples within a group and quantitative control samples. Subsequently, taxonomic and pathway information on the identified compounds were searched by the KEGG, HMDB, and Lipid mass spectrometry databases. Then, the difference multiplier and comparison data were calculated based on the grouping information, and the significance value of the difference of each compound was calculated by a t-test. OPLS-DA modeling was performed using the R language package ropls; furthermore, 200 permutation tests were performed to verify the reliability of the model. The VIP value of the model was calculated using multiple cross-validations. Differential metabolites were screened by combining the fold difference, p value, and VIP value of the OPLS-DA model. Screening criteria were \|log2(fold change)\| ≥ 1, p-value ≤ 0.05, and VIP ≥ 1. Finally, the differential metabolites of the KEGG pathway enrichment significance were calculated using the hypergeometric distribution test. ## 2.6. Joint Analysis of the Transcriptomic and Metabolomic Data To better explain transcriptional regulatory mechanisms in metabolic pathways, the correlations between all genes and metabolites were calculated based on the Pearson correlation. Then, the 9-quadrant graph was drawn based on the correlation coefficient (CC, \|CC\| > 0.80) and the p-value of the correlation (CCP < 0.05). A bubble map was drawn using the KEGG pathway, which was enriched by the significantly correlated combinations. Therefore, to visually reflect the differences in expression patterns of significantly different genes and significantly different metabolites, the heatmap R package was used for hierarchical clustering analysis. ## 2.7. Statistical Analyses To test for the statistical differences for BFT in the two extreme groups, an unpaired t-test was used in the SPSS program. The level of significance was set at p-value < 0.05. ## 3.1. Animal Phenotypic Divergence for BFT In this study, a total of 117 Qinchuan cattle with an average age of 36 months (SD = 7.71 month) was selected for the measurement of backfat thickness. According to BFT records, two groups were selected with a low or high backfat thickness (four H-BFT individuals and four L-BFT individuals, respectively). The average BFT of the cattle was 0.71 cm (SD = 0.29 cm), ranging from 0.27 to 1.55 cm. As shown in Figure 1A, the histogram demonstrates a statistically significant difference between the groups’ H-BFT (with average_BFT = 1.19, SD = 0.21, p-value < 0.05) and L-BFT (with average_BFT = 0.39, SD = 0.10, p-value < 0.05). ## 3.2. DEGs and Transcriptome Analysis To compare the difference in blood mRNA between the H-BFT and L-BFT groups, next-generation sequencing was used. After removing the adaptors and low-quality reads, more than $93.95\%$ of the Q30 was in each sample (Supplementary File S3: Table S1). Additionally, 91.78–$93.64\%$ of the clean reads were mapped to the bovine reference genome (Supplementary File S4: Table S2). As shown in Figure 1B, the boxplot shows that the distribution of gene expression levels was evenly distributed in each sample. Furthermore, it was also relatively centralized between the different samples. Interestingly, the correlation heatmap presented an obvious intergroup difference and high intragroup similarity (Figure 1C). Additionally, the PCA presented that the two BFT groups were clearly separated by the first principal component (Figure 1D). The volcano plot demonstrated that 1106 DEGs were identified in the BFT with 700 up-regulated genes and 406 down-regulated genes in the H group when compared with the L group (Figure 1E). The details of the DEGs are shown in Supplementary File S5: Table S3. Intriguingly, the significantly enriched genes, CRABP2 and ZFP57, were mainly expressed in the H-BFT group (Figure 1H,I). Then, the GO annotation analysis showed that the DEGs were divided into 58 subcategories, including 23 biological process (BP) terms, 17 cellular component (CC) terms, and 18 molecular function (MF) terms. It was found that $53.89\%$ (596 out of 1106) of the genes were related to the cellular process category for the BP category. In total, $58.05\%$ of the genes (642 out of 1106) were annotated in regard to the cellular parts for the CC category. Furthermore, $49.82\%$ of the genes (551 out of 1106) were located in the binding fraction for the MF category (Figure 1F and Supplementary File S6: Table S4). Furthermore, the enrichment analysis of the BP showed that the enriched terms were primarily involved in the granzyme-mediated apoptosis signaling pathway, megakaryocyte differentiation, neutrophil activation, the positive regulation of the ERK1 and ERK2 cascade, the arachidonic acid metabolic process, the long-chain fatty acid metabolic process, and the positive regulation of the establishment of protein localization to the telomeres (Supplementary File S1: Supplementary Figure S1A). The enrichment analysis of MF showed that the top enriched terms were mostly involved in GTP binding, GTPase activity, oxygen transporter activity, and 2′-5′-oligoadenylate synthetase activity (Supplementary File S1: Supplementary Figure S1B). Finally, KEGG pathway analysis indicated that the DEGs were involved in numerous signaling pathways, such as the p53 signaling pathway; the glycine, serine, and threonine metabolism pathways; the pentose phosphate pathway; glutathione metabolism pathway; and ribosome pathway (Figure 1G and Supplementary File S7: Table S5). Overall, these DEGs were involved in lipid metabolism, regulating systemic fat storage and utilization. ## 3.3. DEMs and Metabolome Analysis To evaluate the diversity of the blood metabolite composition in Qinchuan cattle, LC-MS/MS analysis was conducted with the L- and the H-BFT cattle groups. Collectively, a total of 3679 metabolites (1524 for negative ion mode and 2155 for positive ion mode) was identified (Supplementary File S8: Table S6). The PCA and PLS-DA score plots indicated that the quality-control samples were clustered together (Figure 2A,B). Based on the metabolome databases of KEGG, HMDB, and Lipidmaps, all metabolites were qualitatively analyzed. In total, 792 metabolites were identified in the KEGG database, including 85 amino acid metabolites, 64 biosyntheses of the other secondary metabolites, 42 carbohydrate metabolites, 8 glycan biosyntheses and metabolites, and 85 lipid metabolites (Supplementary File S2: Supplementary Figure S2A). In total, we identified 2709 metabolites in the HMDB database, including 906 lipids and lipid-like molecules, 475 organic acids and derivatives, 397 organic heterocyclic compounds, and 131 phenylpropanoids and polyketides (Supplementary File S2: Supplementary Figure S2B). We were able to identify a number of metabolites in total: 403 metabolites were identified in the database of the lipid maps, which included 208 fatty acyls, 9 glycerolipids, 79 glycerophospholipids, 22 polyketides, 20 prenollipids, 12 sphingolipids, and 53 sterol lipids (Supplementary File S2: Supplementary Figure S2C). These were used in the analysis. In addition, the criteria for the DEMs to be significant were \|log2(fold change)\| ≥ 1, VIP value ≥ 1 (variable importance in projection), and p-value ≤ 0.05. The volcano map displayed the DEMs related to the BFT (Figure 2C). A heatmap was created depicting the alignment of the metabolite levels that changed significantly in agreement consistently with the sample group (Figure 2D). In total, 25 up-regulated metabolites in the H-BFT group, including ceramide, beta-glucuronide, monacolin L acid, cornoside, vanilloyl glucose, etc., were higher than those found in the L-BFT. In contrast, 61 down-regulated metabolites in the H-BFT group, including biotin sulfone, phosphonic acid, propylene glycol stearate, dehydromakisterone, etc., were lower than those in the L-BFT (Supplementary File S9: Table S7). According to the KEGG annotation and enrichment results, the 86 DEM-relative metabolics were annotated into 14 pathways, such as sphingolipid metabolism, fatty acid biosynthesis, glutathione metabolism, the sphingolipid signaling pathway, biotin metabolism, the apelin signaling pathway, and steroid hormone biosynthesis (Figure 2E and Supplementary File S10: Table S8). In summary, these results indicated that the ceramide in the serum was closely related to backfat thickness and thus can be used as a potential biomarker. ## 3.4. Joint Analysis of the Transcriptome and Metabolome To reveal the candidate genes involved in the BFT, we combined the analysis of the blood metabolome and transcriptome. The Pearson correlation coefficient (CC) between all genes and metabolites was calculated and screened according to \|CC\| > 0.80 and the p-value < 0.05 (Figure 3A and Supplementary File S11: Table S9). A hierarchical cluster analysis was used to visualize the differences in expression patterns of the DEGs and DEMs (Figure 3B). Among the genes, we presented those genes that have been reported in lipid metabolism as trait-related candidate genes (APCDD1, ENHO, FBP1, FADS6, and KCTD15). Further, we annotated the KEGG pathway with the important DEGs and with their strongly correlated DEMs (Figure 3C and Supplementary File S12: Table S10). It is worth noting that sphingosine 1-phosphate (S1P) was associated with several KEGG pathways, such as the phospholipase D signaling pathway, calcium signaling pathway, apelin signaling pathway, sphingolipid signaling pathway, and the sphingolipid metabolism. Moreover, S1P was significantly positively correlated with the expressions of GABARAPL1, CXCL8, VDAC3, IL18, S1PR1, and ARPC3, while cases were observed to the contrary for TMIGD3, SMPD3, PLCB2, CAMK1, and CERS1 (\|PCC\| > 0.80) (Supplementary File S13: Table S11). Surprisingly, we observed that the sphingolipid metabolic pathway clustered two DEGs (SMPD3 and CERS1), both of which were remarkably up-regulated in the H-BFT group when compared with the L-BFT group. Together these results provided important insights into the blood transcriptomics and metabolomics in Qinchuan cattle for BFT. ## 4. Discussion As an important indicator of meat quality, BFT is an essential guiding indicator of significance for breeding. A test hypothesis of this study was that the concentrations of blood metabolites and transcriptomes were correlated with the BFT traits of beef cattle. In regard to transcription, one interesting finding was that the candidate genes CRABP2 and ZFP57 were mainly expressed in the H-BFT group but not in the L group. CRABP2 was the highest expression in the adipose tissue when compared with the different tissues of pigs [18]. In a high-fat diet mouse model, CRABP2 can activate the RA/RAR pathway in adipocytes in order to inhibit the adipocyte differentiation [19]. Furthermore, ZFP57 recognized its methylated site and played a pivotal role in the establishment of genomic imprints [20]. Moreover, it combines with its methylation site to maintain allele-specific gene repression [21]. Additionally, the mRNA expression of ZFP57 in human adipose tissue was influenced by the genome-wide DNA methylation quantitative trait locus [22]. Moreover, its hypomethylation and mutations were associated with transient neonatal diabetes [23]. In addition, the DEGs and up-regulated and down-regulated KEGG pathways might be associated with fat metabolism and adipogenesis in beef cattle. Furthermore, CRABP2 and ZFP57 might be critical candidate genes related to BFT. In our study, the blood metabolites may be potentially related to BFT. The ceramide and beta-glucuronide found in the H-BFT group were dramatically higher than those in the L-BFT group, whereas biotin sulfone and phosphonic acid were found to be in a negative correlation with BFT. These results are consistent with a previous study, which demonstrated that subcutaneous rib fat showed a negative correlation with dimethyl sulfone and a negative tendency with acetate and isobutyrate [11]. In metabolic syndrome, obesity, and type 2 diabetes (T2D), ceramide and S1P played an important role [24]. Additionally, ceramide/S1P metabolism and signaling were associated with adipose tissue dysfunction in the presence of excess dietary energy intake [25]. The importance of this metabolome cannot be overemphasized as it implies that it can be used for the early identification of cattle with a high propensity for BFT, thereby suggesting that the selection of cattle with a low BFT propensity for fattening may improve feed utilization. In contrast, further research is needed to investigate the potential biomarkers regarding this or for its metabolite application. Moreover, we focused on the integrative analysis of the transcriptomics and metabolomics for BFT. We identified several related genes, including APCDD1, ENHO, FBP1, FADS6, and KCTD15. The adenomatosis polyposis coli down-regulated 1 (APCDD1), a key regulator of adipogenic differentiation, was identified as an inhibitor of Wnt signaling. In addition, it positively regulated the adipogenic differentiation of subcutaneous adipose tissue during diet-induced obesity for the mouse [26]. Similar to this, APCDD1 was significantly up-regulated in H-BFT individuals in our study. Adropin is a secreted protein that is encoded by an energy-homeostasis-associated gene (ENHO) that controls glucose and lipid homeostasis, as well as preventing the hepatic steatosis and hyperinsulinemia that are associated with obesity [27]. The glycoisomerase fructose-1,6-bisphosphatase 1 (FBP1) inhibits certain biological pathways, including cell proliferation, glycolysis, and pentose phosphate in a catalytic-activity-independent manner [28]. Polyunsaturated fatty acids perform critical physiological roles in human health, and Δ6 fatty acid desaturase (FADS6) is an enzyme that is essential in the polyunsaturated fatty acids production pathway [29]. Potassium-channel-tetramerization-domain-containing protein 15 (KCTD15), a member of the K+-channel-tetramerization-domain family, is an obesity-linked protein in humans and is implicated in the crucial physio-pathological processes that are involved in food uptake [30]. There is no doubt that these genes have direct or indirect effects on fat formation and degradation. In the current study, the most obvious finding to emerge from the analysis is that the expression levels of SMPD3 and CERS1 were higher in the H group than in the L group. This is in addition to the same trend being applicable for ceramide, while S1P showed the opposite trend. Ceramide is a core metabolite of the sphingolipid metabolic pathway, and it can promote insulin resistance [31]. Ceramide is bound to protein phosphatase 2A (PP2A) and mediates AKT dephosphorylation, thereby inhibiting glucose transport. In addition, PP2A is activated by ceramide [32,33,34]. Increased liver fat deposition in obese women is accompanied by high levels of ceramide in subcutaneous adipose tissue and in increased macrophage infiltration, thus suggesting that ceramide may also promote insulin resistance and chronic inflammation in adipose tissue [35]. S1P is a bioactive lipid and its level in cells is controlled by two factors: the sphingosine content and the catalytic activity of S1P metabolizing enzymes (such as sphingosine kinase (SK), S1P phosphatase, and S1P lyase [24]). Ceramide, sphingomyelin, and S1P were able to interconvert with each other. S1P, known as a “sphingolipid-variable blocker”, promoted the proliferation/survival pathway, while ceramide induced apoptosis/aging [36,37]. Additionally, research suggested that reducing intracellular ceramide levels may be an effective therapeutic strategy for the treatment of T2D and obesity [38]. It is of interest that the interaction between adipose tissue and the circulatory system is essential to maintain the homeostasis of systemic metabolism. The lipids secreted by adipose tissue may induce elevated levels of ceramide in the circulation of obese individuals. Furthermore, in metabolically active tissues, such as the liver and skeletal muscle, non-esterified fatty acid (NEFA) from dysregulated adipose tissue can be used for sphingolipid biosynthesis and thus can induce ceramide synthesis [39]. Furthermore, ceramides in the circulation may originate from adipose tissue [40], and the S1P secreted by adipose tissue in obese patients can also promote systemic inflammation. Therefore, we suggest that this is also a similar regulation process for BFT in cattle; specifically, CERS1 and SMPD3 were overexpressed by certain signaling molecules. CERS1 increases the rate of ceramide synthesis, thereby resulting in a decrease in sphingosine; in turn, the level of S1P was also decreased. Ceramide circulates through the blood system and enters into adipocytes by endocytosis. Then, the ceramide affects the PP2A to dephosphorylate AKT, thus preventing AKT from being transported to the cell membrane and also inhibiting the function of certain genes in the downstream pathways of AKT—which ultimately inhibits the adipocyte differentiation and promotes adipocyte apoptosis (Figure 4). ## 5. Conclusions The findings of this study suggest that serum ceramide is closely related to backfat thickness and can be used as a potential biomarker. 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--- title: Mycobacterium avium Subspecies paratuberculosis in Asymptomatic Zoo Herbivores in Poland authors: - Małgorzata Bruczyńska - Anna Didkowska - Sylwia Brzezińska - Magdalena Nowak - Katarzyna Filip-Hutsch - Mirosław Kalicki - Ewa Augustynowicz-Kopeć - Krzysztof Anusz journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044416 doi: 10.3390/ani13061022 license: CC BY 4.0 --- # Mycobacterium avium Subspecies paratuberculosis in Asymptomatic Zoo Herbivores in Poland ## Abstract ### Simple Summary Paratuberculosis is a bacterial infection occurring globally in ruminants. Although it has a known impact on animal health and welfare, diagnosis is complicated by high animal densities, the chronic nature of the disease, the variable course of infection, and the immune response. The aim of the current study was to confirm whether Mycobacterium avium sp. paratuberculosis (MAP) infections occur in zoo animals in Poland. Faeces samples ($$n = 131$$) were collected for analysis from different species of animals from eight zoos in Poland. Our study provides the first confirmation of MAP in bongo antelope and confirms that MAP is present in Polish zoological gardens and requires monitoring, which can be easier now due to new legislation. ### Abstract Mycobacterial infections are significant issues in zoo animals, influencing animal welfare, conservation efforts, and the zoonotic potential of pathogens. Although tuberculosis is recognised to be highly dangerous, paratuberculosis can also lead to animal losses and is potentially dangerous for humans. The aim of the current study was to confirm whether Mycobacterium avium spp. paratuberculosis (MAP) infections are currently present in zoos in Poland. Faeces samples ($$n = 131$$) were collected from different animal species from eight zoos in Poland. The faeces were decontaminated and inoculated into Herrold’s Egg Yolk Media. The species was determined using commercial DNA testing. The IS900 was checked using RT-PCR. The culture was positive in seven samples: five with M. avium, one with Mycobacterium fortiatum, and one without any identified Mycobacterium species. RT-PCR confirmed MAP genetic material in nine animals. Our findings represent the first confirmation of MAP in bongo (Tragelaphus eurycerus), indicating that it is present in Polish zoological gardens. Fortunately, the disease can be monitored more easily due to recent legislation (the Animal Health Law). ## 1. Introduction Mycobacterial infections in zoo animals have a significant impact on animal welfare and conservation efforts, and have worrying zoonotic potential [1]. Of these diseases, the most dangerous is believed to be tuberculosis (TB); however, significant animal losses can be caused by paratuberculosis. Paratuberculosis is a chronic granulomatous infectious disease caused by Mycobacterium avium subsp. paratuberculosis (MAP), an acid-fast bacterium characterised by long environmental persistence. The most commonly affected species are ruminants; however, other mammals are also susceptible [2,3]. In zoos, paratuberculosis has been confirmed among Bovidae [4,5,6], Cervidae [7] Giraffidae [8,9], Camelidae [10,11], Rhinocerotidae [12,13], and Rodentia [1,2]. In zoos, many animals can be unrecognised reservoirs of MAP; these can have major epidemiological significance by shedding MAP intermittently or chronically [14,15]. Transmission is mostly through the faecal-oral route, although vertical, pseudo-vertical and venereal transmission have been also described [16,17,18]. Animals usually develop clinical signs after a long incubation period. However, it is important to note that MAP can be shed in faeces several months before clinical signs occur. Progressive weight loss, exercise intolerance, and diarrhoea are the main clinical signs observed in clinical paratuberculosis [19]. Although it remains unclear whether MAP is a potential public health threat [20,21,22], visitors to petting zoos and zookeepers should observe safety precautions. As paratuberculosis can follow a severe course, depending on species and individuals, [1] there is a need to monitor it. This is particularly important in zoos, which are often home to very valuable and endangered species. Although MAP has been confirmed in Poland in livestock [23,24], no studies have yet examined its occurrence in Polish zoos. In total, 25 zoological gardens are registered in Poland, in 13 regions of the country. Of these, the 11 best examples are members of the European Association of Zoos and Aquariums (EAZA), together with the most important zoos from all over Europe. Only animals born and raised outside the natural environment, and which have no chance of survival otherwise, may be kept and bred in zoos; however, they may also be kept if it is required to protect the population or species, or to achieve scientific goals. In such cases, in accordance with the Animal Health Law (AHL), the animals are subject to the supervision of the competent authority. The aim of the current study was to confirm whether MAP infections occur in zoo animals in Poland. ## 2.1. Material Faeces were collected from seven Polish zoological gardens: Zoo “A” ($$n = 61$$), Zoo “B” ($$n = 24$$), Zoo “C” ($$n = 6$$), Zoo “D” ($$n = 9$$), Zoo “E” ($$n = 16$$), Zoo “F” ($$n = 1$$), and Zoo “G” ($$n = 9$$). Samples were also taken from a non-commercial breeding centre “H” ($$n = 5$$) (Table 1). All tested animals have no symptoms of disease. Non-herbivore species were excluded from the study. Animals showing signs of diarrhoea and emaciation were excluded from the study, because the purpose of the study was to monitor clinically healthy animals. Ethical approval was not required for this study as the samples were collected without any harm to the animals. The samples were collected from the following animal species: addax antelope (Addax nasomaculatus) ($$n = 1$$), alpaca (Vicugna pacos) ($$n = 10$$), Ankole-Watusi (Bos taurus) ($$n = 2$$), anoa (Bubalus depressicornis) ($$n = 2$$), waterbuck (Kobus ellipsiprymnus) ($$n = 1$$), Bactrian camel (Camelus bactrianus) ($$n = 6$$), Baringo giraffe (Giraffa camelopardalis rotshildi) ($$n = 3$$), capybara (Hydrochoerus hydrohaeris) ($$n = 1$$), Chinese bharal, (Pseudois nayaur szechuanensis) ($$n = 1$$), Chinese goral (Naemorhedus caudatus arnouxianu) ($$n = 1$$), common eland (Tragepalhus oryx) ($$n = 9$$), Djallonké sheep (Ovis aries) ($$n = 1$$), domestic goat (Capra hircus) ($$n = 9$$), domestic yak (Bos grunniens) ($$n = 1$$), dromedary (Camelus dromedarius) ($$n = 6$$), eastern bongo (Tragelaphus eurycerus isaaci) ($$n = 11$$), European bison (Bison bonasus) ($$n = 3$$), European mouflon (Ovis aries musimon) ($$n = 2$$), fallow deer (Dama dama) ($$n = 2$$), giraffe (Giraffa camelopardalis) ($$n = 3$$), guanaco (Lama guanicoe) ($$n = 2$$), Java mouse-deer (Tragulus javanicus) ($$n = 1$$), llama (Lama glama) ($$n = 3$$), lowland nyala (Nyala angasii) ($$n = 1$$), maned aruis (Ammotragus lervia) ($$n = 3$$), Mesopotamian fallow deer (Dama mesopotamica) ($$n = 1$$), Mishmi takin (Budorcas taxicolor taxicolor) ($$n = 1$$), Nile lechwe (Kobus megaceros) ($$n = 1$$), okapi (Okapia johnstoni) ($$n = 2$$), Père David’s deer (Elaphurus davidianus) ($$n = 1$$), Polish heath sheep (Ovis orientalis f. aries “Wrzosówka”) ($$n = 2$$), prairie bison (Bison bison) ($$n = 1$$), pygmy hippopotamus (Cheoropsis liberiensis) ($$n = 5$$), red cow (Bos taurus) ($$n = 1$$), red deer (Cervus elaphus) ($$n = 1$$), Reeves’s muntjac (Muntiacus reevesi) ($$n = 2$$), reticulated giraffe (Giraffa camelopardalis reticulata) ($$n = 3$$), sable antelope (Hippotragus Niger) ($$n = 2$$), scimitar-horned oryx (Oryx dammach) ($$n = 1$$), Shetland pony (Equus caballus Shetland) ($$n = 7$$), Siberian ibex (Capra sibirica) ($$n = 2$$), sika deer (Cervus nippon dybowskii) ($$n = 2$$), sitatunga (Tragelaphus spekii gratus) ($$n = 2$$), South American tapir (Tapirus terrestris) ($$n = 2$$), southern pudu (Pudu puda) ($$n = 1$$), Thorold’s deer (Cervus albirostris) ($$n = 1$$), vicugna (Vikugna vicugna) ($$n = 1$$), Visayan spotted deer (Rusa alfredi) ($$n = 2$$), white-bearded wildebeest (Connochaetes taurinus albojubatus) ($$n = 1$$), and wild goat (Capra aegagrus) ($$n = 1$$). The age of the animals ranged from 5 months to 22 years (average eight years). The material was collected from 48 females and 47 males (for 36 samples, sex could not be determined). The material (131 faecal samples) was collected in two ways: individual samples ($$n = 89$$) and pulled samples from pens ($$n = 42$$). ## 2.2. Culture The samples were processed by suspension, decontamination, and culture, according to the World Organisation to Animal Health (WOAH) Terrestrial Manual 2021 (https://www.woah.org/en/what-we-do/standards/codes-and-manuals/terrestrial-manual-online-access/, accessed on 15 December 2021). Briefly, 1 g of faeces was transferred to the distilled water and shaken for 30 min at room temperature (RT). The uppermost 5 mL of the faeces suspension was then transferred to a tube containing 20 mL $0.95\%$ 3-Hydroxy-2-phenylcinchoninic acid (HPC). After being inverted several times, the tube was left to stand for 18 h at RT. The undistributed sediment was then inoculated into Herrold’s Egg Yolk Media (HEYM, Becton Dickinson, Franklin Lakes, NJ, US), with and without mycobactin. The media were incubated at 37 °C for eight months and checked for colonies every seven days. ## 2.3. Genetic Analysis DNA from colonies was isolated using the Genolyse isolation kit (Hain Lifescience, Nehren, Germany). The strains were classified as non-tuberculosis mycobacteria species using the GenoType Mycobacterium CM test (Hain Lifescience) based on the DNA-Strip technology. Briefly, the DNA was extracted and then subjected to multiplex amplification with biotinylated primers. Following this, reverse hybridisation was conducted. MAP was detected by real-time PCR using the VetMax M. paratuberculosis 2.0 Kit (Thermofisher Scientific, Waltham, MA, USA). The test targets the insertion sequence IS900, part of the IS1110 family of insertion sequences. It was repeated between 14 and 18 times in MAP genome. All tests were performed according to the manufacturers’ manuals. ## 3.1. Culture Positive results were observed in seven samples. Nonchromogenic, small, round, cream-coloured colonies of fastidious cells developed in four to six months on HEYM media with mycobactin (Figure 1). ## 3.2. Genetic Analysis The genetic analysis confirmed M. avium in five isolated strains and M. fortuitum in another. One strain was found not to be characteristic of any Mycobacterium species (Table 2). RT-PCR was positive in the case of nine animals from four zoos. Detailed data of animals are presented in Table 3. ## 4. Discussion Our findings indicate that MAP infections are present in asymptomatic herbivores in Polish zoological gardens. Although not all infected animals develop clinical disease, inflammatory gastrointestinal disease can occur, especially in ruminants [2]. In addition, as asymptomatic infected animals may also be reservoirs of MAP, and hence play a role in its transmission, it is important to confirm the epidemiological status of zoos. Although infectious diseases are usually monitored using serological methods, in zoos it is difficult to collect sera samples for a large number of animals, so non-invasive materials such as faeces are used. The gold standard diagnostic test in the case of mycobacteria is microbiological culture. While the sensitivity of the test varies according to the type of sample and medium used, it is nevertheless characterised by $100\%$ specificity [25]. In the present study, culture confirmed the presence of M. avium in two bongo antelopes originating from Zoo B (Table 1, Table 2 and Table 3), and MAP was confirmed molecularly. While this appears to be the first confirmed case of MAP infection in this species, another bacterium from the Mycobacterium avium complex (MAC) has previously been diagnosed in bongo; M. avium spp. hominissuis (Mah) was confirmed in five captive bongo antelopes suffering from emaciation [26]. Mah was also confirmed in another sitatunga antelope in a Polish zoo [27]. RT-PCR also achieved positive results in the case of seven other species (Table 3). All seven species have previously been confirmed to harbour MAP: pudu [28], guanaco [29,30], European bison [7], giraffe [8], Bactrian camel [31], alpaca [29,32], and domestic goat [33]. In the present study, more positive samples were confirmed by RT-PCR than by culture; nine samples were confirmed molecularly but only two in culture (Table 2 and Table 3). This is a similar result as noted in research on camelids [34]; however, it contrasts with a recent study from a zoo in Mexico [6]. The different sensitivity observed between our diagnostic methods may be due to intermittent excretion or low numbers of bacteria in the faecal sample. Reliable detection of MAP in specific individuals requires repeated, regular sampling. However, as the present study is intended as an epidemiological assessment of the general situation in Polish zoos, samples were only collected once. In addition, some strain types are difficult to cultivate and may have not been detected in culture [35]. In three out of five M. avium-positive samples, MAP was not detected by RT-PCR (Table 1 and Table 2). Further tests will be needed to confirm which subspecies has been isolated. As tuberculosis has previously been confirmed in Polish zoos [36,37], it should be noted that MAP-positive animals can complicate the diagnosis of tuberculosis, due to cross-reactions [38,39,40]. As even asymptomatic animals were found to yield positive results, all zoos should conduct tests in animals showing symptoms that may suggest paratuberculosis. It is important to note that symptoms can vary between ruminants as well as in other species [41]; however, the most common clinical symptom is diarrhoea, leading to wasting and gradual emaciation, while the feed uptake is not affected [42]. As clinical signs of the disease are often inapparent [41], a key tool for controlling paratuberculosis in zoos is necropsy, although gross pathology does not develop in all species [43]. Furthermore, caseation and calcification of lesions have been confirmed in small ruminants, deer, and camelids, which can be mistaken for tuberculosis [44]. In histological examination, paratuberculosis manifests with histiocytic granulomatous inflammation, mucosal thickening, and atrophy of intestinal villi and glands [45]. A key consideration for zoo owners concerns legal action in the case of paratuberculosis being confirmed in a zoo. Since 21 April 2021, within the territory of the Republic of Poland, as in the territories of all other countries belonging to the European Union, Regulation (EU) $\frac{2016}{429}$ of the European Parliament and of the Council of 9 March 2016 on transmissible animal diseases and amending and repealing certain acts in the field of animal health (Journal of Laws of the European Union L No. 84, p. 1, as amended) also known as the Animal Health Law (AHL), has been in force. In some areas, the AHL has introduced changes in the field of animal health protection, one of which is the division of infectious animal diseases into five categories (A, B, C, D, E). The AHL regards paratuberculosis as a category E disease, indicating that it requires surveillance in the EU, and that notification, reporting, and surveillance rules apply. The AHL introduces a more universal, but very general, division of all animals into kept animals, i.e., those that are kept by humans, and wild animals, i.e., those that are not. Zoo animals, being under human control, are regarded as kept animals. Unfortunately, insufficient information exists concerning sick animals in zoos or on private farms to conduct a full epizootic investigation and thus identify the source of paratuberculosis infection [46]. Although the zoonotic potential of MAP remains uncertain [20], it is important to monitor this disease to ensure public health. This is particularly important in zoos, which often have separate areas where children can pet the animals, and where behaviours conducive to faecal–oral infections can often be observed [47]. Based on the distribution of the tested zoological gardens (Table 1), location does not seem to play an important role in the chance of infection. Effective control of MAP infections in zoo animals requires preventive measures, the most important of which is the introduction of strict hygiene measures. In addition, individuals with unknown MAP status should be tested before being introduced to the zoo, and comprehensive pathology and disease monitoring programmes should be adopted [48]. Additionally, as wildlife faeces are known to play an important role as a source of infection for livestock, effective zoo-wide pest control programmes are important [49]. ## 5. Conclusions This study confirms MAP in zoo animals in Poland, and is the first to identify MAP in bongo antelope. Out of 131 samples of asymptomatic animals, genetic analysis confirmed M. avium in five isolated strains and M. fortuitum in one. Our findings confirm that MAP infections are present in asymptomatic animals in Polish zoological gardens, and that there is a growing need for effective control programmes. 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--- title: Tamarindus indica Extract as a Promising Antimicrobial and Antivirulence Therapy authors: - Mohamed F. Ghaly - Marzough Aziz Albalawi - Mahmoud M. Bendary - Ahmed Shahin - Mohamed A. Shaheen - Abeer F. Abu Eleneen - Mohammed M. Ghoneim - Ayman Abo Elmaaty - Mohamed F. M. Elrefai - Sawsan A. Zaitone - Amira I. Abousaty journal: Antibiotics year: 2023 pmcid: PMC10044421 doi: 10.3390/antibiotics12030464 license: CC BY 4.0 --- # Tamarindus indica Extract as a Promising Antimicrobial and Antivirulence Therapy ## Abstract The worldwide crises from multi-drug-resistant (MDR) bacterial infections are pushing us to search for new alternative therapies. The renewed interest in medicinal plants has gained the attention of our research group. Tamarindus indica L. (T. indica) is one of the traditional medicines used for a wide range of diseases. Therefore, we evaluated the antimicrobial activities of ethanolic extract of T. indica. The inhibitions zones, minimum inhibitory concentration (MIC), minimum bactericidal concentration (MBC), and fractional inhibitor concentration indices (FICI) against Gram+ve and −ve pathogens were detected. The bioactive compounds from T. indica extract were identified by mass spectroscopy, thin-layer chromatography, and bio-autographic assay. We performed scanning electron microscopy (SEM) and molecular docking studies to confirm possible mechanisms of actions and antivirulence activities, respectively. We found more promising antimicrobial activities against MDR pathogens with MIC and MBC values for Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), i.e., (0.78, 3.12 mg/mL) and (1.56, 3.12 mg/mL), respectively. The antimicrobial activities of this extract were attributed to its capability to impair cell membrane permeability, inducing bacterial cell lysis, which was confirmed by the morphological changes observed under SEM. The synergistic interactions between this extract and commonly used antibiotics were confirmed (FICI values < 0.5). The bioactive compounds of this extract were bis (2-ethylhexyl)phthalate, phenol, 2,4-bis(1,1-dimethylethyl), 1,2-benzenedicarboxylic acid, and bis(8-methylnonyl) ester. Additionally, this extract showed antivirulence activities, especially against the S. aureus protease and P. aeruginosa elastase. In conclusion, we hope that pharmaceutical companies can utilize our findings to produce a new formulation of T. indica ethanolic extract with other antibiotics. ## 1. Introduction Recently, the failure to manage and treat infectious diseases associated with resistant bacteria are well-established globally. Further, increased attention is required from all healthy organizations throughout the world to establish promising strategies and funding for researchers to avoid the proliferation of this issue. The control of microbial infections started with the discovery and development of antibiotics. Antimicrobial drugs are critical for lowering the global burden of infectious illnesses [1]. On the other hand, antibiotic resistance has become more frequent worldwide, often due to new resistance mechanisms, which allow the increase and spreading of resistant bacterial strains [2]. Due to the scarcity of antibiotics effective in controlling resistant pathogenic bacteria, the spread of multi-drug-resistant (MDR) strains has become a public health concern [3]. Virulence factors have several essential roles in the increasing mortality and morbidity rates, leading to life threating diseases [4]: allowing microbial breakthroughs the host defense power; increasing the rate of evolutionary processes; and increasing the pathogenesis of the microbial cells. Treatment failures are being recorded at an unprecedented rate, especially for the infections with multi-drug-resistant (MDR) and multi-virulence pathogens [5]. The treatment failures continue to rise due to the transmissibility of genetic components encoding both virulence and antibiotic resistance, in addition to the fact that the discovery and development of new antimicrobial drugs has begun to dry up. Therefore, new trends in the development of antimicrobial strategies include the inhibition of microbial virulence arrays rather than growth pathways. Accordingly, the combination of antivirulence compounds with other antimicrobial drugs can be used to avoid treatment failures. Several antivirulence mechanisms have previously been reported, including toxin neutralization, inhibition of biofilm formations, inhibition of cell adherence, downregulating virulence gene expression, and binding with virulence proteins and enzymes [6,7]. Several new chemical compounds have been found to have antivirulence activities; meanwhile, their potential side effects on different systems in the human body remain the greatest clinical challenge impeding the use of these compounds. Therefore, we return again to the use of natural compounds from medicinal plants to fight the virulence factors of resistant pathogens. Medicinal plants have many advantages, such as their safety and several antimicrobial mechanisms. Several secondary metabolites have been found inside medicinal plants which can be used to treat different diseases [8]. Identification of the bioactive compounds of medicinal plants is essential to select the most active antimicrobial compounds. Several methods can be used to identify the biologically active compounds with high separation efficiency, such as bio-autography assay and layer chromatography (TLC) [9]. Plant phytochemicals, such as flavonoids, phenolic compounds, alkaloids, and tannins, generate secondary metabolisms and exhibited antimicrobial effects against human pathogens and phytopathogens in plants [1,2]. One of the most important multipurpose medicinal plants is the Tamarind tree, which was first produced in India and used in treatment of GIT disorders, such as dysentery and other diarrhea-related illnesses; the healing of wounds; diabetes; hepatic disorders; and some types of helminthic and bacterial infections thanks to the high levels of phenolic compounds, cardiac glycosides, crude proteins, and carbohydrates. In this respect, Tamarindus indica L (T. indica) has significant medicinal properties, as reported in several phytochemical studies [10]. The T. Indica pulps are used for many industrial purposes, such as the production of flavoring agents and sweet meats; meanwhile, the seeds are used in food manufacturing to improve texture and viscosity. In the same context, salads, stews, and soups are made from the leaves and flowers of T. Indica in several areas throughout the worlds. The seeds of T. indica contain many active biological compounds such as fatty acids (palmitic acid, eicosanoic acid), phenolic antioxidants, campesterol, and b-amyrin [11]. On the other hand, leaf extracts of T. indica are known to contain many compounds, such as flavonoids (e.g., naringenin, epicatechin, catechin, and apigenin), polyphenols, β-carotene, and ascorbic acid [12]. These phenolic molecules, with various chemical structures, could be exploited for therapeutic intervention thanks to their effective biological activity [13,14]. Furthermore, the bioactivities of these metabolites are linked to polyphenol interactions with biomolecules, such as carbohydrates, lipids, and proteins, which cause cell permeability alterations in target bacteria and, finally, disruption their cell walls [14]. The goal of this investigation was to assess the antibacterial and antivirulence potentials of a ethanolic extract from fruits of T. indica against multi-drug-resistant (MDR) bacterial strains, such as Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa), isolated from Egyptian hospital environments. ## 2.1. Antibacterial Effects of T. indica against MDR Bacteria The antimicrobial activities of the ethanolic extract from T. indica against both MDR Gram+ve (S. aureus) and Gram−ve (P. aeruginosa) isolates were announced in our study. The inhibition zone diameters of T. indica ethanolic extract were 31 ± 0.17 and 20 ± 0.21 mm for S. aureus and P. aeruginosa, respectively. The promising results of inhibition zones diameters were matched with both MIC and MBC values. Interestingly, the MICs and MBCs of T. indica ethanolic extract were 0.78 ± 0, 3.12 ± 0 mg/mL for S. aureus and 1.56 ± 0, 3.12 ± 0 mg/mL for P. aeruginosa, respectively. Therefore, the antimicrobial activity of this extract on the tested Gram+ve bacteria was higher than on the tested Gram−ve bacteria. Furthermore, the interactions between the tested extract and the commonly used antibiotics (imipenem, amikacin, and ofloxacin) were detected. Interestingly, synergistic interactions were recorded for these combinations, with FICI values < 0.5. Therefore, the antimicrobial activities of the tested antibiotics were increased in the presence of T. indica ethanolic extract. ## 2.2. The Possible Antimicrobial Mechanisms of Actions The leakage of K+ ions in response to the T. indica ethanolic extract at MIC concentration was measured by an atomic absorption spectrophotometer. T. indica ethanolic extract caused a rapid increase in ion leakage in both S. aureus (Gram+ve) (Figure 1A) and P. aeruginosa (Gram−ve) (Figure 1B) during the first 60 min after exposure. This was still increasing after 60 min, but at a low rate. The nucleotide leakage in both S. aureus (Figure 1C) and P. aeruginosa (Figure 1D) gradually increased upon treatment with the MIC concentration. Thus, Gram+ve and Gram−ve bacteria possess a strong sensitivity to T. indica extract, which causes great cell membrane damage. Moreover, SEM was performed to observe the morphological effects of T. indica on the MDR P. aeruginosa and S. aureus isolates. In contrast to untreated P. aeruginosa cells, which displayed typical bacilliform with uniformity in size and distribution (Figure 2A), cells treated with the extract had irregular, withered, coarse surfaces; lysis of cell membranes; and leakage of cellular contents, forming aggregations and adhesions (Figure 2B). Similar alterations were observed in cells of S. aureus treated with the same extract (Figure 2C,D). ## 2.3. Phytochemical Analysis of the Bioactive Molecules Phytochemical analysis revealed that T. indica ethanolic extract includes phenols, flavonoids, alkaloids, quinones, tannins, saponins, and terpenoids. Paper TLC was used to purify active antibacterial compounds, which was followed by a bio-autography assay. The dried fraction of T. indica ethanolic extract with an Rf value of 0.4, among all other ethanolic extract fractions, demonstrated antibacterial activity against P. aeruginosa and S. aureus. This fraction was subjected to UV, IR, and MS scanning. From the UV profile, two major peaks were evidenced at λ = 264.5 and 214.5 nm (Figure 3A). In addition, the IR spectrum (Figure 3B) showed principal peaks at 3351, 2934, 1736, 1633, 1246, and 1076 cm−1, corresponding to OH, aliphatic C-H, C=O, C=C (aromatic ring), O=C-O-, and aromatic C-H bands. MS of the extract revealed three major compounds: Bis (2-ethylhexyl) phthalate (C24H38O4) with an MW of 390,277 (Figure 3C); aromatic hydrocarbon [phenol, 2,4-bis (1,1-dimethylethyl)] (C14H22O) with an MW of 206.167 (Figure 3D), and 1,2-benzenedicarboxylic acid, bis(8-methylnonyl) ester (C28H46O4) with an MW of 446.339 (Figure 3E). ## 2.4. GC-MC Analysis According to mass spectroscopy analysis, the chemical composition of purified ethanolic extract from T. indica contained the following molecules: (i) bis (2-ethylhexyl) phthalate (C24H38O4) with an MW of 390, 277 ($56.5\%$), as illustrated in Figure 4; (ii) the aromatic hydrocarbon (phenol, 2,4-bis (1,1-dimethylethyl)-(C14H22O)) with an MW of 206.16 ($43.6\%$), as illustrated in Figure 4; (iii) 1,2-benzenedicarboxylic acid, bis (8-methylnonyl) ester (C28H46O4) with an MW of 446.339 ($22.4\%$). ## 2.5. Molecular Docking Studies From all the virulence proteins expressed by S. aureus and P. aeruginosa which were tested by the molecular docking studies, only the S. aureus protease and P. aeruginosa elastase could bind with bioactive compounds of T. indica. Regarding S. aureus, it was discovered that the investigated compounds (Bis (2-ethylhexyl) phthalate [1], phenol, 2,4-bis (1,1-dimethylethyl) [2], and 1,2-benzenedicarboxylic acid, bis(8-methylnonyl) ester [3] could interact with the S. aureus protease through pi-H bond with PRO153, at binding scores of −6.52, −4.46, and −7.36 Kcal/mol and with RMSD values of 1.74, 1.52, and 1.58 Å, respectively. It is worth noting that the benzene ring was responsible for pi-H bond formation with PRO153 for the three investigated compounds, as shown in Table 1 and Figure 5. However, regarding P. aeruginosa, it was shown that compound 1 could interact with P. aeruginosa elastase at a binding score of −7.47 Kcal/mol, and the value of RMSD was 2.38 Å. Obviously, the carboxylate group at the phthalate moiety in compound 1 had the ability to form H-bond with TYR155 at 2.85 Å. The terminal side chain of compound 1 could form two H-pi bonds with HIS140, at distances of 3.70 and 4.40 Å. In addition, compound 2 exhibited a binding score of −4.74 Kcal/mol, with an RMSD value of 0.98 Å. Notably, the tert-butyl moiety in compound 2 was able to form an H-pi bond with HIS140 at a distance of 4.25 Å. Furthermore, compound 3 displayed a binding score equal to −7.45 Kcal/mol and an RMSD value equal to 1.90 Å. Notably, the phenyl ring of compound 3 was able to form a pi-H bond with ASN112 at a distance of 4.79 Å. The terminal side chain of compound 3 was able to form an H-pi bond with HIS140 at a distance of 4.43 Å. Compounds 1, 2, and 3 were able to form metal bonds with GLU141 at a distance of 1.72 Å, as illustrated in Table 1 and Figure 6. ## 3. Discussion The wide spread of MDR bacterial and fungal pathogens has created several health problems [15,16], especially throughout those countries that did not follow up on the infection control guidance. The discovery and development of antimicrobial therapies are normally conducted with very slow steps, which is not in line with the rate of evolution of antimicrobial resistance mechanisms to commonly used antibiotics. In the same context, the therapeutic switching of already-used medicine [17] and the renewed interest in medicinal plants [18] may compensate for the wide gap in solutions for this issue. Therefore, the use of complementary and alternative medicines, especially natural compounds and essential oils, with certain precautions, are the perfect choice to prevent the compounding of this crisis. The use of medicinal plants must occur under full medical supervision, without any self-medication, to avoid drug interactions, in addition to other adverse effects [19]. T. indica extracts from various plant parts have been used for several therapeutic purposes [20]. In this study, T. indica ethanolic extract was selected, and its antimicrobial and antivirulence activities were evaluated against Gram+ve and Gram−ve resistant pathogens. Generally, a broad spectrum of antibacterial activity with low MIC and MBC values was observed for the tested ethanolic extract compared to ordinary aqueous extract [20]. In this study, the promising use of ethanolic extract from T. indica as an alternative and complementary therapy for resistant pathogens was confirmed by the large zones of inhibitions. Additionally, the MIC and MBC values were detected for S. aureus (0.78, 3.12 mg/mL) and P. aeruginosa (1.56, 3.12 mg/mL), respectively. Parallel to our finding, several authors reported the antimicrobial activities of ethanolic extract of T. indica through MIC and MBC values, thus confirming our hypothesis [20,21,22]. Therefore, the success of our postulates regarding the antimicrobial activities of T. indica makes reconsidering the use of other medicinal plants an urgent necessity. In fact, the in vitro antimicrobial potential of these natural compounds did not reflect the overall bacterial response in vivo, since it was tested in broth rather than in a physiological human body, in addition to the bioavailability problems [23]. Therefore, we cannot suggest the use of T. indica extract as the sole drug for treating resistant pathogens. In the same context, resistance to commonly used antibiotics such as imipenem, amikacin, and ofloxacin were previously reported. For that, we suggest the use of a combination of any of these antibiotics and T. indica extract. Synergistic interactions between these combinations were detected (FICI > 0.5) against both Gram+ve and Gram−ve bacteria. Confirming our finding, the co-admixing of antibiotics with natural compounds and/or essential oils had huge success in treating MDR bacterial and fungal infections, in contrast to the use of each one alone [24,25,26]. Furthermore, the use of medicinal plants can reduce the duration of use, dose, and toxicity hazards associated with antibiotics, and decrease the possibility of the emergence of new resistant strains [27]. It has also been reported that a huge number of bioactive compounds were found in various parts of medicinal plants. These bioactive compounds were diverse in their chemical structure, and their concentration was not the same in each part of the medicinal plants. Therefore, it is essential to determine the exact bioactive compounds of medicinal plant extract. In this report, GC-mass and other spectrophotometer analyses of the ethanolic extract of T. indica revealed several phytochemicals, including phenolic content, flavonoids, alkaloids, quinones, tannins, saponins, and terpenoids. This finding was in agreement with other T. indica-related phytochemical studies [28]. The antimicrobial activity of this extract may be attributable to its phenolic compounds [28]. Chemical analysis of the extract revealed three major compounds: (i) Bis (2-ethylhexyl) phthalate (DEHP, C24H38O4); (ii) aromatic hydrocarbon [phenol, 2,4-bis (1,1-dimethylethyl)] (PD, C14H22O); and (iii) 1,2-benzenedicarboxylic acid, bis(8-methylnonyl) ester (C28H46O4). The DEHP, a major bioactive compound in this extract, showed a broad spectrum of antibacterial activity against both G+ve and G−ve bacteria compared to other secondary metabolites [29]. The amount of these phenolic compounds, which are present in almost every part of this medicinal plant, varied according to the extraction method, geographical location, and climatic conditions [30]. The novelty of this study is the determination of antimicrobial mechanisms by various methods, in addition to the assessment of the antivirulence activity of T. indica extract by molecular docking. In this study, the microbial cytoplasmic membrane is the main target site of the bioactive compounds of this extract. Similar studies documented the mechanism of action via inhibition of protein and DNA synthesis, increasing cell membrane and wall permeability as well as lysing the cells [31,32]. The results obtained in this study revealed that T. indica ethanolic extract caused a rapid increase in ion leakage, especially of K+ ions, and nucleotides in both S. aureus (Gram+ve) and P. aeruginosa (Gram−ve); this was confirmed by an atomic absorption spectrophotometer. Additionally, the treated isolates showed irregular, withered, and coarse surfaces; lysis of the cell membrane; and leakage of cellular contents, forming aggregations and adhesions under SEM. Parallel to our findings, it was confirmed that the phenolic compounds acted on the bacterial cytoplasmic membrane as the essential intercellular materials of the treated pathogens, such as nucleic acids and other ions, released into the extracellular solution by cellular leakage [33], and similar observations have been documented by other studies [31]. Furthermore, the antivirulence activities of the T. indica ethanolic extract were assessed by a molecular docking study. All of the bioactive compounds showed good binding capacities with the S. aureus protease and P. aeruginosa elastase. The measurement of binding scores, RMSD values, and amino acid interactions of the investigated compounds of the tested extract with the S. aureus proteases and P. aeruginosa elastase confirmed these antivirulence activities. The DEHP affected the intercellular communication in the bacteria and resulted in a significant reduction in biofilm, extracellular polysaccharide, prodigiosin, lipase, haemolysin, and protease, thus increasing the susceptibility of bacteria to conventional antibiotics when administered synergistically [29]. In addition, 1,2-benzenedicarboxylic acid, bis (8-methylnonyl) ester is one of putative compounds found in many plants, and is known to have antimicrobial activity [34]. ## 4.1. Microorganisms, Plant Materials, and Extraction The MDR bacterial isolates which were used in this study, such as S. aureus ATCC25923 and Pseudomonas aeuginosa ATCC 27853 were kindly provided from the microbiological units of Zagazig University Hospitals. Furthermore, these isolates were confirmed by molecular detection of specific 16S RNA genes using the previously described primers. Additionally, the MDR patterns for these isolates were confirmed by the Kirby Bauer Disc Diffusion Method according to CLSI, 2020 [35]. Tamarind (Tamarindus indica L.) is a medicinal plant used commonly in Egyptian folk medicine. Fresh fruit of T. indica, which was planted in Southern Egypt (Aswan city), was purchased from a local supplier in Zagazig City, Egypt. This fresh fruit was used to prepare the antimicrobial ethanolic extract used in the investigation as follows. A 50-g sample of dry fruit powder was added to 500 mL of methanol $80\%$ and continuously shaken for 48 h at room temperature. The ethanolic solution was then centrifuged at 5000 rpm for 10 min and filtered through 1 layer of Whatman No. 1 filter paper. The supernatant was evaporated using a rotary vacuum evaporator under 34–36 kPa pressure at 45 °C. The pellet was dissolved in distilled water containing $2\%$ dimethylsulfoxide to form stock solutions with 25 mg/mL concentration [36]. ## 4.2.1. Agar Diffusion Assay by Filter Paper Disc Method The antibacterial activity of ethanolic Tamarindus indica extract was evaluated in triplicate by the disc diffusion method [37]. Pure bacterial isolates were sub-cultured in Muller–Hinton agar medium at 37 °C for 4 h. The density of the bacterial suspension was adjusted to 106 CFU/mL, equivalent to standard barium sulfate (0.5 McFarland). Then, 3 layers of sterile filter paper discs (Whatman No. 3, 6 mm diameter) were saturated with the fruit extract, left to dry for 1 h, and then placed on the surface of the agar plate and incubated for 24 h at 37 °C. Antibacterial activity was evaluated by measuring the entire diameter of the inhibition zone in mm. ## 4.2.2. Estimation of Minimum Inhibitory Concentration (MIC) and Minimum Bactericidal Concentration (MBC) MIC is the lowest concentration that inhibits visible bacterial growth in liquid media, whereas MBC is the lowest concentration at which no growth occurs in solid media. The MICs and MBCs of the ethanolic extract from T. indica were determined in triplicate by the broth microdilution method [38]. To obtain the appropriate suspensions needed for each experiment, the stock solution of tamarind extract (25 mg/mL) was diluted in nutrient broth to obtain twofold serial dilutions ranging from 0.195 to 12.5 mg/mL. The bacterial broth suspensions were prepared at 108 CFU/mL from overnight cultures. Each experiment used positive and negative controls, the first consisting of tubes containing a bacterial suspension and nutrient broth, and the second of tubes containing the extract from T. indica and nutrient broth. All tubes were incubated for 24 h at 37 °C and examined for turbidity (λ = 600 nm) to detect the MIC value. Regarding the determination of MBC, 100 µL of MIC concentration and the 2 highest concentrations were introduced onto the nutrient agar plate and incubated at 37 °C for 24 h to determine their MBC values. ## 4.2.3. Evaluation of Co-Admixture of the Ethanolic Extract from T. indica with the Commonly Used Antibiotics by Checkerboard Method The degree of interaction between the tested extract and other antibiotics (imipenem, amikacin, and ofloxacin) was assessed by determination of the fractional inhibitory concentration (FICI) values in triplicate according to [39,40]. The MIC values of the tested extract alone and in the presence of other antibiotics were detected. Additionally, the MIC values of the antibiotics alone and in the presence of tested extract were measured. The FICI values were detected according to the following equation: FICI = (MIC of tested extract in combination/MIC of tested extract alone) + (MIC of antibiotic in combination/ MIC of antibiotic alone). The synergistic and antagonistic interactions were obtained for FICI ≤ 0.5 and FICI ≥ 4, respectively. On the other hand, additive and indifference effects were expected when 0.5 < FICI ≤ 1 and 1 < FICI < 4, respectively. ## 4.3. Assessment the Possible Antibacterial Mechanisms of T. indica Ethanolic Extract We assessed the cell membrane integrity by measuring both the K+ level and nucleotide leakage. The K+ leakage was determined following the method reported previously [41], with minor modifications. Briefly, bacteria were allowed to grow overnight in nutrient broth in a shaking incubator at 37 °C. Then, normal saline was used to wash the cells three times, and then cells were resuspended in 1 mmol/L glycylglycine (Sigma, USA) buffer solution with a pH value of 6.8 [42]. Bacteria were treated with the studied extract at the detected MIC, and incubated in a shaking incubator at 37 °C. After that, we took the bacterial cell suspensions after 0, 10, 20, 40, 60, 80, 100, and 120 min and filtered them through a membrane (0.22 µm pore-size membrane, Sartorius, Gottingen, Germany) to remove any bacteria. We determined the K+ concentration in the supernatant by applying an atomic absorption spectrophotometer (900T, Perkin-Elmer Ltd., Beaconsfield, UK) at λ = 766.5 nm. In reference to previously established standard K+ solutions, the absorbance was converted to K+ concentration (ppm). The experiments were conducted in triplicate, and the obtained data value averages are reported herein. The bacterial nucleotide leakage was measured upon treatment with the studied extract [43]. After incubation of the bacterial suspensions with MIC concentrations of the extracts at 37 °C and 150 rpm, samples were taken after 1, 2, 4, 6, and 8 h and filtered through a 0.22 µm pore-size membrane for the removal of the bacterial cells. The absorbance of the filtrate was detected utilizing a UV-spectrophotometer at λ = 260 nm. The nucleotide leakage was confirmed to be a valid indicator of cytoplasmic membrane damage. Scanning electron microscopy (SEM) was also employed to observe morphological changes caused in S. aureus and P. aeruginosa by the ethanolic extract of T. indica. Cultures of tested microorganisms were treated with the detected MIC, then incubated for 6 h at 37 °C. After incubation, bacterial cells were pelleted by low-speed centrifugation (4000 rpm for 15 min); washed with sterile, distilled water; and fixed with $3\%$ glutaraldehyde in 0.1 M phosphate buffer for 4 h at 4 °C. Then, cells were exposed to a secondary fixation with $2\%$ aqueous solution of osmium tetroxide for 60 min at room temperature, and were then serially dehydrated with 75, 95, and $100\%$ ethanol. The last drying step was performed over anhydrous CuSO4 for 15 min. After finishing the drying step, we mounted the cells on stubs of 12.5 mm diameter, attached them with sticky tabs, and then coated them in an Edwards S150B sputter coater with 25 nm thickness. Non-treated cells were used as negative controls. Small cell samples of the treated bacteria and the relative controls were examined with SEM (JEOL, Japan) at an accelerating voltage of 20 kv [44]. ## 4.4.1. TLC and Bio-Autographic Assays Thin layer chromatography (TLC) was carried out to identify the bioactive fractions of T. indica extract. First, its powder was dissolved in ethyl acetate and spotted by capillary tubes on TLC paper (20 × 20 cm) using running solvents chloroform/methanol (6:4, v/v). The detected fractions were then dissolved in methanol and dried. The retention factor (Rf) values of each fraction were calculated, and the antimicrobial activity of the dried fractions was re-tested against the selected pathogenic bacteria using bio-autographic assay. TLC-dried fractions were placed on the surface of a Mueller–Hinton agar plate seeded with each microbe and incubated at 37 °C for 24 h. After incubation, the clear zone that appeared on the media was taken as proof of the antibacterial efficacy of the tested extract [45,46]. ## 4.4.2. Phytochemicals Analysis The active ingredients of the T. indica ethanolic extract were analyzed for the presence of different phytochemicals according to standard procedures [47]. The structure of the purified active components of T. indica was analyzed using data from a wide range of spectroscopic techniques, such as ultraviolet (UV), infrared (IR), and mass spectroscopy (MS), at the Regional Centre for Mycology and Biotechnology, AL-Azhar University, Cairo (Egypt). ## 4.4.3. GC-MS Analysis Identification of the bioactive substances from T. indica was conducted at the National Research Center, Cairo, Egypt. A GC/MS-QP –1000 -Mass spectrophotometer (SHIMADU, Kyoto, Japan) instrument was used for analyses. For interpretation of the mass spectroscopy (GC-MS), we used the database of the Chemical Abstracts Service (CAS). The spectrum of unknown components was compared with the spectrum of known molecules stored in the CAS and Wiley 6 N libraries [48,49]. We recorded the retention time, molecular weight (M.Wt), molecular formula, and composition percentage in the sample material, following a previously published method [50]. ## 4.5.1. Molecular Docking (In Silico) Studies A molecular docking study, which afforded us further insights into the inhibitory potential of the investigated compounds, was used in this study. The nuclei of the detected bioactive compounds of T. indica were evaluated against all virulence proteins expressed by the tested pathogens. The potential of the investigated compounds for the virulence proteins S. aureus and P. aeruginosa was pursued via molecular docking using an MOE 2019 suite [51]. ## 4.5.2. Preparation of the Investigated Compounds By using PerkinElmer ChemOffice Suite 2017, the bioactive compounds of the tested extract were chemically drawn to make them ready for the molecular docking program [52,53]. We uploaded the investigated compounds to one database and saved them as an MDB extension file. ## 4.5.3. Preparation of the Proteases of S. aureus and P. aeruginosa All virulence proteins of the X-ray structure of S. aureus and P. aeruginosa were detected from an online protein data bank website, and downloaded with PDB entries 4INK [54] and 1EZM [55]. Accordingly, the sequence of the target protein chain was identified and protonated; then, the broken bonds were connected and fixed. Before beginning the docking process, the virulence proteins of the tested pathogens were energetically minimized [52,53]. ## 5. Conclusions This study revealed that T. indica ethanolic extract had a variety of in vitro antibacterial activities against MDR Gram+ve and Gram−ve isolates. It also had synergistic effects with conventional antibiotics (imipenem, amikacin, and ofloxacin) and reduced their MICs. The mechanism of activity showed that the extract was able to influence the cellular membrane permeability, as evidenced by potassium and nucleic acid leakage, resulting in cell lysis and death. 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--- title: Exosome inspired photo-triggered gelation hydrogel composite on modulating immune pathogenesis for treating rheumatoid arthritis authors: - Ke Rui - Xiaoxuan Tang - Ziwei Shen - Chao Jiang - Qiugang Zhu - Shiyi Liu - Nan Che - Jie Tian - Jue Ling - Yumin Yang journal: Journal of Nanobiotechnology year: 2023 pmcid: PMC10044428 doi: 10.1186/s12951-023-01865-8 license: CC BY 4.0 --- # Exosome inspired photo-triggered gelation hydrogel composite on modulating immune pathogenesis for treating rheumatoid arthritis ## Abstract Although exosome therapy has been recognized as a promising strategy in the treatment of rheumatoid arthritis (RA), sustained modulation on RA specific pathogenesis and desirable protective effects for attenuating joint destruction still remain challenges. Here, silk fibroin hydrogel encapsulated with olfactory ecto-mesenchymal stem cell-derived exosomes (Exos@SFMA) was photo-crosslinked in situ to yield long-lasting therapeutic effect on modulating the immune microenvironment in RA. This in situ hydrogel system exhibited flexible mechanical properties and excellent biocompatibility for protecting tissue surfaces in joint. Moreover, the promising PD-L1 expression was identified on the exosomes, which potently suppressed Tfh cell polarization via inhibiting the PI3K/AKT pathway. Importantly, Exos@SFMA effectively relieved synovial inflammation and joint destruction by significantly reducing T follicular helper (Tfh) cell response and further suppressing the differentiation of germinal center (GC) B cells into plasma cells. Taken together, this exosome enhanced silk fibroin hydrogel provides an effective strategy for the treatment of RA and other autoimmune diseases. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12951-023-01865-8. ## Introduction As a chronic immune-mediated disease, rheumatoid arthritis (RA) is characterized by synovitis and destruction of articular cartilage [1, 2]. Patients with RA are at higher risk for osteoporosis, cardiovascular disease and cancer, which results in significant social-economic problems [3]. Current therapeutic approaches, such as cytokine suppressive drugs and surgery, can only achieve symptom relief and slow down the process of joint destruction [4, 5]. Thus, development of a treatment modality against specific pathological environments for alleviating immune dysregulations and effectively protecting joint is necessary. T follicular helper cells (Tfh), a newly discovered subset of CD4 + T cells, play a significant pathogenic role in RA [6–8]. Tfh can interact with germinal center B cells (GC B) to maintain their survival and promote their differentiation into plasma cells (PC) for antibody production upon antigenic challenge [9]. Mesenchymal stem cells (MSCs) can effectively modulate the immune response by secreting anti-inflammatory cytokines. However, suboptimal differentiation of MSCs in inflammatory environment and their fast clearance by the immune system greatly compromise the therapeutic efficacy of MSC therapy in RA [10]. Alternatively, exosomes, as an essential component of extracellular vesicles, can carry biological molecules such as proteins, lipids and RNAs, which are involved in intercellular communication pathways for regulating the immune response in vivo [11, 12]. Recently, we have demonstrated that olfactory ecto-mesenchymal stem cell-derived exosomes (OE-MSC-Exos) possess therapeutic effect on suppressing autoimmune pathogenesis, such as experimental Sjögren’s syndrome (ESS) and inflammatory bowel disease (IBD) [13, 14]. Although exosomes have exhibited potent immune modulatory functions, the short residence time in joint tissue limits their therapeutic efficacy and their therapeutic mechanism still remains unclear [15, 16]. In situ gelation system can be delivered to fill irregularities of target sites upon local microenvironment or external stimuli to reduce the friction between tissue surfaces in joints [17–19]. Combined with exosome therapy, in situ hydrogels also allow extended exosome release and protects them from enzymatic degradation [20–22]. Specially, photo-triggered gelation system that cross-linked under source of UV and visible light irradiation offers great opportunities to delivery exosomes in the site of interest, due to its mild gelation conditions with high spatial and temporal precision of the gelation process [23–26]. However, the poor biocompatibility and insufficient bioactivity of most synthetic polymers limits the efficacy of in situ hydrogel based therapeutic strategies. As a natural protein, silk fibroin based scaffolds with low immunogenicity exhibit excellent biocompatibility and favorable chondrocyte response for biomedical applications in cartilage tissue engineering [27–29]. Moreover, we previously demonstrated that the in situ silk fibroin hydrogel could successfully recruit immune cells to enhance the therapeutic effect on remodeling the immune microenvironment [30]. Olfactory ecto-mesenchymal stem cells (OE-MSCs) can be isolated from the olfactory lamina propria and retain the potential for multidirectional differentiation [31, 32]. As the olfactory mucosa nerve tissue is permanently renewable and the nasal sheath is an open organ, OE-MSCs are easily accessible in every individual and have normal functions and renewability even in old persons, facilitating the OE-MSC-Exos based therapies [33]. In this study, an effective strategy on modulating immune pathogenesis against RA was developed by encapsulating OE-MSC-Exos into photo-cross-linkable silk fibroin hydrogel for in situ treatment of RA and protecting joints. The exosomes released from Exos@SFMA successfully inhibited Tfh cell polarization by expressing PD-L1 to down-regulate the PI3K/AKT pathway in T cells. Significantly, Exos@SFMA hydrogel holds excellent in vivo therapeutic effects on suppressing Tfh cell response and hindering development of GC B cells and plasma cells for treating RA (Scheme 1). Scheme 1Schematic illustration of (a) isolation of exosomes from OE-MSCs obtained from olfactory lamina propria and (b) in situ gelation system improved exosome therapy for successfully modulating immune pathogenesis by inhibiting Tfh cell polarization and B cell development in RA. ## Characterization of OE-MSC-Exos and their suppressive effects on tfh cell response in vitro Initially, exosomes were isolated from the stem cell conditioned medium of olfactory ecto-mesenchymal stem cells (OE-MSCs) using the ultracentrifugation method [13]. The morphology and diameter of exosomes were investigated using transmission electron microscopy (TEM) and scanning electron microscopy (SEM). The TEM images demonstrated that OE-MSC-Exos possessed classic cup-shaped structure and SEM images demonstrated that the exosomes were spherical in shape (Fig. 1a and b). The particle sizes of OE-MSC-Exos were further determined by nanoparticle tracking analysis (NTA), which ranged from 50 to 150 nm (Fig. 1c). To investigate the chemical components of the exosomes, LC-MS/MS proteomic analysis of OE-MSC-Exos were performed and several classic markers of exosomes such as CD81, CD9, CD63, CD44, CD90 and HSP72 were found in OE-MSC-Exos (Fig. S1). To further identify the source of exosomes and provide a basis for future applications [34], common surface markers of exosomes, such as the tetraspanin protein family (CD63 and CD9) and mesenchymal stem cell surface markers (CD44, CD90 and CD27), were analyzed by western blotting and flow cytometry. As shown in Fig. 1d, CD63, CD9, TSG101 and ALIX were positive, while calnexin, a cellular marker, was negative in OE-MSC-Exos, confirming that exosomes were derived from mesenchymal stem cells. Then, the immunophenotype of OE-MSC-Exos was determined. Homologous labeling of OE-MSCs, such as CD44, CD90, and CD29, was expressed on these exosomes, whereas hematopoietic cell markers, such as CD34 and CD45, and the myeloid cell marker CD11b were absent (Fig. 1e). Furthermore, more than $85\%$ of the exosomes expressed CD63, indicating that the membrane antigens of OE-MSC-Exos are preserved with high purity (Fig. S2). Fig. 1Isolation and characterization of OE-MSC-Exos and their suppression on Tfh cell responses in vitro. ( a, b) Representative TEM (a) and SEM (b) images of exosomes derived from OE-MSCs. ( c) The particle sizes of OE-MSC-Exos were determined by NTA. ( d) Western blot analysis of CD63, CD9, TSG101, ALIX and Calnexin in OE-MSC-Exos. ( e) The expression of typical identification markers of MSCs on the surface of OE-MSC-Exos. ( f) Naïve CD4 + T cells from the spleen of C57BL/6 mice were cultured for 72 h in the presence of OE-MSC-Exos (30, 60, 90 µg/ml) under Tfh differentiation conditions and analyzed by flow cytometry assay. Values represent means ± S.D. ($$n = 3$$). * $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$ It has been recently reported that Tfh cells can facilitate the differentiation of germinal center (GC) B cells into plasma cells, which leads to RA pathogenesis [7, 9, 35]. Thus, the capacity of OE-MSC-Exos on regulating the Tfh cell polarization was investigated by culturing Naïve T cells with 30, 60, or 90 µg/mL OE-MSC-Exos under Tfh polarization conditions respectively. As shown in Fig. 1f, OE-MSC-Exos effectively decreased the frequencies of Tfh cells in vitro, indicating that OE-MSC-Exos can successfully inhibit Tfh polarization of Naïve T cells and may restrain pathogenic Tfh cell generation in RA. ## PD-L1 expression of OE-MSC-Exos on suppressing tfh cells via PI3K/AKT pathway Numerous studies have reported that the PD-1/PD-L1 pathway can regulate T cell activation, tolerance and exhaustion [36]. Tfh cells highly express the inhibitory PD-1 molecule, and the PD-1/PD-L1 pathway has been shown to to suppresses Tfh cell polarization [37]. Therefore, to understand the mechanism of this immunosuppressive effect, the present of PD-L1 on the surface of OE-MSC-Exos was investigated. As shown in Fig. 2a, flow cytometry analysis showed that PD-L1 was highly expressed on OE-MSC-Exos. Then, whether PD-L1 on MSC-Exos contributes immunosuppressive effect on Tfh polarization was further determined by culturing T cell with OE-MSC-Exos interfered with siPD-L1 (Fig. S4). Importantly, the inhibition of Tfh cell polarization was significantly reversed in the OE-MSC-Exos group treated with siPD-L1, indicating that PD-L1 expressed on OE-MSC-Exos plays a key role in the suppression of Tfh cell differentiation (Fig. 2b and c). Fig. 2PD-L1 expression on OE-MSC-Exos and mechanisms of OE-MSC-Exos on suppressing Tfh cell polarization. ( a) Flow cytometry analysis of PD-L1 expression on the surface of OE-MSC-Exos. ( b, c) Tfh cell polarization in the presence of OE-MSC-Exos or OE-MSC-Exos (siPD-L1) after 72 h analyzed by flow cytometry assay. Values represent means ± S.D. ($$n = 3$$). ( d, e) The phosphorylation levels of PI3K and AKT in naïve T cells treated with OE-MSC-Exos or OE-MSC-Exos (siPD-L1) under Tfh polarization conditions. Values represent means ± S.D. ($$n = 3$$). $P \leq 0.05$, *$P \leq 0.01$ It has been reported that the differentiation of Tfh cells is initiated by activating the PI3K/AKT pathway, which further induces CXCR5 expression [38]. Therefore, to explore the underlying mechanism of immunosuppressive effect on Tfh cells by the released exosomes, T cells were incubated with OE-MSC-Exos or OE-MSC-Exos with siPD-L1 interference, and the protein level of phosphorylated PI3K/AKT in T cells was assessed. As shown in Fig. 2d and e, the protein levels of both phosphorylated PI3K and phosphorylated AKT were significantly decreased in the OE-MSC-Exos treated group, whereas this effect was almost abolished after knocking down PD-L1 expression. These results show that the released exosomes express PD-L1 to inhibit Tfh polarization by down-regulating the PI3K/AKT pathway. ## Fabrication and characterization of OE-MSC-Exos loaded hydrogel As encapsulating exosomes within the hydrogel network can effectively prolong exosome release and consumption for enhancing therapeutic efficacy [39, 40], OE-MSC-Exos were loaded into silk fibroin based photo-crosslinkable hydrogel (SFMA). Photographs in Fig. 3a show that the successful photo-triggered gelation of the precursor solution containing 100 µg/mL of OE-MSC-Exos after irradiation to give Exos@SFMA hydrogel. The rapid gelation was achieved within 200 s upon irradiation of 365 nm light (Fig. 3b and S3), which is essential for precious exosome delivery to target sites. As joint destruction usually occurs during RA progression, ideal injectable biomaterials should also possess flexible mechanical properties to distribute the stress of normal physiological activities to protect joints from further damage [41]. As shown in Fig. 3e, a cylindrical sample of Exos@SFMA hydrogel exhibited promising capacity on recovering to its initial shape rapidly from compression when the loading was released, due to the high mechanical strength of silk fibroin, which facilitates the stress dispersion in lesions of RA. Meanwhile, there were no statistically significant differences in the compressive stress and Young’s modulus of hydrogels between SFMA and Exos@SFMA hydrogels, eliminating the effect of exosome encapsulation on the mechanical properties of the silk fiborin based hydrogel system (Fig. 3c and d). Additionally, both hydrogels showed good surface hydrophilicity, which is beneficial for lubrication of the joint (Fig. 3f) [42, 43]. Hydrogen bonds can be formed between phospholipids of exosomes and protein chains within the protein-based hydrogels to facilitate exosome encapsulation [44]. To further demonstrate the successful exosome encapsulation in hydrogels, lyophilized Exos@SFMA hydrogels were observed using SEM. Monodisperse or aggregated exosomes existed on the surface of the porous structure inside hydrogels (Fig. 3g). Fig. 3Fabrication and characterization of OE-MSC-Exos loaded hydrogel (Exos@SFMA). ( a) Photographs of the photo-triggered gelation process of hydrogel for OE-MSC-Exos encapsulation. ( b) Rheological analysis* of hydrogels. ( c) Compressive stress and (d) Young’s modulus of hydrogels. Values represent means ± S.D. ($$n = 3$$). ( e) Photographs of compression and recovery process of hydrogels for stress dispersion. ( f) Contact angle of hydrogels. Values represent the means ± S.D. ($$n = 5$$). ( g) SEM images of microstructure and OE-MSC-Exos encapsulated in hydrogels. ( Dispersed exosomes are indicated by white arrows) $P \leq 0.05$, $P \leq 0.01$, *$P \leq 0.001$ ## In vitro cell cytotoxicity of the hydrogels To evaluate the in vitro cell cytotoxicity of the hydrogels, L929 fibroblasts were seeded on SFMA and Exos@SFMA hydrogels and assessed by CCK-8 assay. Figure 4d shows that L929 fibroblasts exhibited normal proliferation with cell viability of more than $95\%$ during 3 days of incubation on both SFMA and Exos@SFMA hydrogels. Bone marrow stromal cells (BMSCs) are important for cartilage regeneration [20, 45, 46]. The results of Live/Dead staining assay showed that very few dead BMSCs were observed after culturing on the Exos@SFMA hydrogel (Fig. 4a), suggesting that Exos@SFMA hydrogel has excellent cell compatibility to BMSCs for facilitating cartilage repair. Moreover, as shown in Fig. 4b and c, the migration of BMSCs was accelerated by hydrogels and the cells in SFMA and Exos@SFMA group exhibited higher migration rates than control group within 24 h, demonstrating Exos@SFMA holds strong capacity on recruiting BMSCs. Fig. 4In vitro cell cytotoxicity and recruitment effect of Exos@SFMA on bone marrow stromal cells (BMSCs) and in vitro immunosuppressive effect of Exos@SFMA on Tfh cell polarization. ( a) Live/dead assay of BMSCs cultured on hydrogels for 3 days. Scale bar: 100 μm. ( b, c) Migration of BMSCs toward Exos@SFMA. ( C: Area where BMSCs were seeded; H: Area where hydrogels were fabricated; Arrow: Direction of BMSCs migration) scale bar: 200 μm. Values represent means ± S.D. ($$n = 3$$). ( d) Cell viability of L929 cells cultured on hydrogels. Values represent means ± S.D. ($$n = 3$$). ( e) Schematic illustration of the transwell assay and the accumulation of PKH67-labeled OE-MSC-Exos (green) released by Exos@SFMA hydrogel in T cells after 24, 48, 72 h of incubation. ( bar = 10 μm). ( f) Relative green fluorescence intensity of T cells after 24, 48, 96 h of incubation. Values represent means ± S.D. ($$n = 4$$). ( g) The proportions of Tfh cells cultured on Exos@SFMA hydrogel after 72 h. Values represent means ± S.D. ($$n = 3$$). $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$ As hydrogels can maintain the bioactivity of exosomes and allow extended exosome release [47], the cellular uptake of the exosomes from Exos@SFMA hydrogel by T cells was investigated. The OE-MSC-Exos were labelled with membrane dye PKH67 and encapsulated in the hydrogel. As shown in Fig. 4e and f, green fluorescence labelled exosomes were constantly released from Exos@SFMA and gradually accumulated in T cells within 96 h of incubation, demonstrating the prolonged exosome release and consumption. Then, immunosuppressive effect of hydrogels on Tfh polarization was evaluated by transwell assays. Significantly, Exos@SFMA hydrogels effectively suppressed T cell activation by releasing exosomes after 48 h of incubation, whereas no immunosuppressive effect on Tfh cells was found for the pure SFMA hydrogel (Fig. 4g). These results indicate that the Exos@SFMA hydrogel can provide the sustained release of exosomes and exhibits an excellent capacity on suppressing Tfh polarization. ## Exos@SFMA efficiently alleviated the progression of CIA Inspired by the positive results of in vitro studies, the therapeutic effect of the Exos@SFMA hydrogel on treating RA was evaluated. The collagen-induced arthritis (CIA) mouse model was established by immunization with CII/CFA on Day 0 and boosted with CII/IFA on Day 21. The hind paws were treated with OE-MSC-Exos (Exos group) or in situ formed Exos@SFMA hydrogel (Exos@SFMA group) on Day 18 and 25 after the first immunization (Fig. 5a). As shown in Fig. 5b and c, the degree of swelling in each paw was significantly ameliorated and the size of the draining lymph nodes was also significantly reduced in Exos@SFMA hydrogel treated mice on Day 42 after the first immunization. Moreover, the clinical score was significantly lower than that of other groups and the development of arthritis was strikingly delayed in the Exos@SFMA hydrogel group (Fig. 5d and e). The notable immunological feature in CIA mice is the excessive production of autoantibodies against CII [48]. As shown in Fig. 5f, the level of anti-CII autoantibodies in the serum of mice in Exos@SFMA hydrogel group was significantly lower than those in control group and Exos group. Furthermore, the section of hind paws on Day 42 was evaluated with H&E staining. The histological examination indicated that cartilage destruction (cd) and inflammatory cell infiltration (ici) occurred in the joints of mice in both the PBS and OE-MSC-Exos groups, whereas significant improvement was achieved in the Exos@SFMA group with clear joint spaces, intact articular cartilage and much less joint inflammation (Fig. 5g). Masson’s trichrome staining assay also indicated an increased volume fraction of collagen at articular cartilage in Exos@SFMA group than PBS and Exos groups (Fig. 5h). Taken together, these results suggest that Exos@SFMA holds promising therapeutic effect on the treatment of RA and effectively protects articular cartilage, which is much more efficient than OE-MSC-Exos alone. Fig. 5Exos@SFMA efficiently alleviated the progression of CIA and modulated immune dysregulations. ( a) Schematic illustration of establishing the CIA mouse model and OE-MSC-Exo or Exos@SFMA treatments. ( b, c) Photographs of popliteal lymph nodes (b) and the hind paws (c) of mice treated with PBS, OE-MSC-Exos or Exos@SFMA on Day 42 after the first immunization. ( d, e) Clinical score (d) and incidence (e) in CIA mice from each group monitored every 3 days after the first immunization. Values represent means ± S.D. ($$n = 6$$). ( f) Serum levels of CII-specific autoantibodies from each group measured using ELISA. Values represent means ± S.D. ($$n = 4$$). ( g) Hematoxylin and eosin staining of hind paw sections from each group on day 42 after the first immunization. Cartilage destruction (cd) and inflammatory cell infiltration (ici) are indicated by black arrows. Values represent means ± S.D. ($$n = 4$$). ( h) Expression of collagen (blue) Masson’s trichrome staining assay. Values represent means ± S.D. ($$n = 5$$). $P \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$ ## Modulation of immune pathogenesis during RA development Importantly, as Tfh cells play an essential pathogenic role in RA, the efficacy of Exos@SFMA hydrogel on suppressing the Tfh cell response in RA pathogenesis was explored in CIA mice (Fig. 6a). As shown in Fig. 6b, the percentage of Tfh cells in the popliteal lymph nodes (PLN) of CIA mice was remarkably reduced after treatment with Exos@SFMA hydrogel, which was significantly lower than that in OE-MSC-Exos group. In RA pathogenesis, Tfh cells can further promote the proliferation of GC B cells and their differentiation into plasma cells to damage the joint [7, 9]. Thus, the development of GC B cells and plasma cells in CIA mice after treatment was further investigated. As shown in Fig. 6c and d, the frequencies of both GC B cells and plasma cells in MLNs were also significantly decreased in Exos@SFMA hydrogel group. These results demonstrated that Exos@SFMA hydrogel treatment can effectively modulate the immune pathogenesis of RA, leading to the alleviation of the disease. Fig. 6Exos@SFMA efficiently modulated immune dysregulations. ( a) Schematic illustration of modulating immune pathogenesis by inhibiting Tfh cell polarization and B cell development in RA via Exos@SFMA treatment. ( b-d) Modulation of immune pathogenesis in RA development. Proportions and cell number of Tfh cells (b), germinal center B cells (c), and plasma cells (d) in popliteal lymph nodes (PLN) of CIA mice treated with PBS, OE-MSC-Exos or Exos@SFMA on day 42 after the first immunization. Values represent means ± S.D. ($$n = 4$$). $P \leq 0.05$, $P \leq 0.01$ ## Conclusion In this work, we demonstrated that OE-MSC-Exos improved hydrogel system held promising immunotherapeutic effects, excellent mechanical property and bioactivities for the effective treatment of RA, which directly targeted immune pathogenesis of RA with enhanced protective effects on inflamed joints. Although MSCs have been demonstrated to display therapeutic effect in RA, the majority of infused stem cells get entrapped in filter organs without significantly homing to sites of injury [49, 50]. Exosomes are essential paracrine products of MSCs, which have emerged as important mediators of cellular and interorgan communication for the replacement of cell-based therapy [15]. Although exosomes are smaller than cells, it contains quite complex biomolecules, including proteins and RNA, which holds enhanced capacity on cell penetrating for delivery of therapeutic biomolecules [51–53]. At present, studies have found that exosomes derived from various MSCs display good therapeutic effect on CIA through microRNA or cytokines (Table S1) [54–59]. In this study, we demonstrated that PD-L1 on OE-MSC-Exos played a critical role in exosome mediated immunosuppression of Tfh cell differentiation, depending on PI3K/AKT pathway. In summary, our work contributes to illustrating the therapeutic mechanism of transplanted stem cell-derived exosomes and provides a powerful platform to treat RA and other autoimmune disorders in the future. ## Isolation and culture of OE-MSCs and BM-MSCs For the culture of OE-MSCs, the olfactory epithelium tissue was obtained from the nasal cavity of DBA/1 mice (4-week-old) and cultured in the medium (DMEM/F-12 supplemented with $15\%$ fetal bovine serum) (Gibco) for 7 days. The growth of adherent cells was observed after removal of non-adherent cells. When the adherent cells reached $90\%$ confluence in the flask, they were trypsinized and expanded for three passages. For the culture of bone marrow mesenchymal stem cells (BMSCs), BMSCs were harvested from the femurs and tibiae of wild-type mice and culturing them in medium (DMEM supplemented with $15\%$ fetal calf serum) (Gibco) for 3 days. Then, nonadherent cells were removed by careful three-time washings with PBS. The adherent cells were expanded for three passages and used for experiments. ## Isolation of OE-MSC-Exos Isolation of exosomes from OE-MSCs was described previously [13]. Briefly, OE-MSCs were washed three times with PBS and cultured in the medium (DMEM/F-12 supplemented with exosome-depleted fetal bovine serum) for 48 h. The culture supernatants were collected and centrifuged at 300 g for 10 min, 2000 g for 10 min, and 10,000 g for 30 min at 4 °C to remove cells and debris. This was followed by ultracentrifugation spins at 10,000 g (Beckman Coulter, California, USA) for 1 h at 4 °C. The exosomal pellets were washed with PBS and spun 10,000 g centrifugation for another 1 h at 4 °C. Finally, the OE-MSC-Exos were resuspended in PBS and stored at − 80 °C. The protein concentration of OE-MSC-Exos was measured with bovine calf albumin (BCA) kit (CWBIO, Beijing, China). The size of the OE-MSC-Exos was measured by ZetaView PMX 110 (Particle Metrix) and data was analysed using the NTA software ZetaView 8.04.02. ## Uptake of OE-MSC-Exos by T cells To evaluate the cellular uptake of the released exosomes from Exos@SFMA hydrogel by T cells, OE-MSC-Exos were labeled with the PKH67 Fluorescent Cell Linker Kit (Sigma-Aldrich) and encapsulated the hydrogel in a transwell chamber. Then, it was incubated with T cells by transwell assay for 24, 48 and 96 h, and photographed under an Olympus FluoView FV1000 confocal microscope. ## Immunosuppression of tfh differentiation Naïve CD4+ T cells were purified from the spleens of wild-type mice using naïve CD4+ T cell Isolation Kit (Stem Cell). Purified murine naive CD4+ T cells (1.75 × 106 /mL) were seeded in a culture plate precoated with anti-CD3 (2 µg/mL) and anti-CD28 (2 µg/mL) antibodies and incubated with OE-MSC-Exos or Exos@SFMA hydrogel under Tfh polarization conditions for 3 days. Cytokines and neutralizing antibodies for Tfh polarization are as follows: recombinant murine IL-6 (25 ng/mL) and IL-21 (20 ng/mL); anti-IFN-γ (5 µg/mL), anti-IL-4 (5 µg/mL) and anti-TGF-β (5 µg/mL) neutralizing antibodies. In order to explore the effects of PD-L1 molecule carried by OE-MSC-Exos on Tfh differentiation, the proportion of Tfh cells was detected after adding siPD-L1-OE-MSC-Exos into the Tfh induction system. To obtain siPD-L1-OE-MSC-Exos, PD-L1 siRNA (GCCACAGCGAATGATGTTT) and nonspecific scramble siRNA (RiboBio Co, Guangzhou, China) was designed and synthesized. OE-MSCs were transfected with PD-L1 siRNA or negative control using lipofectamine 2000 (Invitrogen) according to the manufacturers’ instructions and exosomes were extracted from transfected cells following the above protocol. ## Animal experiments DBA/1J mice (8–10 weeks old) were obtained from the Shanghai Laboratory Animal Center (Shanghai, China) and maintained in the Jiangsu University Animal Center (Jiangsu, China). All animal experiments were approved by the Jiangsu University Animal Ethics and Experimentation Committee. ## Arthritis induction and treatment CIA mice were immunized twice using bovine type II collagen (Chondrex, WA, USA). Bovine type II collagen and Freund’s complete adjuvant (SigmaAldrich, St. Louis, MO) were mixed and injected subcutaneously at the base of the tail in the first immunization. In order to boost immunization, the mixture of bovine type II collagen and Freund’s incomplete adjuvant (SigmaAldrich, St. Louis, MO) were administered 21 days later. To explore the effects of the Exos@SFMA hydrogels treatment, The hind paws were treated with OE-MSC-Exos or in situ formed Exos@SFMA hydrogel on days 18 and 25 after the first immunization. The joint tissue of mice was collected for histologic analyses with H&E staining and Masson’s trichrome staining. ## Statistical analysis All data were shown as the means ± Standard Deviation (SD). The statistical significance was determined by the Student’s t test or one-way ANOVA. All analyses were performed using SPSS 16.0 software. P values < 0.05 were considered statistically significant. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. Smolen JS. Rheumatoid arthritis primer — behind the scenes*. Nat Rev Dis Primers* (2020.0) 6 32. 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--- title: Environmental Factors Influencing COVID-19 Incidence and Severity authors: - Amanda K. Weaver - Jennifer R. Head - Carlos F. Gould - Elizabeth J. Carlton - Justin V. Remais journal: Annual review of public health year: 2022 pmcid: PMC10044492 doi: 10.1146/annurev-publhealth-052120-101420 license: CC BY 4.0 --- # Environmental Factors Influencing COVID-19 Incidence and Severity ## Abstract Emerging evidence supports a link between environmental factors—including air pollution and chemical exposures, climate, and the built environment—and severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission and coronavirus disease 2019 (COVID-19) susceptibility and severity. Climate, air pollution, and the built environment have long been recognized to influence viral respiratory infections, and studies have established similar associations with COVID-19 outcomes. More limited evidence links chemical exposures to COVID-19. Environmental factors were found to influence COVID-19 through four major interlinking mechanisms: increased risk of preexisting conditions associated with disease severity; immune system impairment; viral survival and transport; and behaviors that increase viral exposure. Both data and methodologic issues complicate the investigation of these relationships, including reliance on coarse COVID-19 surveillance data; gaps in mechanistic studies; and the predominance of ecological designs. We evaluate the strength of evidence for environment–COVID-19 relationships and discuss environmental actions that might simultaneously address the COVID-19 pandemic, environmental determinants of health, and health disparities. ## INTRODUCTION The coronavirus disease 2019 (COVID-19) pandemic caused by the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has resulted in tens of millions of infections and millions of deaths worldwide [22]. SARS-CoV-2 can be transmitted through the air via aerosols and droplets from talking, sneezing, and coughing [22, 136]. Exposure—particularly prolonged and indoors—to the airspace of symptomatic and asymptomatic individuals is the dominant route of viral transmission, although transmission can also occur via direct physical contact and fomites [22, 54]. Although SARS-CoV-2 transmission has been documented in nearly all countries, the transmission dynamics and burden of morbidity and mortality have varied substantially across nations, regions, and even neighborhoods [17, 22, 145]. This spatial and temporal heterogeneity is likely attributable to several factors, including nonpharmaceutical interventions, risk perception and human behavior, prevalence of comorbidities, structural determinants of health, and environmental conditions [52, 59, 76, 84, 92, 140, 146]. Here, we evaluate the rapidly evolving COVID-19 literature—as well as research on related respiratory illnesses such as severe acute respiratory syndrome (SARS), Middle East respiratory syndrome (MERS), and influenza—to examine the effect of environmental conditions on SARS-CoV-2 transmission and COVID-19 incidence and severity. We propose that environmental factors influence SARS-CoV-2 via four main mechanisms (Figure 1): (a) exacerbating comorbidities and other respiratory conditions associated with severe COVID-19, (b) modifying host susceptibility to infection and/or disease severity through immune response modification, (c) regulating viral survival and aerosol transport, and (d) altering behavioral patterns that determine the frequency and intensity of pathogen exposure. We focus on the effect of four main factors—air pollution, chemical exposures, climate, and the built environment—on these pathways. In evaluating the strength of evidence for these mechanisms and environmental factors, we identify areas of uncertainty and emerging topics that could guide future research priorities (Supplemental Table 1). A critical evaluation of these relationships can strengthen estimates of the risk of COVID-19 attributable to environmental exposures and guide the design of interventions to slow virus spread, protect vulnerable populations from infection, and limit severe disease among those with elevated levels of risk. ## AIR POLLUTION EXPOSURES Robust epidemiologic literature supports the role of short- and long-term air pollution exposure in elevating the risk of respiratory viral infections and impairing immune function, raising hypotheses regarding its potential effects on COVID-19 incidence, severity, and disparities. Emerging evidence suggests that air pollution may elevate the risk of infection and mortality from COVID-19 via two key pathways (Figure 1): (a) modifying host susceptibility to infection and disease severity, and (b) elevating the risk of comorbidities. The former may be mediated by upregulation of proteins critical to viral entry and by immune system suppression from oxidative stress, epithelial damage, and pulmonary inflammation. ## Protein Expression and Immune Impairment Exposure to particulate matter (PM) can increase the expression of angiotensin-converting enzyme 2 (ACE2) and transmembrane protease serine type 2 (TMPRSS2), proteins critical to SARS-CoV-2 entry into host cells [56]. Upregulation of proteins necessary for viral entry may lead to higher viral load, thereby elevating the risk of severe COVID-19. Laboratory studies show that these proteins are upregulated in response to short-term smoke and PM exposure [115, 144]. Studies in mice have documented a dose-dependent upregulation of ACE2 and TMPRSS2 proteins following a 4-24-h exposure to smoke [144] and, separately, an intratracheal exposure to a 50-µg PM solution [115]. Upregulation occurred more in alveolar type 2 (AT2) cells—potential targets of SARS-CoV-2 [38]—and macrophages [115]. Immunological impairment prior to COVID-19 infection, induced by long-term exposure to PM, NO2, and ozone, may also increase the risk of COVID-19 infection and/or its severity. Pulmonary barrier tissues and mucociliary clearance processes form the first line of defense against invading pathogens [42, 57]. Exposure to air pollution renders these defenses less effective at preventing host pathogen invasion when damaged by particulate invasion [44]. Oxidative stress associated with air pollution exposure (e.g., to NO2, ozone, or PM) can also yield barrier tissue and respiratory system impairment [27, 72]. In addition, NO2 exposure can lead to inflammation and impair tissue defenses and phagocytic activity by depleting the antioxidant pool [86], while ozone exposure can trigger an inflammatory response and systematic oxidative stress [131]. Macrophages produce antigens to clear pathogens; however, evidence has shown that alveolar macrophages exposed to PM10 produced $50\%$ less viral antigen in response to respiratory syncytial virus (RSV), increasing the risk of infection [10]. Severe COVID-19 is associated with high inflammation and elevated levels of inflammatory cytokines [89]. Once pathogens establish themselves, inflammation of pulmonary tract mucus membranes resulting from air pollution exposure may contribute to a higher risk of severe COVID-19 outcomes through compounded inflammation [86]. Both inflammation and oxidative stress are associated with aging [46, 47, 79], and their upregulation by PM, NO2, and ozone exposure may contribute to even greater age-related COVID-19 mortality [86]. ## Elevated Comorbidity Risk PM exposure has been associated with an increased risk of chronic obstructive pulmonary disease (COPD), diabetes, and hypertension [73, 138], conditions associated with an increased risk of intensive care unit (ICU) admission, ventilation, and death from COVID-19 by harming the respiratory system and/or increasing inflammation [52]. In addition, PM2.5 exposure is linked to atherosclerosis [71], cardiovascular disease, congestive heart failure, arrhythmias, lung cancer [73, 138], and community-acquired pneumonia in older adults [99]. Meanwhile, ozone exposure has been associated with hypertension, arrhythmias, and cardiovascular-related hospital admissions, among other cardiovascular conditions [131, 138]. Short-term NO2 exposure has been associated with respiratory and cardiac mortality, including conditions such as ischemic heart disease and heart failure [26]. Short-term exposures to PM10, ozone, and NO2 have also been linked to heart failure, asthma, pneumonia, and influenza hospital admissions [138]. Such preexisting comorbidities elevate the upstream risk of poor COVID-19 outcomes [52] due to poorer baseline health. Because a history of respiratory dysfunction can lead to elevated risk of severe COVID-19, long-term exposure to metals [e.g., arsenic [118], cadmium [48], and lead [14]] that are associated with lung function impairment, respiratory symptoms, and respiratory diseases—e.g., COPD and interstitial lung disease—may elevate upstream risk of severe disease after viral infection [52]. Mechanisms have been proposed on the basis of both epidemiologic evidence and animal studies. For example, arsenic, cadmium, and lead may permanently alter lung structure and function via extensive tissue inflammation, altered expression of structurally important extracellular matrix genes, and impaired repair mechanisms in the lung epithelium [48, 118]. These physical changes to the airways then lead to restrictive and/or obstructive impairments to the lung. Additional emerging evidence indicates that exposures to pesticides, phthalates, and PFCs and per- and polyfluoroalkyl substances (PFAS) may be associated with impaired lung function [65, 106, 111]. However, the exact mechanisms through which these chemical exposures may affect lung function are unknown, though some researchers have suggested that exposures act through oxidative stress, which is associated with declining lung function and COPD [66]. Epidemiologic, clinical, and mechanistic evidence suggests several links between chemical exposures and comorbidity risk factors for COVID-19 severity beyond respiratory dysfunction, including hypertension [52], obesity [119], diabetes [98], and cancer [64]. For instance, metals—particularly mercury, lead, cadmium, and arsenic—are associated with cardiovascular disease of atherosclerotic origin [130]. Cadmium and lead exposure have well-established associations with hypertension, as well as with atherosclerosis from increased aortic atherosclerotic plaque burden [130]. Epidemiologic studies have shown links between obesity and type 2 diabetes and various metals (e.g., mercury, cadmium, lead, and arsenic) and EDCs (e.g., BPA, phthalates) [8]. Obesity is characterized by constant chronic inflammation, causing a delayed and inferior immune response. As reviewed elsewhere [98, 119], obesity and type 2 diabetes are risk factors for poor COVID-19 prognosis. A number of metals (e.g., arsenic, cadmium) and chemicals (e.g., polycyclic aromatic hydrocarbons) are considered carcinogens [64, 103] and may be important environmental correlates of COVID-19, given that individuals with cancer are considered high risk for infection and severe disease. ## Epidemiologic Studies Complementary to evidence of these plausible mechanisms, epidemiologic evidence shows a robust association between air pollution and the incidence of a number of respiratory viral infections, including SARS, influenza, and RSV [40]. Several ecological studies in the United States, China, Italy, England, and the Netherlands have found evidence that areas with poorer air quality are more likely to have elevated COVID-19 incidence and mortality [28, 29, 43, 134, 140, 150]. For example, in a study examining the relationship between long-term (from 2000 to 2016) average PM2.5 concentrations and COVID-19 mortality rate in 3,089 US counties, adjusted for 20 county-level confounders, researchers found that a 1 µg per m3 increase in PM2.5 was associated with an $11\%$ [$95\%$ confidence interval (CI): 6–$17\%$] increase in county-level COVID-19 mortality rate [140]. Using individual-level data, a cohort study of US veterans found that a 1.9 µg per m3 increase in PM2.5 concentration was associated with a $10\%$ ($95\%$ CI: 8–$12\%$) increase in risk of COVID-19 hospitalization, among individuals with COVID-19 [15]. Short- and medium-term air pollution exposures, such as those experienced during wildfire events, may also influence COVID-19 outcomes [149]. A time-series analysis of PM2.5 exposure in wildfire-affected counties in California found that a daily median increase of 10 µg per m3 over 28 days was associated with a $12\%$ ($95\%$ CI: 8–$16\%$) increase in COVID-19 cases and an $8\%$ ($95\%$ CI: 2–$15\%$) increase in COVID-19 deaths [149]. Independent of PM2.5, emerging evidence supports a positive association between COVID-19 and NOx [24, 81, 134] and mixed directionality of association with ozone exposure [134, 150]. For example, long-term exposure to NO2 has been associated with an elevated risk of COVID-19 cases and mortality across neighborhoods in Los Angeles [81]. Researchers found that an 8.7-ppb increase in mean annual NO2 concentration was associated with a 16–$31\%$ increase in the COVID-19 case rate and a 35–$60\%$ increase in mortality rates across model specifications [81]. More recently, a multiethnic cohort study from Kaiser Permanente Southern California found that one year of exposure to near-roadway nonfreeway NOx was significantly associated with an $8\%$ increase in odds of COVID-19-related ICU admission ($95\%$ CI: 2–$16\%$) and an $8\%$ increase in hazard of death ($95\%$ CI: 1–$16\%$), adjusting for sociodemographic covariates and regional air pollutants (PM2.5 and NO2) [24]. The extent to which ozone exposure influences COVID-19 is less clear, as the evidence base is limited and studies have found mixed directionality of association [134, 150]. Air pollution exposure disproportionately impacts racial and ethnic minorities and those of low socioeconomic status in both the outdoor [11, 53] and indoor environments [2], potentially contributing to unequal COVID-19 incidence and mortality rates observed across racial, ethnic, and other groups in the United States and globally [9, 97]. For example, a cross-sectional study found that Black and Hispanic populations experienced age-standardized COVID-19 mortality rate ratios that were 3.6 and 2.2 times higher, respectively, than that of non-Hispanic White populations in the United States [9]. Furthermore, Bowe et al. [ 15] observed that race and neighborhood modify the effect of air pollution on COVID-19 outcomes, with an elevated risk for Black populations ($$p \leq 0.045$$) and those living in low-socioeconomic-status neighborhoods ($p \leq 0.001$). Early time-series analyses found consistent evidence that COVID-19 incidence and mortality were negatively associated with ambient temperature and humidity in temperate and tropical regions [82, 141]. A systematic review of 17 studies of temperature, humidity, and SARS-CoV-2 that were published before March 24, 2020, found consistent evidence that SARS-CoV-2 transmission was associated with low temperatures and low humidity [88]. Since then, studies have generally shown similar, albeit more nuanced, results. For example, a study of reported COVID-19 cases in 54 English cities observed negative nonlinear associations between temperature and reported cases; cooler, drier conditions were associated with the greatest risk of incidence [100]. A study of 26 countries found modest, nonlinear association between mean temperature and Re that peaked at 10.2°C; a weak nonlinear association with RH; and no association with solar radiation, wind speed, and precipitation [122]. An investigation of the nonlinear relationships between Re and meteorological factors in the United States found that Re peaked between 10°C and 20°C and increased at lower levels of specific humidity and solar radiation [84]. Another global study found that a one standard deviation increase in local UV was associated with $0.97\%$ decrease in COVID-19 growth rate over the subsequent 2.5 weeks [21]. While meteorological conditions may facilitate or limit transmission, mitigation policies (e.g., public health measures) and behaviors are likely to play a larger role in determining the degree of transmission [114, 122]. Interventions adopted by governments (e.g., masking, distancing, policies as measured by the government response index) were found to explain five times as much variation in Re as mean temperature in the early stages of local epidemics [122]. Another global study similarly found that weather and demography explained only $17\%$ of the variation in maximum COVID-19 growth rates, while country-specific effects explained an additional $19\%$ [92]. One study of COVID-19 in the United States prior to large-scale vaccination efforts found the fraction of Re attributable to meteorological factors to be $17.5\%$, with effects proportionally attributed to temperature ($3.7\%$), humidity ($9.4\%$), and UV radiation ($4.4\%$) [84]. Another study from this same setting and time period found that Re increased as temperatures cooled but that the influence of population density on Re was 1.4 times greater than the influence of temperature [129]. Furthermore, in the phase of the pandemic preceding large-scale global implementation of vaccinations (i.e., late 2019 through early 2021), the majority of the global population was susceptible to infection, and the effects of climate as a driver of spread are likely to be minimal as compared with the effects of contact rates and public health measures. One study that focused on the beginning of the pandemic through the summer of 2020 estimated that high supply of susceptible individuals strongly limits the role of climate, suggesting that climate may become more important in the longer term, as populations become immunized through vaccination and prior infection [7]. ## Critical Gaps Epidemiologic studies assessing the relationship between air pollution and COVID-19 incidence to date have been subject to methodologic limitations that may introduce bias and limit causal inference, as discussed elsewhere [12, 135]. First, most studies to date have employed ecological designs, associating group-level air pollution exposures with aggregate COVID-19 outcomes over a broad geographic domain (e.g., 28, 29, 43, 134, 140), thus preventing inference at the individual level. Such designs may be subject to bias from residual confounding, as individual-level confounders (e.g., race, age, sex, smoking status) are also aggregated. Important group-level confounders such as population density, testing rate, and pandemic stage have not always been accounted for in the current literature [43, 135]. Second, many air pollution epidemiologic studies rely on COVID-19 disease incidence estimated from surveillance data, resulting in an outcome inherently conditioned on cases that sought care and obtained testing, thus introducing selection bias if other factors that induce an association between air pollution and testing rates are not accounted for in the analysis. Third, ambient air pollution exposure estimates are subject to misclassification from a variety of factors (e.g., inadequate ground monitoring, limitations of remotely sensed data, failure to capture individual heterogeneity in exposure) that add additional uncertainty to analyses. Finally, many of the studies discussed in this review focus on the relationship between long-term exposures to air pollution and COVID-19 incidence and severity. Yet, short-term exposures to high concentrations of pollutants, such as might be experienced during a wildfire event, may also play a role in exacerbating COVID-19 morbidity and mortality [149]. Studies of the relationship between air pollution and COVID-19 incidence will benefit from personal monitoring to estimate individual-level air pollution exposures indoors, outdoors, and across all activities (e.g., working, driving, walking). While personal monitoring is expected to benefit the air pollution epidemiology community at large, it would also advance our understanding of key exposures relevant to COVID-19, such as short-term changes in filtration systems, mobility, and shifts in emissions associated with changing commute patterns. Furthermore, monitoring air pollution exposures at the individual level may expose economic and racial disparities in access to filtration systems that remove both air pollutants and aerosolized viral particles. Prospective cohort studies based on personal measurements can reduce exposure misclassification while permitting confounder control at the individual level. However, these approaches are resource intensive; in their absence, case-crossover studies may be a feasible study design for examining the effect of group-level exposures on individual outcomes while controlling for time-invariant, individual-level confounders. Studying the effect of chemical exposures on COVID-19 incidence and severity comes with several challenges. First, populations are widely exposed to many chemicals and stressors from a variety of sources; thus, identifying the impacts of a single chemical or stressor is a challenge and is also subject to interactions with other chemical exposures. Second, chemical exposures vary over the life course, and the impacts of the timing of exposure are often uncertain. In some cases, prenatal and early-life exposures during critical windows of immune development can lead to immune function impairments and increased risk of infections later in life [19]. In addition, growing evidence indicates both that some exposures lead to epigenetic changes that can affect later generations and that exposures in utero can affect the development of the immune system, altering immune function later in life. Direct evidence demonstrating that chemical exposures affect COVID-19 risk and severity is still lacking. Even for long-studied chemicals and metals, including lead, arsenic, cadmium, bisphenols, phthalates, and PFAS, the precise impacts and mechanisms through which they act on the human body are still subject to significant uncertainty. Still, taken together, it is clear that multiple metal and chemical exposures may impact host susceptibility to COVID-19 infection and severity of COVID-19 given infection, particularly through their associations with health conditions that predispose individuals to severe COVID-19. Many of the challenges and limitations discussed previously—such as for COVID-19 air pollution epidemiology—arise when examining the relationship between the climate and COVID-19. These include methodological challenges, including unmeasured confounders, limitations of surveillance data, aggregation of exposures across broad regions, and a lack of indoor environmental monitoring, as well as a lack of mechanistic understanding of the effect of climate on SARS-CoV-2 immune response. In addition, the range of environmental conditions used in laboratory studies of virus survival are not always representative of real-world environmental conditions (e.g., conditioned indoor spaces). For example, few laboratory studies have considered RH below $30\%$, even though in winter months and in arid climates indoor RH may frequently be below $10\%$. Moreover, while it is commonly understood that the climate influences human behavior in ways that may impact SARS-CoV-2 transmission, uncertainty remains regarding the role of human movement and contact patterns, especially as government intervention has been shown to explain significant variation in transmission in the early stages of the pandemic [122]. Key uncertainties limit our understanding of the relationship between the built environment and COVID-19, including major challenges in resolving the built environment exposome and its causal linkages to health outcomes [34]. People vary greatly in their daily routines, traveling from home, work, school, and public and commercial spaces, and may exhibit location-dependent and highly personal exposures to pathogens, chemicals, and other stressors [63]. Given that many studies to date leverage neighborhood-level data, exposure misclassification may limit accurate inference on the role of the built environment. Significant research efforts are needed to clarify causal relationships between the exposome, structural characteristics, and health outcomes [34]. Such work will be critical to informing future building codes, city planning agendas, regulation of environmental hazards, and policy reforms that address housing and other built environment disparities. ## CHEMICAL EXPOSURES Exposures to a wide variety of chemicals, including metals (e.g., arsenic, cadmium, and lead) and endocrine-disrupting chemicals [EDCs; e.g., bisphenol A (BPA), phthalates, and perfluorinated chemicals (PFCs)], may be risk factors for COVID-19 susceptibility and severity [40, 127]. Although direct evidence supporting these hypotheses currently remains limited, we outline potential mechanisms for and available evidence of how chemical exposures may lead to increased susceptibility to COVID-19 infection and, given infection, increased severity of COVID-19. We focus on two key pathways (Figure 1): (a) modifying host susceptibility to infection and disease severity, and (b) elevating the risk of comorbidities. We highlight how chemical exposures may contribute to immune impairment through barrier organ dysfunction, inflammation, and oxidative stress, as well as elevate the risk of both respiratory and nonrespiratory diseases associated with severe COVID-19. ## Immune Impairment Like air pollution exposures, chemical insults have damaging effects on barrier organ function, thereby increasing the likelihood of viral entry into the host and increasing the likelihood of COVID-19 infection [103]. For example, chemical exposures can damage lung epithelial cells [55] and interfere with the tight junctions between epithelial cells [20], yielding reduced protection against viral infection over time through a more permeable airway and pulmonary epithelium. Chemical exposures—e.g., to arsenic [110] and cadmium [143]—can also lead to reduced mucociliary clearance, increasing pathogen time within the host and viral infection risk. Furthermore, chemical exposures—e.g., PFCs [132], arsenic [6], cadmium [69], and lead [39]—can weaken immune function and reduce resistance to infection through a variety of mechanisms, including (a) altered T cell proliferation and activation by reduced interleukin-2 production, an important cytokine in cell-mediated immune function; (b) altered T cell structure; (c) altered B cell maturation; and (d) direct cytotoxicity to monocytes, lymphocytes, and macrophages. After infection, chemical exposures, including cadmium, arsenic, phthalates, and BPA, may also increase the risk of severe COVID-19 through aberrant or exaggerated immune responses marked by oxidative stress, inflammation, immune dysfunction, and cell death [60, 103, 110]. Such exaggerated immune responses are associated with multiple organ system failure, COVID-19 hospitalization, and death [89]. Complementary to evidence of these plausible mechanisms through which chemical exposures may increase the risk of viral infection and severe disease, epidemiologic studies have also identified positive associations between chemical exposures and viral infections, including arsenic exposure and the risk of hepatitis A, B, and E infections [6] as well as lower respiratory infections [6]; cadmium exposure and the risk of mortality from influenza and pneumonia [102]; exposure to BPA and phthalates and the risk of respiratory tract infections [49]; and exposure to polychlorinated biphenyls and the risk of acute respiratory infections [35]. ## Evidence At this time, there remains only limited direct evidence of the potential relationship between chemical exposures and COVID-19. One study used a computational systems biology approach to characterize pathways through which EDCs (notably perfluorooctanoic acid and perfluorooctane sulfonic acid) may lead to increased predisposition to severe COVID-19. The investigators identified IL-17 and advanced glycation end products and the associated receptor signaling pathways as important potential avenues, given their association with stress and inflammation [139]. Several studies have examined the concentrations of metals/metalloids and toxic chemicals in COVID-19 patients [51, 147, 148]. One study found elevated levels of urinary chromium, cadmium, mercury, and lead in patients with worse outcomes (severe versus nonsevere; deceased versus recovered) [148], while a similar study by the same group found higher levels of whole blood chromium and cadmium concentrations—but also lower arsenic concentrations—independent of sex, comorbidities, and metal concentrations [147]. Further research leveraged Danish biobanks to obtain plasma samples from 323 subjects with known SARS-CoV-2 infection to show that, among five PFAS measured, perfluorobutanoic acid was associated with increased severe COVID-19, even when adjusted for sex, age, comorbidities, and sample batch [51]. ## CLIMATIC CONDITIONS Many infectious respiratory diseases, including influenza and those caused by other coronaviruses, exhibit seasonal patterns that are partially explained by climatic conditions affecting virus survival, seasonal immunity, and population mixing. A growing body of epidemiologic evidence suggests that SARS-CoV-2 transmission risk is higher at lower ambient temperatures and at lower humidity (e.g., 82, 84, 88, 100, 122, 129). We focus on climate conditions implicated as potential drivers of SARS-CoV-2 infection risk and COVID-19 susceptibility and severity—temperature, humidity, UV radiation, and extreme weather events—and detail potential mechanisms (Figure 1) by which these factors may influence viral persistence in the environment, immune system function, and population movement and human behaviors. ## Virus Survival The relationship between cooler temperatures and lower humidity and increased risk of COVID-19 [82, 84, 100, 122, 129, 141] may be explained by the effects of these conditions on viral persistence in the environment. Temperature and relative humidity (RH) can modulate the decay rate of viruses within aerosols [80] as well as droplet size through evaporation [85]. Laboratory studies have shown that SARS-CoV-2 exhibits greater stability at lower temperatures [25, 87, 95, 108, 113]. For example, when contained within liquid human nasal mucus and sputum, the half-life of the virus consistently declined with increasing temperatures (4°C, 21°C, and 27°C) [87]. Using a mechanistic model to predict the impact of temperature and RH on SARS-CoV-2 stability [95], researchers found that virus survival was highest at low temperatures across all humidity levels considered. The sensitivity of virus to temperature is strongest in the absence of UV light [108]. In dark conditions, the half-life of SARS-CoV-2 on simulated human secretions fell from a few days at 20°C to a few hours at 40°C [113], and at 20°C, 10 times more active virus remained on surfaces 7 hours after inoculation compared with 35°C [108]. The relationship between humidity and SARS-CoV-2 survival is more nuanced, with some evidence suggesting that the relationship is convex, such that stability is highest under both high and low RH [95]. While higher RH may slow the evaporation of respiratory droplets [85], some controlled studies of human nasal mucus and sputum [87] and viral aerosols [33] show that SARS-CoV-2 decays more rapidly at higher RH. The shared conclusion that virus survival is greater in relatively dry conditions is consistent with evidence for influenza, where transmission is optimal at a low absolute humidity [124]. UV radiation also appears to reduce viral stability, consistent with previous evidence that single-stranded RNA viruses such as SARS-CoV-2 are generally susceptible to inactivation via UV radiation [108, 116, 120]. Studies of SARS-CoV-2 in laboratory settings have shown rapid viral decay under simulated sunlight [33, 112, 116, 120]. For example, one laboratory-based simulation found that 19 min of exposure to simulated winter and fall UV conditions inactivated $90\%$ of SARS-CoV-2, a degree of inactivation achieved in just 8 min of simulated summer conditions [120]. This finding is similar to other studies that estimated inactivation of $90\%$ of SARS-CoV-2 after 11–34 min [116] and 14.3 min [112] of midday sunlight exposure in North America. While UV radiation appears to be the driving factor for viral stability in sunlight-exposed areas [108], most SARS-CoV-2 transmission occurs indoors [105], where the role of sunlight in regulating transmission may be limited. ## Immune System Effects Both the adaptive and innate immune responses have been shown to be modulated by seasonal fluctuations. In particular, the cold, dry conditions of the winter months can suppress the immune system through a number of mechanisms, including reduced mucociliary clearance [83] and reduced levels of vitamin D due to reduced sun (UVB) exposure [18]. Cellular immune response may also be affected by temperature and humidity. For example, mouse airway epithelial cells initiated a more robust antiviral response at warmer temperatures as compared with cooler temperatures [45], and mice exposed to low-humidity conditions were more susceptible to influenza [70]. However, the effects of seasonal fluctuations in immune response on COVID-19 susceptibility and severity are still largely unknown. ## Population Mixing Extreme weather events can alter transmission by affecting population mixing. On the one hand, weather-related closures of schools or businesses can weaken social connections and reduce disease incidence. Such was the case in Seattle, when a snowstorm during the height of influenza season forced workplace and school closures, leading to a reduction of 16–$95\%$ in contact rates and a reduction of 3–$9\%$ in seasonal influenza incidence [61]. On the other hand, extreme weather events such as hurricanes, wildfires, and earthquakes can also displace populations, forcing individuals to aggregate in shelters and leading to elevated population mixing and infection spread [30]. Nominal weather patterns may also play a limited role. For instance, early in the pandemic, researchers found that people in the United States were more likely to go to parks in warmer weather, but no association was found between temperature and encounter rate [142]. However, populations may also gather in closer contact indoors in the colder months, settings known to be dominant loci of transmission. In an analysis of 30 counties, meteorological factors were found to have a marginal direct effect on COVID-19 cases and deaths, reflecting action on virus stability, but meteorological factors demonstrated significant indirect effects via human mobility [36]. ## BUILT ENVIRONMENT Features of the built environment moderate the spread of infectious agents through regulation of indoor air quality and ventilation in residential and occupational settings, maintenance of ambient temperature, determination of crowding, and the distribution of health resources and hazards within neighborhoods (Figure 1) [104]. The built environment has played a key role in the transmission of other novel viral respiratory infections, including the 2003 outbreak of SARS, where transmission was facilitated by unsealed floor drains and a poor ventilation system in the Amoy Gardens apartment complex [104]. Here, we focus on both the interior characteristics of buildings and the structure and design of neighborhoods as key environmental determinants of COVID-19 incidence and severity. We discuss, but do not comprehensively review, the influence of the built environment on occupational exposures, reserving the topic for future research and synthesis. ## The Indoor Environment People in the United States and other settings spend approximately $90\%$ of their time indoors [74], and SARS-CoV-2 is acquired predominately by transmission in the indoor environment [105]. Indoor transmission can be regulated by ventilation, filtration, and climate control, which in part determine the density and survival of pathogens and thus infection risk [76, 78]. While sanitation can reduce pathogen density on surfaces, risk of infection from touching a contaminated surface has been estimated to be low for COVID-19 (<4 in 10,000 surface touches) [54]. Given the minimal role of fomite transmission in the COVID-19 pandemic, the importance of surface sanitation in indoor environments is expected to be minimal compared with ventilation and filtration. Ventilation that achieves 4–6 air changes per hour (ACH) is thought to reduce airborne concentrations and mitigate airborne spread of SARS-CoV-2 [4]. However, few nonspecialized buildings are designed to mitigate airborne transmission [93], and many indoor settings do not achieve infection-risk-based ventilation targets. For instance, more than half of California elementary school classrooms did not meet state standards for classroom ventilation (2.8 ACH) in a 2013 assessment [91]. Optimal ventilation rates are challenging to define for transmission prevention because they vary on the basis of individual risk, occupancy, and activity [93]. Ventilation alone is not sufficient in many scenarios, necessitating additional filtration practices. Filtration with minimum efficiency rating value (MERV) 13 or high-efficiency particulate air (HEPA) filters can reduce viral particle concentrations, with MERV 13 filters capturing $66\%$ of 0.3–1.0 µm particles and HEPA filters exhibiting near $100\%$ capture efficiency [4]. Many buildings are maintained at temperature and humidity conditions within a comfortable range, which could protect SARS-CoV-2 from destabilizing extremes [95]. SARS-CoV-2, like other human coronaviruses [94, 95], can survive in typical climate-controlled conditions of moderate temperature and low humidity. Homes, businesses, and other buildings that are equipped with climate control may inadvertently maintain suitable transmission environments. At the same time, lower-income and minority populations are more likely to live in buildings without climate controls such as air conditioning [101], which can pose additional health risks from prolonged exposures to extreme temperatures and mold [59, 101, 126]. Furthermore, the role of temperature is complicated by behavioral responses to indoor conditions. For instance, in a high school in Israel, temperatures exceeding 40°C may have been less suitable for SARS-CoV-2 survival but prompted students and teachers to remove their masks, leading to an outbreak of COVID-19 cases [31]. The physical structure of homes, businesses, and other buildings can also facilitate or impede the ability to social distance and/or quarantine. Crowding can raise SARS-CoV-2 transmission risk by increasing interpersonal contact frequency and duration. Indeed, SARS-CoV-2 transmission was associated with large household size, crowding, and socioeconomic status among pregnant women in New York City [41], and high SARS-CoV-2 seroprevalence ($22.1\%$) was observed among agricultural workers, many living in overcrowded housing [77]. In addition, US counties with a high percentage of households with poor housing, defined as overcrowded, overpriced, and/or missing kitchen and plumbing facilities, also had a higher incidence of—and mortality from—COVID-19 [3]. Finally, crowding in carceral facilities has resulted in several large COVID-19 outbreaks, and the case rate among prisoners has been estimated at 5.5 times that of the general population [117]. Increased crowding in Massachusetts prisons (defined as a $10\%$ increase in occupancy over design capacity) was also associated with a $14\%$ increase in COVID-19 incidence ($95\%$ CI: 3–$27\%$) [75]. ## Multiscale Structural Factors At the neighborhood scale, the built environment codifies structural inequalities through how people live, work, learn, and socialize, creating a highly racialized, heterogeneous geography of risk [145]. These structural factors create environments where historically marginalized groups are more likely to be exposed to pathogens, become infected, and die from infection [104]. Historical practices, including redlining and the diversion of public resources away from minority communities, have decreased access to quality education, health care, work, housing, and food [1, 32, 59, 107, 145]. Thus, neighborhoods can be chronically detrimental to community health and particularly risky for infectious disease transmission. Racial and socioeconomic segregation may elevate the risk of COVID-19 transmission by creating barriers that separate communities from essential resources, elevate exposure to SARS-CoV-2, and concentrate transmission in segregated communities [1, 58, 146]. In the United States, ongoing racial and socioeconomic segregation groups people in neighborhoods with fewer essential services and complete streets investments, including grocery stores, bus and bike infrastructure, green spaces, and quality housing [16, 59, 137]. This historic disinvestment contributes to higher burdens of both infectious disease and noncommunicable comorbidities in majority-minority, low-income neighborhoods, as compared with majority White, wealthy neighborhoods [5], through reduced access to nutritious foods, limited active and safe travel, and increased household exposures to pathogens and temperature extremes [59, 126, 137]. For example, US counties that were one standard deviation above the mean of residential segregation (as measured by the multigroup relative diversity index) experienced COVID-19 mortality and infection rates that were $8\%$ ($95\%$ CI: 2–$14\%$) and $5\%$ ($95\%$ CI: 1–$10\%$) higher than the mean when accounting for 50 demographic, density, social capital, health risk, health system capacity, air pollution, essential business, and political view variables [133]. In Kolkata, India, COVID-19 spatial clusters were found to have high correspondence with low-income areas with high values on the index of multiple deprivation (a measure of housing conditions and amenities, assets, water, sanitation, hygiene access, and gender disparities in literacy and work) [37]. Health system access is also lower in high-minority and lower-income areas, with medical infrastructure, insurance coverage, and other health resources distributed highly inequitably [137]. The persistence of large populations who are uninsured or underinsured has been a long-standing source of health inequity, resulting in disparities in COVID-19 outcomes among racial minorities and the poor [9, 145, 146]. For instance, minorities encountered longer travel times and thus reduced access to SARS-CoV-2 testing sites in the United States [107]. Accordingly, neighborhoods in Chicago with lower health insurance coverage were associated with higher COVID-19 mortality [17]. Low-income and minority populations are also more likely to work in essential services, are unable to work remotely and social distance, and thus experience elevated levels of exposure [32, 58, 62]. As such, New York City neighborhoods where daily movement and population outflow were unchanged by social distancing policies had the highest exposure density and were composed of a higher percentage of racial minorities and health care support workers as compared with neighborhoods where movement was limited by social distancing policies [58]. In addition, these neighborhoods had lower income and educational attainment, as well as higher unemployment rates and large household size [58]. ## EMERGING DIRECTIONS AND FUTURE RESEARCH Key emerging areas of inquiry and investigation are set to expand our understanding of the environmental dimensions of the COVID-19 pandemic. While environmental exposures may influence SARS-CoV-2 transmission, the pandemic has also impacted exposures. For instance, lockdown policies may lead to greater concentrations of residential indoor air pollution [96] and reductions in ambient concentrations [13]. Global economic shocks have likely increased energy insecurity, leading some to engage in risky behaviors to meet household energy needs, including the use of highly polluting biomass fuels for cooking or gas ovens for heating, and forego other basic needs [90]. Shifting time activity from workplaces, schools, and commercial spaces to residential environments has reignited interest in a fuller accounting of environmental exposures across indoor and outdoor environments. The growing availability of mobile device data indicates profound shifts in telework, shopping from home, and public transit usage [121], highlighting the need to reassess assumptions about time activity. These changes have yielded uncertain impacts on environmental exposures and will likely be the subject of research for years to come. Wastewater-based surveillance and epidemiology—to reveal areas of persistent infection and targets for vaccination efforts, for instance—are additional areas that will be potentially critical to long-term prevention and recovery efforts [128]. Wastewater surveillance systems have been established across the United States to monitor COVID-19 infection extent and variants in communities. Given the long-term risk of ongoing transmission in low- and middle-income countries, and the limited capacity of clinical surveillance networks, establishment of wastewater surveillance has particular public health potential in these settings. Though such approaches have been used to monitor for outbreaks of other diseases (e.g., poliomyelitis), further research will be needed to accommodate the scale of the COVID-19 pandemic, the threat of emerging variants, variable sewer quality and access, and challenges defining sewersheds. As vaccination coverage increases, climate may play a larger role in determining COVID-19 infection in comparison to contact dynamics, potentially leading to seasonal transmission patterns [7]. Other human coronaviruses and influenza exhibit pronounced seasonality that peaks in the winter months, driven by the cyclical nature of temperature, humidity, behavior, and waning immunity [94]. Laboratory experiments have shown that SARS-CoV-2 is similarly environmentally sensitive [25, 87, 95, 108, 113]. If SARS-CoV-2 immunity wanes at rates similar to those of other coronaviruses, one study predicts recurrent winter outbreaks [68]. Future research should address interactions between climate and immunity—including cross-immunity, seasonal cycles, and heterogeneity in the duration of immunity from multiple vaccines and previous infections—to inform the likelihood of seasonal circulation of SARS-CoV-2. The COVID-19 pandemic has emerged as climate change continues to influence temperature and precipitation patterns, induce extreme weather events, and force population displacement, leading to compounding public health crises. Shifting temperatures and precipitation patterns could have a profound effect on the future seasonality and spatial spread of SARS-CoV-2 and other viruses either directly or through effects on reservoir species. Moreover, climate change is a major driver of the increased frequency and strength of meteorological disasters, including flooding, hurricanes, and wildfires, leading to, for instance, an increasingly long and severe global wildfire season, which represents a growing source of acute air pollution exposure. Climatic extremes can lead to mass migration, resulting in crowding that can facilitate the spread of infectious diseases, including respiratory infections [30]. Extreme climatic events can also severely hamper health infrastructure, making vaccination against and treatment of COVID-19 and other communicable diseases more difficult [30]. Future research will be needed to characterize the impact of climate change on the spatiotemporal distribution of viruses and to identify areas where increasing frequency and duration of extreme events compound the risk of disease transmission. Finally, key uncertainties related to SARS-CoV-2 transmission itself limit a full understanding of the environment–COVID-19 relationship. With the emergence of variants of concern, it remains to be seen how environmental relationships change. Both B.1.1.7 and B.1.351 variants have shown higher receptor binding affinity [109] and could be less sensitive to environmental pressures, though this assumption has not yet been documented. Individuals with the B.1.1.7 variant may be infected for longer [67], potentially leading to more virus in wastewater, in indoor air, and on high-touch surfaces. The severity of B.1.1.7 and B.1.617.2 (delta) variant infections is thought to be increased as compared with previously circulating variants [23, 125], which may lead to compounding mortality risks for individuals who are highly exposed to environmental insults. Age is a critical risk factor for poor outcomes, yet we still do not have fully resolved estimates of age-dependent susceptibility and transmissibility. Similarly, immune age, a combination of immunosenescence and exposure history, may affect susceptibility to COVID-19 infection [123]. At the same time, vulnerability to environmental exposures changes over the life course [72, 73], and we do not know how timing of exposures will affect COVID-19 susceptibility and severity. ## CONCLUSIONS Public health efforts to reduce adverse environmental impacts on COVID-19 incidence and severity would yield substantial public health cobenefits, though much work remains to elucidate the relationships detailed in this review. Global declines in air pollution levels would reduce child mortality from lower respiratory infections; limiting workplace chemical exposures would reduce the risk of cancers; and a restructuring of the built environment could provide more efficient, safe public transit, reduce crowding, expand green space, and promote quality housing. These are highly desirable end points on their own merit, but they would also address risk modifiers for COVID-19 and serve as preventive measures for future pandemics. Adverse environmental exposures are distributed inequitably across geographic, racial, and economic strata, often higher in communities that also lack access to health resources. Special emphasis should be placed on directing assistance and resources to communities with a doubly high burden of environmental pollutants and low access to health, housing, nutrition, and other essential needs. Even further upstream, the emergence of SARS-CoV-2 has underlined the importance of our relationship with nature. Ongoing population growth, agricultural expansion, habitat destruction, wildlife trade, concentrated animal agriculture, and other major global changes are altering the risk of transmission of zoonotic pathogens [50]. Novel pathogens originating from wet markets, wildlife trade, poaching, farming, and suburbanization are consequences of unsustainable human expansion. 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--- title: Characteristics of Previous Tuberculosis Treatment History in Patients with Treatment Failure and the Impact on Acquired Drug-Resistant Tuberculosis authors: - Soedarsono Soedarsono - Ni Made Mertaniasih - Tutik Kusmiati - Ariani Permatasari - Wiwik Kurnia Ilahi - Amelia Tantri Anggraeni journal: Antibiotics year: 2023 pmcid: PMC10044547 doi: 10.3390/antibiotics12030598 license: CC BY 4.0 --- # Characteristics of Previous Tuberculosis Treatment History in Patients with Treatment Failure and the Impact on Acquired Drug-Resistant Tuberculosis ## Abstract Tuberculosis (TB) treatment failure is a health burden, as the patient remains a source of infection and may lead to the development of multi-drug resistance (MDR). Information from cases of treatment failure that develop into MDR, which is related to a history of previous TB treatment, in accordance with the pharmacokinetic aspect, is one important thing to prevent TB treatment failure and to prevent drug resistance. This was an observational descriptive study in an acquired MDR-TB patient who had a prior history of treatment failure. A structured questionnaire was used to collect information. The questionnaire consisted of a focus on the use of TB drug formulas during the treatment period, as well as when and how to take them. This study included 171 acquired MDR-TB patients from treatment failure cases. An amount of 64 patients received the separated TB drug, and 107 patients received the fixed dose combination (FDC) TB drug. An amount of 21 ($32.8\%$) patients receiving separated TB drug and six ($5.6\%$) patients receiving FDC TB drug took their drug in divided doses. In addition, three ($4.7\%$) patients receiving separated TB drug and eight ($7.5\%$) patients receiving FDC TB drug took their drug with food. An amount of 132 out of 171 ($77.2\%$) patients had a history of incorrect treatment that developed into MDR-TB. Education on how to take the correct medication, both the separate version and the FDC TB drug, according to the pharmacokinetic aspect, is important before starting TB treatment. ## 1. Introduction Tuberculosis (TB) is a communicable disease that is one of the leading causes of death worldwide. An estimated 10.6 million people hadTB in 2021, an increase of $4.5\%$ from 10.1 million in 2020. Indonesia is rank 2nd, representing $9.2\%$ of its population, which accounts for two-thirds of the global TB cases in 2021 [1]. Drug-resistant TB (DR-TB) continues to be a public health threat [2]. Resistance to rifampicin (RIF), the most effective first-line drug, is of greatest concern. Resistance to RIF and isoniazid (INH) is defined as multidrug-resistant TB (MDR-TB) [3]. Both MDR-TB and rifampicin-resistant TB (RR-TB) require treatment with second-line drugs. Globally, in 2021, there were an estimated 450,000 MDR/RR-TB cases. Indonesia is one of seven countries with the highest burden in terms of numbers of MDR/RR -TB cases, and that accounted for two-thirds of global MDR/RR -TB cases in 2021. The estimated proportion of people with TB who had MDR/RR -TB was $3.6\%$ among new cases and $18\%$ among those previously treated [1]. A previous study in Indonesia reported that 433 MDR-TB patients were from seven ($1.7\%$) new cases, 165 ($38\%$) treatment failures, 160 ($37\%$) relapses, 91 ($21\%$) returns after loss to follow-up, and 10 ($2.3\%$) other cases [4]. Previously treated cases are TB patients who have received one month or more of anti-TB drugs in the past. They are further classified by the outcome of their most recent case of treatment. One of the previously treated TB cases was treatment failure, which is defined as a TB patient whose sputum smear or culture is positive at month five or later after treatment [5,6]. TB treatment failure was reported as the risk factor for development of MDR-TB in a previous study [7]. The treatment for pulmonary TB can be inadequate due to various causes, including an incorrect medication method, which is not in accordance with the aspects of pharmacokinetics [8]. One of the factors reported to be the cause of treatment failure, as well as the occurrence of drug resistance, was a history of non-standard and inadequate TB treatment. Inadequate treatment causes treatment failure and recurrence, which are some of the causes of drug resistance [9,10,11]. First-line TB drugs, such as RIF, INH, ethambutol, and pyrazinamide, based on their pharmacokinetics, are concentration-dependent in activity, where the drug should be taken once a day at the correct dose to obtain the optimal Cmax and Cmax/AUC, which mean that the drug is taken once daily, and not two or three times a day. This type of regiment is called divided dose [12,13]. The interactions between food and drugs could reduce the bioavailability of anti-TB drugs, especially for RIF and INH. TB patients are endorsed to take anti-TB drugs at fasting condition to avoid therapeutic failure due to reduced blood concentrations [14]. Poor compliance with Directly Observed Treatment Short-course (DOTS) guidelines and inadequate care delivery that result in treatment failure and relapse are major causes of drug resistance in tuberculosis [15]. DOTS is a specific strategy, endorsed by the World Health Organization (WHO), to improve adherence by requiring health workers, community volunteers, or family members to observe and record patients taking each dose [16,17]. The main aspect of the DOTS strategy is direct supervision of the process of taking medication as it relates to drugs that are always available. Therefore, the adherence is guaranteed when taking TB drugs during the treatment period. In Indonesia, medication supervision is conducted by patients’ family member who have been given education concerning when the patient will start treatment, and this is not done by a nurse or technician [18]. However, although the drugs are taken regularly, the administration of anti-TB drugs, without considering the aspects of pharmacokinetics, can lead to treatment failure and drug resistance. Based on pharmacokinetics aspect, there were two types of drugs available for TB treatment in the community: the separated drug and the FDC TB drug formula. FDC was a drug package that contains certain active drug components [19]. The formulas of FDC TB drugs are listed as RIF, INH, pyrazinamide, and ethambutol. They are called a 2 FDC TB drug if they contain RIF and INH, and they are called 3 FDC if they contain RIF, INH, and pyrazinamide. They are called 4 FDC if they contains RIF, INH, pyrazinamide, and ethambutol [20]. 4 FDC was given during the first two months during the intensive phase, and 2 FDC was given during four months, following the continuation phase [21]. This study was conducted with adult patients. Hence, the FDC TB drug was meant for these patients, and the FDC TB drug was taken during TB treatment. WHO recommendations adopted by the TB sub-directorate of the Indonesian Ministry of Health use the FDC TB drug in TB treatment services [22], but unfortunately there may not be coordination regarding how many FDC TB drug should be provided and distributed to all health services. In Indonesia, there are 3 level public health facility. Primary health care only provide an initial services and not specialized. Secondary health care provided an initial services and several specialize services. Tertiary health care is a complete specialized services. Primary Health Care in Indonesia is a public health facility who give primary services including TB treatment based on DOTS TB program [23]. Tuberculosis treatment failure is a health burden as the patient remains of infection in the community and it may lead to the development of multidrug resistance [24]. It is important to prevent the emergence and transmission of drug-resistant TB because the second line drugs are less effective, have toxic side effects, and require extended treatment. Moreover, treatment failure subsequently leads to higher mortality rates [25]. To improve treatment outcomes for TB especially in Drug-Sensitive TB (DS-TB), efforts to reduce treatment failure are necessary [26]. Information from Acquired MDR-TB patients from treatment failure cases related to the history characteristics of previous treatment is important for strengthening the TB control program through DOTS to prevent TB treatment failure and drug resistance. This study was conducted to evaluate the characteristic of previous drug-intake history related to how drug-intake TB patients with outcome of treatment failure who were developed drug resistance. ## 2. Results After selection regarding to flow chart of the inclusion of study subjects, This study included 171 Acquired MDR-TB patients from new TB cases who have treatment failed with first-line standard anti-TB regimen, with mean age of 44 years old from 94 ($55\%$) men and 77 ($45\%$) women. In addition, patients who have comorbid controlled Diabetes Mellitus were 70 of 171 ($40.9\%$). Sites of previous TB treatment were reported in Primary Health Care were 112 ($65.5\%$) patients, hospital were 24 ($14\%$), private clinic were 23 ($13.5\%$), independent general practitioner were 7 ($4.1\%$), and the rest were independent medical specialist were 5 ($2.9\%$) patients. The characteristic of study subjects was presented in Table 1 below. Table 2 showed there are 2 kind of TB medication formula: Separated TB drug and FDC TB drug. Patients who received Separated TB drug were 64 ($37.4\%$) and FDC TB drug were 107 ($62.6\%$). In separated drug, patients who taken in divided doses were 21 of 64 ($32.8\%$) and 43 of 64 ($67.2\%$) were taken the Anti TB-drugs all at once. In FDC TB drug, patients who taken in divided doses were 6 of 107 ($5.6\%$) and 101 of 107 ($94.4\%$) were taken the Anti TB-drugs all at once. Mostly, patients who were given anti-TB drug with separated TB drug in divided doses were patients in private clinics amount 11 ($17.2\%$). Also, the most patients who were given anti-TB drug with separated TB drug taken all at once were patients in Primary Health Care amount 24 ($37.5\%$). Furthermore, patients who were given anti-TB drug with FDC TB drug taken in divided doses were patients in Primary Health Care amount 4 ($3.7\%$) and the most patients who were given with FDC TB drug taken all at once were patients in Primary Health Care amount 82 ($76.6\%$). Table 3 showed patients who received separated TB drug taken with food were 3 of 64 ($4.7\%$) and 61 of 64 ($95.3\%$) were taken ≥2 h before/after food. For patients who received FDC TB drug, 8 of 107 ($7.5\%$) taken Anti TB-drug with food and 99 of 107 ($92.5\%$) were taken ≥2 h before/after food. The most patients who were given anti-TB drugs with separated TB drug taken with food were patients in private clinics amount 2 ($3.1\%$). Patients who were given anti-TB drugs with separated TB drug taken ≥2 h before/after food were patients in Primary Health Care amount 26 ($40.6\%$) mostly. Moreover, patients who were given anti-TB drugs with FDC TB drug taken with food were patients in Primary Health Care amount 6 ($5.6\%$) and the most patients who were given anti-TB drugs with FDC TB drug taken ≥2 h before/after food were patients in Primary Health Care amount 80 ($74.8\%$). Table 4 showed education about how to take the medicine by the health worker was commonly reported in 105 ($61.4\%$) patients treated in the Primary Health Care, followed by hospital was 22 ($12.9\%$) patients, private clinic were 21 ($12.3\%$) patients, independent general practitioner were 7 ($4.1\%$) patients, and independent medical specialist were 5 ($2.9\%$) patients. 7 ($4.1\%$) patient treated in the primary healthcare and 2 ($1.2\%$) patients respectively who treated in hospital and private health care did not explained by the health worker about how to take the medicine. ## 3. Discussion Though since 1994, WHO and the International Union against Tuberculosis and Lung Disease (IUATLD) have recommended the use of FDC TB drug as anti-TB therapy. This is to simplify the therapy, increase the compliance, and prevent the inadvertent medication errors [27]. Separated TB drug only for certain patients such as patients who experience side effects [28]. Most frequent causes correlated with TB resistance included non-implementation of DOTS and other important risk factors are correlated with inadequate drug intake by patients, quality of the drugs, and others [29]. Besides, the acquired resistance could be affected by drug malabsorption [30]. Under standard doses due to the wrong way of drug-intake though regularly will cause treatment failure and continue to become drug-resistance. This study included 171 Acquired MDR-TB patients who have treatment failed in their previous TB treatments regularly. This study found that the administered of Separated TB drug were 64 patients. This is not in accordance with WHO recommendations that have been adopted in Indonesia. Separated TB drug was given by all the health service sites and the site that provided the most separated drugs was non-Primary Health Care sites that is equal to 38 of 64 ($59.4\%$). In this study, we were not ask about why the drugs given separately, because the questionnaire was only aimed to patients. Overall, every sites who gave the drug separately for patients who taken in divided doses were 21 of 64 ($32.8\%$). This may be because education on how to take medicine through DOTS training or in the medical education curriculum related to TB treatment is not included. Even so, in the Primary Health Care there were still 26 ($40.6\%$) patients who given separated TB drug were 2 ($3.1\%$) patients received in divided doses. In addition, there were 86 patients who received FDC TB drug, but 4 ($4.6\%$) patients received in divided doses. 4 patients who received FDC TB drug in divided doses at Primary Health Care dominated compared to other health service sites. In the countries with high burden of TB where DOTS strategy is not implemented well, TB treatment in private sector is reported as the risk factor for MDR-TB. Failure of anti-TB treatment in the private sector was grouped at moderate risk of resistance and MDR-TB [29]. Indonesia is one of many countries with high burden TB and DOTS strategy is still focus only on Primary Health Care. As an illustration, those who have carried out Drug Sensitive TB (DS-TB) notifications as a component of implementing the DOTS strategy by facility Health Services are Primary Health Care estimated approximately $65\%$, Public hospital $20\%$, and private hospitals $15\%$ [31]. In this study, patients who take the medication correctly (FDC TB drug or Separated TB drug in all at once and ≥2 h before/after food) were only 39 of 171 ($22.8\%$) patients. Of the 64 patients received Separated TB drug, 3 ($4.9\%$) patients taken the medicine with food which given by private clinic and independent medical specialist, while of the 107 patients received FDC TB drug, 8 ($7.5\%$) patients taken the medicine with food which mostly given by Primary Health Care amount 6 ($5.6\%$) and the rest was hospital amount 2 ($1.9\%$). This condition when wrong time medication error can be the cause of resistance in such a way that when the FDC TB drug was taken on divided doses chances of resistance becomes several folds [32]. The interactions between food and drugs could reduce the bioavailability of anti-TB drugs, especially for RIF and INH. TB patients should be endorsed to take anti-TB drugs at fasting condition to avoid therapeutic failure due to reduced blood concentrations [14]. A study in India reported that food lowered anti-TB drug concentrations significantly and delayed absorption [33]. As mentioned above, the quality of the drug is also decisive but in this study, the quality of drug which given to patients were not analyzed. Other factors may play a role in the occurrence of treatment failure and development of drug resistance. Isoniazid as the key drug of first-line anti-TB treatment, exerts potent early bactericidal activity (EBA). Antimicrobial activity is well-correlated with INH exposure. The wide inter individual variability (IIV) seen in the response to INH could lead to suboptimal concentrations in some patients, resulting in treatment failure and a risk of drug resistance. INH metabolism is also influenced by the genetic polymorphism of N- acetyltransferase 2 (NAT2), which contributes to the high IIV in INH clearance and concentration. NAT2 genetic polymorphisms can be classified into 3 phenotypes: rapid, intermediate, and slow acetylators. The population of Indonesia has a large proportion of slow acetylators. Some NAT2 phenotypes are associated with a higher risk of adverse drug reactions (ADRs) to INH and treatment failure [32]. Rapid acetylator were significantly more frequent in patients with MDR-TB, appropriate dose adjustment of INH is important to prevent treatment failure and acquired drug resistance [34]. The guidelines for using maximal doses INH in Indonesia were 300 mg. In this study, the acetylators status of study subjects were not analyzed. Another risk factor that impact to failure treatment was MTB strain. This factor could not be neglected especially for some various of MTB strains such as Beijing genotype strains. Previous study reported that MTB Beijing genotype 1.9–3.6 times significantly associated with an increased risk of treatment failure [35]. Study was conducted in Indonesia showed that MTB Beijing genotype strains was less susceptible to TB treatment, though there was no resistance to anti TB drug [36]. This study was not analyzed the genotype strains which may cause failure TB treatment. Education how to take the drug before starting treatment was important. Medication supervisor has important role for the implementation of DOTS in TB treatment. Unfortunately, WHO guidelines only mentioned that the TB medicine combination should be taken regularly in the same time and didn’t mention about timing exactly according to meal’s time. Questionnare of this study enquired that health services site who did not provide education to patients about how to take TB drug medicine correctly were Primary Health Care was 5 ($2.9\%$) patients, followed by Hospital was 4 ($2.3\%$) patients, and private clinic as well as independent general practitioner amount 1 ($0.6\%$) patient respectively. 161 ($94.2\%$) patients have medication supervisor, while 10 ($5.8\%$) patients have no medication supervisor. Comorbid conditions associated with malabsorption was also reported to be risk factor for drug resistance [24,29]. In HIV disease, the small intestine is frequently impacted either by opportunistic enteric infections that cause intestinal dysfunction or by the HIV virus itself, which causes malabsorption of the majority of nutrients [37,38]. TB patients with HIV that are commonly with malabsorption have been excluded in this study to avoid biased results. This study found that 42 ($24.6\%$) patients experienced adverse event causing vomiting anti-TB drugs and 70 ($40.9\%$) patients with DM (Table 1). ADRs to first-line anti-TB drugs are common. Moreover, these ADRs result in interruption or revision of the anti-TB treatment that could cause treatment failure and mortality in TB patients [39]. In this study, the occurrence of adverse effects did not lead to discontinuation of treatment. TB patients with uncontrolled DM have a higher risk of treatment failure and TB drug resistance [26,40]. Patients with uncontrolled DM have been excluded in this study to avoid biased results. In DS TB with impaired kidney function and liver disorders, there needs to be a change in the standard TB regimen. Therefore in this study patients with renal failure or impaired liver function were excluded. The presence of primary mono or polydrug-resistant TB is one of factors associated with treatment failure if only given first-line regimen standard [17]. In this current study, although all study subjects in their previous TB treatment were diagnosed by Xpert MTB/RIF with RIF resistance not detected but this does not rule out the possibility of the presence of primary mono or polydrug-resistant TB. In national TB program adopted from WHO TB guideline, It applies that new TB patients, if GeneXpert results showed RIF sensitive, are immediately given a first-line regimen standard without further confirmation whether there is resistance in other anti-TB drug, especially INH. A previous study in new TB patients with rifampicin susceptible TB (RS-TB) detected by Xpert MTB/RIF reported that INH resistance was detected in 4 ($7.4\%$) using first-line line probe assay (FL-LPA) and 5 ($9.3\%$) using culture-based drug susceptibility test (DST). RIF resistance was also found in 1 ($1.9\%$) using FL-LPA and 2 ($3.7\%$) using culture-based DST [41]. In the subjects of this study, further sensitivity tests were not carried out, especially to INH and also to Ethambutol and Pyrazinamide. This study has some limitations. It was not all of the TB patients diagnosed initially by microbiological examination (molecular rapid test). The proportion of microbiology-positive and clinical radiology test of TB patients was not evaluated, but all patients diagnosed and treated at health primary care had $100\%$ performed according to regulation, while those at non-Primary Health Care varied (some used molecular rapid test and some did not) and not all the sites implemented DOTS program. ## 4.1. Study Design and Ethical Statement This was an observational descriptive study in Dr. Soetomo General Academic Hospital Surabaya and Ibnu Sina General Hospital Gresik that was conducted from May 2021 to October 2021. The samples were all Acquired pulmonary MDR-TB patients from new TB cases who have treatment failed with first-line standard anti-TB regimen (4 FDC). Acquired pulmonary MDR-TB patients from new pulmonary TB cases who have treatment failed with first-line standard anti-TB regimen per definition are new case pulmonary TB patients diagnosed based on GeneXpert examination showing positive Mycobacterium tuberculosis (MTB) and RIF Resistance not detected, and or positive clinical radiology to TB diagnosis, then given standard treatment regimen first line TB drugs and sputum smear or culture is positive at month 5 or later during treatment as well as GeneXpert examination in those time showed positive MTB RIF resistant detected. For the confirmation, the next examination by culture/sensitivity test forward showed RIF and INH resistant (MDR-TB). New TB cases was defined as TB patients who have never had TB before nor received TB treatment who receive standard treatment but do not recover and instead become MDR TB at the end of the treatment duration. This study was approved by the ethics committee with ethical clearance number 83/EC/KEPK/FKUA/2022 on 19 May 2022. This study was conducted in accordance with the Declaration of Helsinki. All participants included had given their written informed consent to participate in this study. ## 4.2. Data Collection A structured questionnaire was used to collect information by interviewing the subjects as respondents who have signed the informed consent. The questionnaire was developed from any validated and previously published articles, and were added from the experiences of physician when providing medical services [10,11,12]. The questionnaire had 22 questions including demographic profile 11 questions and the administration of drug amount 11 questions. Interview was conducted by the physician, peer educators, and patients’ supporters who were trained before collecting data. Completed questionnaires are input by the research assistant and double checked by the investigators. The questionnaire focused on the use of TB drug formulas during the treatment period, when and how to take them, In addition, demographic patients and health service site for treatment. Patients with uncontrolled diabetes mellitus (DM), HIV infection, renal failure, liver disease, and TB patients whose considered to have taken TB medication irregularly were excluded. Data was entered and presented as a table. Figure 1 is the flow chart of the inclusion of study subjects. ## 5. Conclusions Acquired MDR-TB from treatment failure history cases of patients in Primary health care contributed wrong administration to take the drug mostly. 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--- title: Investigating the Effect of Pulicaria jaubertii as a Natural Feed Additive on the Growth Performance, Blood Biochemistry, Immunological Response, and Cecal Microbiota of Broiler Chickens authors: - Abdulrahman S. Alharthi - Nawaf W. Alruwaili - Hani H. Al-Baadani - Maged A. Al-Garadi - Ghalia Shamlan - Ibrahim A. Alhidary journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044572 doi: 10.3390/ani13061116 license: CC BY 4.0 --- # Investigating the Effect of Pulicaria jaubertii as a Natural Feed Additive on the Growth Performance, Blood Biochemistry, Immunological Response, and Cecal Microbiota of Broiler Chickens ## Abstract ### Simple Summary Pulicaria jaubertii is considered a medicinal plant traditionally used to treat various human diseases because it contains many biologically active compounds, including monoterpenoids, sesquiterpenoids, diterpenoids, flavonoids, and phenols, which could have beneficial effects on broiler performance and health, although there are no studies on the use of this plant in broilers. The aim of this study was to investigate the effect of Pulicaria jaubertii (3–9 g Pulicaria jaubertii powder/kg basal diet) on the performance, blood biochemistry, internal organs, gene expression related to immune response, and cecal microbiota of broiler chickens. We conclude that Pulicaria jaubertii has a positive effect on overall performance, immune response, and microbiota. ### Abstract Based on the biologically active compounds of Pulicaria jaubertii studied so far, there are no studies on the use of this plant in broilers. Therefore, the present study aims is to investigate the effect of Pulicaria jaubertii on the performance, blood biochemistry, internal organs, gene expression related to immune response, and the cecal microbiota of broiler chickens. A total of two hundred and forty male broilers were used and divided into four diet groups (T1 = 0, T2 = 3, T3 = 6, and T4 = 9 g Pulicaria jaubertii powder/kg basal diet). The performance evaluation, serum biochemical parameters, internal organ indicators, cytokines’ gene expression, and microbiota colonization were determined. The study results showed that this plant was rich in nutrients, some fatty acids, and bioactive phenolic compounds. All growth performance indicators and relative liver weight were improved by Pulicaria jaubertii levels (T2 to T4) with no effect on feed intake. T3 and T4 showed higher total protein and lower triglycerides and total cholesterol. Birds fed Pulicaria jaubertii showed immune regulation through the modulation of pre-inflammatory cytokines and increased mucin-2 and secretory Immunoglobulin A compared with the control group. Diet groups (T2 to T4) had higher quantities of Lactobacillus spp. and lower levels of Salmonella spp. than the control group. We conclude that Pulicaria jaubertii could be used as a feed supplement for broilers due to its beneficial effects on overall performance, immune response, and microbiota. Further studies are recommended to investigate the potential mechanism of Pulicaria jaubertii in broilers. ## 1. Introduction In recent years, several studies have shown that the addition of aromatic medicinal plants as natural feed additives in broiler diets can improve growth performance, health, nutrient digestibility [1,2], innate immunity [3], and antioxidant status [4]. These natural additives depend on the plant part used (e.g., seed, leaf, root, or bark) and the processing technique (e.g., extraction with non-aqueous solvents) to alter the active compounds in the final product. In addition, supplementation with aromatic medicinal plants alters normal gut microflora and modulates immune responses [5]. However, the use of aromatic medicinal plants as growth promoters and antimicrobial agents is becoming increasingly important as an alternative to antibiotics [6]. Due to the development of resistant bacteria, the use of antibiotics in a diet as a subtherapy to improve the performance and health status of broiler chickens has been banned recently, starting with the European Union in 2006 [7,8]. Munyaka et al. [ 9] reported that the use of aromatic medicinal plants as an alternative to antibiotics in poultry could improve growth performance and immune system integrity. A study by Pirgozliev et al. [ 2] reported that a commercial medicinal plant mixture improved overall growth performance, nutrient retention, and intestinal cytokine expression in broiler chickens. In addition, Cho et al. [ 10] indicated that the medicinal plants improved growth performance and inhibited pathogenic bacteria (Clostridium perfringens and Escherichia coli) in the small intestine of broiler chickens. Renewed interest in the search for new dietary supplements from natural plant sources has attracted worldwide attention in recent years. Pulicaria jaubertii, also known as Anssif or Alkhaw’ah, is an aromatic medicinal plant of the Asteraceae family that is widely distributed in southern Saudi Arabia and Yemen [11]. Pulicaria jaubertii is a medicinal plant traditionally used to treat various human diseases, including inflammation, colds, diuretics, and digestive disorders [12,13]. Al-Maqtari et al. [ 14] and Mohammed et al. [ 15] showed that Pulicaria jaubertii extract has potent antioxidant, antimicrobial, and anti-inflammatory effects. It is also used as a food supplement [16]. According to Hussein et al. [ 17], food and pharmaceutical industries could use Pulicaria jaubertii oil as a safe, natural substitute for synthetic antioxidants. El-Ghaly et al. [ 18] reported that Pulicaria jaubertii extract has antihypertensive activity due to its content of flavonoids and phenols. In another study, biologically active compounds were found in Pulicaria jaubertii, including monoterpenoids, sesquiterpenoids, diterpenoids, flavonoids, and phenols [17]. However, the methanolic extract of Pulicaria jaubertii has antioxidant, antifungal, and antibacterial activities [19]. The results of these studies may further support the supplementation potential of Pulicaria jaubertii for future applications in broiler diets. To date, there have been no studies on the use of Pulicaria jaubertii in broiler diets. However, based on this plant’s previously studied biologically active compounds, it could have a positive effect as a growth promoter and on gut health by modulating the immune response and microbiota of broilers. Therefore, this study aimed to investigate Pulicaria jaubertii powder’s effects as a whole plant on the performance, blood biochemistry, internal organs, immune response-related gene expression, and microbiota of broiler chickens. ## 2. Materials and Methods The whole plant (flowers, seeds, leaves, and stems) of Pulicaria jaubertii was collected in the valleys of the city of Ibb in the Republic of Yemen. The plant was classified in the herbarium of the College of Science-King Saud University (NO: 24544) and ground into a fine powder for broiler diets. ## 2.1. Ethical Reference The Scientific Research Ethics Committee (SREC) of King Saud University, Saudi Arabia, approved all the methods and techniques used in this study (Ethical Reference No: KSU-SE-21-47). ## 2.2. Chemical and Bioactive Composition of Pulicaria jaubertii An approximate analysis of Pulicaria jaubertii samples was performed to determine the content of dry matter, crude protein, ash, crude fiber, and total fat according to the methods of the Association of Official Analytical Chemists [20]. Fatty acid profiles were determined after extraction of total fat and analysis by gas chromatography-mass spectrometry (Agilent Technologies, Palo Alto, CA, USA) according to the method of Biesek et al. [ 21]. Fatty acids were expressed as g/100 g of identified fatty acid methyl esters. To identify the main bioactive compounds, they were extracted from Pulicaria jaubertii using a methanol solution and then analyzed by gas chromatography-mass spectrometry (Agilent Technologies, Palo Alto, CA, USA) according to the method described by Hussein et al. [ 17]. To identify the different chemicals, their relative retention times were compared with those of the authentic samples, and their peak-to-peak mass spectra were compared with those of the authentic samples and presented as percentages. ## 2.3. Birds, Study Design, and Housing A total of two hundred and forty (one-day-old) male broiler chickens (Ross 308) were used for this study. All birds were weighed individually (BW = 43.94) and randomly assigned to four diet groups with ten replicates per group (six birds per replicate). The diet groups (T1, T2, T3, and T4) were supplemented with Pulicaria jaubertii powder (whole plant) at 0, 3, 6, and 9 g per kg of the basal diet (control group) for 35 days. The basal diet was formulated as a mash according to the nutrient requirements of the Ross 308 Management Guide recommendations (Aviagen, 2019, New York, NY, USA) at three feeding stages: starter stage (1 to 10 days), grower stage (11 to 24 days), and finisher stage (25 to 35 days) (Table 1). All optimal environmental conditions, including temperature, humidity, and lighting, were maintained according to Ross strain guidelines. The study was conducted in a chamber with electrically heated cages, where the temperature and humidity were set at 33 °C and $50\%$ on the first day and then gradually decreased (2 °C/3 days) to 22 °C and $50\%$ after 27 days. The lighting program was offered for 24 h (30–40 lux) until 7 days of age and for 23 h (minimum 20 lux) after 7 days. Feed and water were offered ad libitum to the birds during the study period. All birds were vaccinated against ND, IB (at 5 and 22 days of age), and against IBDV (at 14 days of age) according to the manufacturer’s instructions (Fort Dodge Animal Health, Fort Dodge, IA, USA). ## 2.4. General Performance Evaluation Live weight and feed intake were measured throughout the study period. Daily weight gain (final live weight-initial live weight/35 days), daily feed intake (feed provided-residual feed/35 days), and feed conversion ratio (daily feed intake/daily weight gain) were calculated according to El-Ratel et al. [ 22]. In addition, the production efficiency index ((livability × live weight)/(35 days × feed conversion ratio) × 100) and performance index (live weight growth/feed conversion ratio × 100) were calculated according to Goiri et al. [ 23]. ## 2.5. Sampling and Blood Analysis At 35 days of age, blood samples were collected from 10 birds in each dietary group after 4 h of feed deprivation in tubes without EDTA via brachial vein to measure biochemical parameters. Serum was separated in a centrifuge at 3000× g for 20 min. Serum samples were kept frozen at −20 °C until the biochemical parameters were analyzed. Serum biochemical parameters such as total protein, albumin, glucose, triglycerides, total cholesterol, and high-density lipoprotein were analyzed using colorimetric kits (Randox Laboratories Limited, London, UK) with an automated spectrophotometric analyzer (Chem Well, Awareness Technology, Palm City, FL, USA). According to Albaadani et al. [ 24], serum low-density lipoprotein (triglycerides-high density lipoprotein-(triglycerides/5)) and globulin (total protein-albumin) concentrations were determined. ## 2.6. Internal Organs Indicators At 35 days of age, 10 birds per dietary group were selected for slaughter. The carcass of the birds was opened. All internal organs such as the thymus, bursa, spleen, liver, heart, pancreas, and kidneys, were weighed and then calculated as percentage of live weight [25]. ## 2.7. Sampling and Gene Expression One centimeter of jejunum (middle) tissue was collected (35 days old). All tissues from each dietary treatment were washed directly (phosphate-buffered saline), placed in a sterile collection tube containing RNA later solution (Qiagen, Hilden, Germany), and snap-frozen and stored at −80 °C for later quantification of gene expression. Total mRNA was extracted using an mRNA extraction kit (Quick-RNA Miniprep, Zymo Research, Irvine, CA, USA). The quality and quantity of mRNA were checked using a Nanodrop spectrophotometer (Thermo Scientific, NANODROP 2000, Waltham, MA, USA). The mRNA was converted to complementary DNA using the Reverse Transcription Kit (Applied Biosystems, Thermo Fisher Scientific, Oxford, UK) through a PCR system (BIO-RAD, T100 Thermal Cycler, Singapore, Singapore). Gene expression of the cDNA samples was performed by real-time quantitative PCR (7300 Real-Time PCR System, Applied Biosystems, Foster City, CA, USA), using primers to determine the expression of TNF-α, IL-1β, IL-4, IL-6, IL-2, IL-10, INF-Y, sIgA, MUC-2, and β-actin genes (Table 2; by Basic Local Alignment Search Tool) and addition of SYBR Green PCR Master Mix (Applied Biosystems, Thermo Fisher Scientific, Foster, CA, USA). The fold change in gene expression for each target gene was calculated using the 2−ΔΔCt method compared to the control group [26]. ## 2.8. Cecal Microbiota Cecal digesta samples were collected from 10 birds from each diet group in a sterile collection tube and stored at −20 °C to determine microbiota colonization [27]. Colonies were clear and easy to count (50 to 300 colonies). A total of 10 μL were grown on specific media for the bacterial species studied. Lactobacillus spp. on MRS agar (Himedia, Mumbai, India), Clostridium perfringens on BHI agar (Oxoid, Milan, Italy), anaerobic and aerobic bacteria on plate counter agar (Himedia, Mumbai, India), and Salmonella spp. and Escherichia coli on EMB (Hardy Diagnostics, Santa Maria, CA, USA) were counted using a colony counter. Results were expressed in log10 colony forming units per gram (log10 CFU/1 g digesta). ## 2.9. Data Analysis Statistical analysis of data for all parameters was performed using SAS software [28], based on one-way analysis of variance with a completely randomized design. The following statistical model was used: Observed values (Yij) = data mean (μ) + diet groups (Ti) + random error (eij). Normality of the data (skewness, kurtosis, and boxplot), a statistical difference ($p \leq 0.05$), and differences between diet group means were examined with the Duncan multiple range test. Means of all parameters for each diet group were expressed as mean ± standard error of the means (SEM). ## 3. Results The chemical composition and fatty acid profile of Pulicaria jaubertii are shown in Table 3. The results of the approximate analysis showed that Pulicaria jaubertii (whole plant) was rich in dry matter ($92.84\%$), crude protein ($15.52\%$), ash ($17.19\%$), and crude fiber ($35.55\%$). In addition, the fatty acid profile of Pulicaria jaubertii contains saturated fatty acids ($50.91\%$) and unsaturated fatty acids ($48.46\%$) of the total fat ($2.80\%$) in the Pulicaria jaubertii plant. Saturated fatty acids such as palmitic acid ($20.31\%$), tridecylic acid ($14.91\%$), and stearic acid ($9.59\%$) and unsaturated fatty acids such as oleic acid ($20.48\%$), linolelaidic acid ($14.08\%$), and linoleic acid ($9.31\%$) were the most abundant residues and represented more than $88.68\%$ of the total fat. The gas chromatography-mass analysis of the major bioactive compounds in the extract of Pulicaria jaubertii is shown in Table 4. The present study revealed that Pulicaria jaubertii contains 22 compounds. The phenolic compounds, including benzaldehyde thiosemicarbazone ($21.35\%$) and dimethoxy dimethylsilane ($16.35\%$) were found to be the major constituents and accounted for $37.70\%$ of the bioactive compounds in Pulicaria jaubertii (whole plant extract). The effects of the dietary groups on the overall performance of broiler chickens aged 1 to 35 days are shown in Table 5. The results obtained in the current study for performance indicators such as live weight, daily weight gain, production efficiency index, and performance index were higher when birds were fed different levels of Pulicaria jaubertii (T2 to T4) than in the control group (T1; $p \leq 0.05$). Daily feed intake was not affected by dietary groups ($p \leq 0.05$), while the feed conversion ratio improved by T2 to T4 compared to T1 ($p \leq 0.05$). The effects of the dietary groups on serum biochemical measurements of broiler chickens are shown in Table 6. The diet supplemented with 9 g of Pulicaria jaubertii/kg of the basal diet (T4) had higher total protein content compared to T1 and T2 ($p \leq 0.05$), while albumin, globulin, and albumin to globulin ratio were not affected by the diet groups ($p \leq 0.05$). The different diet groups also did not significantly affect glucose concentration ($p \leq 0.05$). Birds fed 9 g of Pulicaria jaubertii/kg of the basal diet (T4) had lower triglyceride concentrations compared to T1 and the other groups ($p \leq 0.05$). Supplemental diet groups (T2 to T4) had lower total cholesterol concentrations ($p \leq 0.05$), while high-density lipoprotein and low-density lipoprotein were not significantly affected in supplemental diet groups compared to the control group ($p \leq 0.05$). The effects of the dietary groups on the relative weight of the internal organs of broiler chickens are shown in Table 7. The birds fed T2 to T4 had a higher relative weight of the liver compared with the control group ($p \leq 0.05$). The relative weight of the other organs, such as the thymus, bursa, spleen, heart, pancreas, and kidneys, were also not significantly affected among the diet groups compared to the control group ($p \leq 0.05$). The effects of dietary groups on gene expression of pre-inflammatory cytokines in the intestines of broiler chickens are shown in Figure 1. The fold change in the expression of IL-1β was decreased in birds fed with diet groups T2 to T4 compared with the control group, whereas IL-6 was increased ($p \leq 0.05$). Furthermore, when compared to the other groups, changes in the expression of IL-4, IL-6, and TNF-α were greater in birds fed T2. The change in expression of INF-Y was increased in T2 and T3, whereas IL-10 was decreased in T2 and T4 compared with the control and other groups ($p \leq 0.05$). The effects of dietary groups on mucin-2 protein (MUC-2) and secretory immunoglobulin A (SIgA) gene expression in the intestine of broiler chickens are shown in Figure 2. The current results show that the change in the expression of MUC-2 and SIgA was increased in birds fed with diet groups T2 to T4 compared with the control group ($p \leq 0.05$). In addition, birds fed T2 had a higher expression of SIgA than T4 and T3. The effects of the dietary groups on the microbiota in the caecum of broiler chickens are shown in Table 8. Diets supplemented with 3, 6, and 9 g of Pulicaria jaubertii/kg of the basal diet (T2 to T4) had a higher quantity of Lactobacillus spp. and a lower Salmonella spp. compared with the control group ($p \leq 0.05$). On the other hand, other microbiota in the cecum (anaerobes, Clostridium perfringens, aerobes, and Escherichia coli) were not significantly affected among the dietary groups compared to the control group ($p \leq 0.05$). ## 4. Discussion Renewed interest in the search for new dietary supplements from natural plant sources has gained worldwide attention in recent years due to the development of antibiotic-resistant bacteria [29]. These aromatic and medicinal plants have antimicrobial and anti-inflammatory effects that could be attributed to bioactive compounds (phenols, esters, alcohols, acids, and steroids) that have beneficial effects on broiler production and health [30,31]. The results of these studies could further support the supplementation potential of Pulicaria jaubertii for future applications in broiler feeds. In addition, there are no studies on the use of this plant as a feed additive for poultry. Pulicaria jaubertii is one of the aromatic and medicinal plants traditionally used to treat a variety of human diseases, including inflammation, colds, diuretics, and digestive disorders [12,13]. The results of the chemical analysis in the current study showed that Pulicaria jaubertii, as a whole plant, is rich in crude protein, ash, and crude fiber. In addition, Pulicaria jaubertii contains almost equal amounts of saturated and unsaturated fatty acids in the total fat. Saturated fatty acids such as palmitic acid, tridecylic acid, and stearic acid and unsaturated fatty acids such as oleic acid, linoleic acid, and linolenic acid were the most abundant acids. They accounted for more than $88.68\%$ of the total fatty acids. On the other hand, our study by gas chromatography-mass spectrometry revealed that Pulicaria jaubertii contained 22 compounds, including benzaldehyde thiosemicarbazone and dimethoxy dimethylsilane as phenolic compounds, which accounted for $37.70\%$ of the bioactive compounds. The results are consistent with several studies that confirmed that the extract of Pulicaria jaubertii contains biologically active compounds such as monoterpenoids, sesquiterpenoids, diterpenoids, flavonoids, and phenols [13,14]. Hussein et al. [ 17] found that the oil extract of Pulicaria jaubertii contains 53 volatile components, including sesquiterpene hydrocarbons, monoterpene hydrocarbons, and oxygenated monoterpenes. Several studies have reported the effect of medicinal plants on broiler chickens [32,33]. In this study, dietary supplementation with Pulicaria jaubertii improved the overall performance of broiler chickens, such as live weight, daily weight gain, feed conversion ratio, production efficiency index, and performance index compared to the control group during the study period. Elabd [34] found that dietary supplementation with pulicaria undulata powder as a medicinal plant (3 g/kg diet) gave the best results in the overall growth performance of broilers. This suggests that the effectiveness of dietary supplementation with Pulicaria jaubertii on growth performance may be due to the active compounds, fatty acids, and nutrients contained in it. In addition, Dotas et al. [ 35] hypothesized that the quantity of phytocomponents was responsible for improving the overall performance of broiler chickens. The better overall performance of chickens fed aromatic and medicinal plants such as Pulicaria jaubertii might be due to the presence of phenolic compounds and fatty acids, which stimulate the secretion of endogenous intestinal enzymes, thus improving nutrient digestion and gut passage rate [36]. On the other hand, broiler chickens fed medicinal plants may have developed an adaptive response to cope with increased dietary fiber, which ultimately improves digestion and absorption [5]. In addition, higher daily weight gain in broilers fed 3, 6, and 9 g Pulicaria jaubertii per kg of the basal diet (T2–T4) without affecting daily feed intake results in better nutrient utilization, as reflected by the improved feed conversion ratio. Production efficiency and performance index are often used as indicators of the production economic status, performance, health, and welfare of broilers [37,38]. Thus, a higher value of production efficiency and performance index indicates that broiler chickens receiving Pulicaria jaubertii have consistent body weight gain and are in good health [39]. Dietary supplementation with aromatic and medicinal plants could improve the secretion of intestinal enzymes [40], intestinal morphology [41], and enhance immune function [42] in chickens. Changes in the serum biochemical profile are metabolic indicators of health and nutritional status [43]. Our study showed that total protein concentration increased in T3 and T4, while total cholesterol concentration decreased due to Pulicaria jaubertii and low triglyceride concentration in T4 compared to the control group (T1). Other serum biochemical indicators were also unaffected by the supplemental diet groups. The increase in total protein concentration may indicate that the Pulicaria jaubertii levels enhance body protein anabolism in the chickens. The decrease in triglycerides and total cholesterol could be due to disruption of bile acid absorption in the intestine (impairment of bile acid circulation) by Pulicaria jaubertii supplementation, resulting in lower serum total cholesterol concentration. In addition, the high fiber content of this plant may play a role in the lowest lipid levels [44]. Another study by Elabd [34] reported that this decrease in total cholesterol concentration could be due to the bioactive compounds of Pulicaria jaubertii, which are able to inhibit hepatic 3-hydroxy-3-methylglutaryl coenzyme A activity, an important regulatory enzyme in cholesterol synthesis. The results of our study showed that Pulicaria jaubertii had no effect on the relative weight of internal organs (thymus, bursa, spleen, heart, pancreas, and kidneys), while T2 and T3 increased the relative weight of the liver compared to the control group. Immune cells play a critical role in the secretion of proteins known as cytokines, such as interleukins (ILs), interferon-gamma (IFN-Y), and tumor necrosis factor-alpha (TNF-α), during immune responses by activating and regulating other cells and tissues [45]. Cytokines also serve as the first warning of potential threats to the immune system and are considered a communication point between innate and adaptive responses [46]. The results showed that 3 g of Pulicaria jaubertii per kg of the basal diet (T2) induced immune activation and regulation via increased fold changes in the gene expression of pre-inflammatory cytokines such as IL-4, IL-6, IL-12, TNF-α, and INF-Y and decreased fold changes in the gene expression of IL-10 compared to the basal diet. This may be due to the beneficial effects of Pulicaria jaubertii in increasing immune cell activity and improving gut health. According to Al-Gabr et al. [ 11] and Mohammed et al. [ 15], Pulicaria jaubertii oil (flower or leaf) showed strong anti-inflammatory activity. SIgA is a protein secreted by plasma cells that plays an important role in the gut immune response by preventing antigens from attaching to the epithelium. In addition, MUC-2 plays a crucial role as the first line of defense, promoting humoral and cellular immunity in the gut [47]. In this study, dietary supplementation with Pulicaria jaubertii (T2 to T4) showed increased fold change in MUC-2 and SIgA expression in the intestinal mucosa. Microbiota have a crucial influence on intestinal development and physiological and immunological functions [48]. A healthy gut is closely related to the balance of intestinal flora [49]. The results of the current study showed that the diet supplemented with Pulicaria jaubertii (T2 to T4) had a higher quantity of Lactobacillus spp. and a lower Salmonella spp. than T1. The antimicrobial activity against Salmonella spp. may be attributed to major bioactive compounds, including phenols found in Pulicaria jaubertii. Lactobacillus spp. is capable of lowering intestinal pH through the secretion of lactic acid, which may lead to the inhibition of Salmonella spp. in broiler chickens [50]. Giannenas et al. [ 41] indicated that phytogenic products rich in phenolic compounds might act against pathogenic intestinal bacteria due to their antibacterial activity, thus promoting the proliferation of beneficial bacteria. The beneficial intestinal bacteria play an important role in protecting the integrity of the intestinal mucosa [51]. Al-Fatimi et al. [ 52] and Al-Maqtari et al. [ 14] showed that the extract or oil of Pulicaria jaubertii (flower or leaf) has strong antimicrobial activity. ## 5. Conclusions In conclusion, Pulicaria jaubertii (whole plant) contains nutrient compositions, fatty acids, and pharmacologically active components. 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--- title: Microbiome–Metabolome Reveals the Contribution of the Gut–Testis Axis to Sperm Motility in Sheep (Ovis aries) authors: - Mingming Wang - Chunhuan Ren - Penghui Wang - Xiao Cheng - Yale Chen - Yafeng Huang - Jiahong Chen - Zhipeng Sun - Qiangjun Wang - Zijun Zhang journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044597 doi: 10.3390/ani13060996 license: CC BY 4.0 --- # Microbiome–Metabolome Reveals the Contribution of the Gut–Testis Axis to Sperm Motility in Sheep (Ovis aries) ## Abstract ### Simple Summary Healthy sperm viability, the core of male fertility, affects the sustainability of livestock breeding. Several studies have shown a strong link between male sperm motility and gut microbial regulation of the host metabolome, testicular function, and gut microbiota. However, few studies have examined the association between the gut microbiome, host metabolome, and testicular function. In this study, the microbiome and metabolome of adult sheep with significantly different sperm viabilities were analyzed. Our results confirm that gut microorganisms and metabolites differ significantly in sheep with different sperm viabilities and are strongly correlated among themselves and with sperm viability. Thus, our study provides new insights into weak spermatozoa in rams. ### Abstract A close association exists among testicular function, gut microbiota regulation, and organismal metabolism. In this study, serum and seminal plasma metabolomes, and the rumen microbiome of sheep with significant differences in sperm viability, were explored. Serum and seminal plasma metabolomes differed significantly between high-motility (HM) and low-motility (LM) groups of sheep, and 39 differential metabolites closely related to sperm motility in sheep were found in seminal plasma metabolomes, while 35 were found in serum samples. A 16S rRNA sequence analysis showed that the relative abundance of HM and LM rumen microorganisms, such as Ruminococcus and Quinella, was significantly higher in the HM group, whereas genera such as Rikenellaceae_RC9_gut_group and Lactobacillus were enriched in the mid-LM group. Serum hormone assays revealed that serum follicle-stimulating hormone (FSH) and MT levels were significantly lower in the LM group than in the HM group, whereas serum glucocorticoid (GC) levels were higher in the LM group than in the HM group, and they all affected sperm motility in sheep. Ruminococcus and other rumen microorganisms were positively correlated with sperm motility, whereas Lactobacillus was negatively correlated with FSH and GCs levels. Our findings suggest that rumen microbial activity can influence the host metabolism and hormone levels associated with fertility in sheep. ## 1. Introduction The sheep industry has recently gradually developed a large-scale, standardized, and intensive breeding system. However, with the continuous promotion of large-scale breeding, breeding density is too high, seriously limiting the living habits of sheep. In particular, the relatively limited movement area of breeding sheep sheds results in a decrease in the semen quality of the breeding rams and a decrease in the conception rate of ewes, seriously affecting the sustainable development of the sheep industry. Sperm motility and count are key factors for male fertility and successful fertilization. Sperm with low motility is usually associated with incomplete spermatogenesis and defects in sperm malformation that prevents the penetration of the cervical mucus and reaching the site of fertilization [1]. Furthermore, unlike the reproductive tract of cattle, the reproductive tract of sheep and goats prevents a large number of sperm from entering the uterus, requiring more time for sperm to reach the oviduct to fertilize the oocyte [2]. Therefore, having rams with highly viable semen is crucial for sheep breeding. Numerous studies have shown that gut microbes play a crucial role in spermatogenesis and male fertility [3,4]. Studies have shown a strong link between testicular function and gut microbiota regulation through the host metabolome, particularly the synthesis of metabolites such as secondary bile acids, which are gut derivatives that affect testicular physiology [5]. In addition, beneficial microbiota have been shown to significantly improve spermatogenesis and semen quality in busulfan-impaired spermatozoa [3]. Simultaneously, intestinal imbalances can impair spermatogenesis and reduce semen quality and male fertility [6]. Ding et al. found that a high-fat diet disrupts intestinal flora and leads to an increased abundance of Bacteroides and Prevotella in the gut of normal mice, along with a decrease in the number of spermatocytes and spermatozoa in the spermatogenic tubules of normal mice, which is associated with a decrease in sperm quality and quantity [6]. Recent studies have shown that an altered abundance of the intestinal Ruminococcaceae_NK4A214_group reduces bile acid levels, leading to impaired spermatogenesis and reduced spermatogenic cell numbers [4]. In contrast, dietary fiber supplementation improves the intestinal flora of boars, promotes the production of short-chain fatty acids, and improves spermatogenesis and semen quality [7]. Thus, the correlation between microbiome and semen parameters/fertility is gradually being revealed with the application of 16S ribosomal RNA sequencing to evaluate the microbiome of sterile males. Furthermore, a potential correlation between sperm viability, the microbiome, and organismal metabolite regulation is also being revealed. However, whether ram sperm motility is associated with gut microbial composition and dysfunction requires further exploration. The intestinal flora may contribute to changes in host metabolism by altering metabolites or by altering digestion and energy absorption, which may interfere with gamete formation. Intestinal flora can metabolize nutrients in the intestine and regulate intestinal metabolites to affect the blood metabolome [8]. When passing through other organs, blood metabolites can affect development or cause diseases [9]. Improved intestinal flora can improve sperm quality by modulating the plasma metabolome and small-intestinal function in mice [10]. Han et al. showed that the potential mechanism by which hydroxytyrosol (HT) improves semen quality promotes intestinal flora to improve plasma metabolites, which consequently promotes spermatogenesis and semen quality [11]. Ding et al. found that high-fat diets disrupted intestinal flora, stimulated host immune responses, induced inflammation, reduced semen quality, and increased the relative abundance of Bacteroides and Prevotella, which was associated with higher circulating blood endotoxin levels and reduced spermatogenesis [6]. In addition, dysbiosis of intestinal flora can lower bile acid levels, alter vitamin A metabolism, and transfer it to testicular cells through blood circulation, leading to sperm abnormalities [4]. This suggests that intestinal flora can influence sperm function by regulating blood metabolites. In addition, according to Zhao et al., the combined action of the blood and intestinal flora and testicular metabolome under fucoidan oligosaccharide (AOS) treatment attenuates the destruction of spermatogenesis by busulfan [10]. Zhang et al. also reported that fecal microbial transplantation caused changes in testicular lipid metabolism, increasing the levels of testicular metabolites, which are positively correlated with sperm quality, and contributing to the restoration of spermatogenesis [4]. This finding suggests a correlation between testicular metabolites and male fertility. Seminal plasma, as a testicular secretion, is an important medium for sperm survival and function. Seminal plasma metabolites affect downstream and complementary gene and protein expression changes, which may be key regulators of fertility in bulls [12]. Velho et al. found that metabolites such as bull seminal plasma 2-oxoglutarate and fructose play important roles in physiological processes such as sperm motility, energy metabolism, and regulation of metabolic activity and are important biomarkers for assessing fertility. They identified 21 amino acids in the seminal plasma of bulls of different fertility classes, indicating different metabolite patterns in low-fertility bulls compared to high-fertility bulls [13]. Hamamah et al. studied the seminal plasma of fertile and infertile men using nuclear magnetic resonance spectroscopy (NMR) and found significant changes in the concentrations of glyceryl citrate phosphocholine and lactate in patients with azoospermia and oligozoospermia [14]. Meanwhile, 1857 differential metabolites were identified between the high- and low-sperm viability groups in goats. A database of goat seminal plasma metabolomes was established, and some metabolites possibly affecting sperm viability were detected [15]. Another study concluded that seminal plasma from high-fertility bulls contained more tryptamine, taurine, and leucine and less citric acid and isoleucine [16]. Overall, intestinal flora plays an important role in the host’s energy absorption and metabolism, and dysbiosis can lead to metabolic disorders or stimulate inflammatory responses, disrupt endocrine function, and impair the reproductive capacity of the host. However, it is unclear whether high- and low-sperm motility in rams is related to disorders of intestinal microorganisms and altered serum and seminal plasma metabolites. Therefore, the primary objective of this study was to understand the correlation between high- and low-sperm motility and microbiota by identifying ram gut microbes with high and low-sperm motility using 16S rRNA sequencing. The secondary objective was to explore whether a non-targeted metabolomics-based approach can highlight significant differences between fingerprints obtained from adult ram serum and seminal plasma samples with different sperm viability levels, to identify new reliable markers of fertility, and to elucidate the intestine–testis axis mechanisms affecting spermatogenesis by combining the gut microbiome and serum and seminal plasma metabolome approaches. Characterizing impaired semen quality is promising and may provide new insights into low-motility sperm in rams and human infertility. ## 2.1. Study Subjects All experimental procedures were approved by the Ethics Committee of the Anhui Agricultural University, Hefei, China (approval number AHAUB2022008). Adult rams with no significant differences in age, weight, height, length, and chest circumference were obtained from Haiqinsheng Eco-Farming Co., Ltd. (Dingyuan, China) ($$n = 16$$). High-quality silage was supplemented with mixed concentrated feed, and water was provided ad libitum (all test animals were fed under the same conditions). After 3–5 days of abstinence during the breeding season, fresh ejaculates were collected using an artificial vagina ($$n = 2$$/ram) [17]. Sperm quality parameters assessed by the computer-assisted sperm analysis system (CASAS, Hamilton Thorne, Beverly, MA, USA) according to World Health Organization (WHO) standards [18] included total sperm motility, velocity of curved line (VCL), velocity of straight line (VSL), mean velocity (VAP), vibration index (WOB), and whip frequency (BCF). Sperm motility was divided into the high-motility (HM) group (sperm motility < $50\%$) and the low-motility (LM) group (sperm motility > $80\%$) according to the test method described by Yao et al. [ 19]. The remaining semen samples were separated from spermatozoa and seminal plasma by centrifugation (10,000× g, 4 °C, and 10 min). The separated samples were immediately snap-frozen in liquid nitrogen and used for biochemical analyses. ## 2.2. Sample Collection and Preservation The rumen contents of adult rams were collected through a gastric tube as described by Wang et al. [ 20]. Briefly, a gastric tube rumen fluid sampler (Colibri Pastoral Technology Co., Ltd., Wuhan, China) is inserted to a depth of about 100 cm so that the probe tip reaches the rumen ventral sac, a large-capacity sterile syringe (100–200 mL) is connected to the rear of the sampling tube, and the large-capacity syringe is pumped to extract a sample of rumen contents. Approximately 10 mL of the initial rumen sample contents were discarded to avoid saliva contamination. The collected surimi was filtered through two layers of sterile gauze and the filtered rumen fluid was collected into sterile 50 mL centrifuge tubes and immediately frozen in liquid nitrogen for DNA extraction and subsequent microbial and metabolite analysis. All test rams were fasted overnight and blood was collected from the jugular vein via venipuncture into Vacuette blood collection tubes. Each blood sample was then centrifuged (4 °C, 3000× g, 10 min) to obtain a serum sample, which was then immediately snap-frozen in liquid nitrogen until analysis. ## 2.3. Extraction and Sequencing of Microbial DNA Nucleic acid extraction was carried out using the TGuide S96 Fecal Genomic DNA Extraction Kit (Beijing Tiangen Biochemical Technology Co., Ltd., Beijing, China). PCR amplification of bacterial full-length 16SrRNA (V1–V9) was performed using primers 27F (5′-AGRGTTTGATYNTGGCTCAG-3′) and 1492R (5′-TASGGHTACCTTGTTASGACTT-3′). PCR products were mixed and purified, and sequencing libraries were generated using the SMRTbell Template Prep Kit (PacBio), and the libraries were tested for concentration (Qubit) and size (Agilent 2100). The final reaction products were purified by AMpure PB Beads and then sequenced on the Sequel II sequencing platform. ## 2.4. Bioinformatics Analysis Quality filtering, trimming, denoising, and merging of fastq files was performed using the dada2 package [21] in QIIME2 2020.6.0 software [22]. Amplicon sequence variants (ASVs) were obtained, and ASVs with relative abundance less than $0.005\%$ were filtered. Taxonomic annotation of feature sequences was carried out using the Naive Bayes classifier combined with a comparison approach using Silva.138 as a reference database. ## 2.5. Measurement of Serum Biochemical Indicators Enzyme-linked immunosorbent assay (ELISA) was used to determine the serum follicle-stimulating hormone (FSH), luteinizing hormone (LH), testosterone (T), vitamin D3 (VD3), lipopolysaccharide (LPS), melatonin (MT), and glucocorticoid (GC) levels. The assay kit was purchased from Shanghai Jianglai Biotechnology Co., Ltd. (Shanghai, China), and the assay procedure was carried out in strict accordance with the manufacturer’s instructions. ## 2.6. Metabolomic Sample Preparation and UPLC-Q/TOF-MS/MS Procedures All serum and seminal plasma samples were stored at −80 °C and thawed on ice prior to analysis. The process involves the following steps: obtain 100 μL of sample (serum, seminal plasma), add 500 μL of extract containing internal standard (methanol acetonitrile volume ratio = 1:1, internal standard concentration 20 mg/L), vortex and mix for 30 s; sonicate for 10 min (ice water bath). Afterwards, all samples were left to stand at −20 °C for one hour. Then all samples were centrifuged (4 °C, 12,000 rpm, 15 min), 500 μL of supernatant was carefully removed in an EP tube, the extract was dried in a vacuum concentrator, 160 μL of extract (acetonitrile water volume ratio: 1:1) was added to the dried metabolites for re-solubilization, vortexed for 30 s, and sonicated in an ice water bath for 10 min. After that, the samples were centrifuged again (4 °C, 12,000 rpm, 15 min). Finally, 120 μL of supernatant was carefully removed from the 2 mL injection bottle, and 10 μL of each sample was mixed into QC samples for machine testing. The LC system used for metabolomics analysis consisted of Acquity I-Class PLUS UPLC tandem with a Waters Xevo G2-XS QTOF high-resolution mass spectrometer. The column used was a Waters Acquity UPLC HSS T3 column (1.8 µm, 2.1 × 100 mm) purchased for LC separation, and the column temperature was maintained at 25 °C. The flow rate was 0.5 mL/min and the injection volume was 6 μL. For the positive ion mode, mobile phase A contained $0.1\%$ formic acid aqueous solution and mobile phase B contained $0.1\%$ formic acid acetonitrile. For the negative ion mode, mobile phase A contained $0.1\%$ formic acid aqueous solution and mobile phase B contained $0.1\%$ formic acid acetonitrile. For the negative ionization mode, mobile phase A contained $0.1\%$ formic acid aqueous solution and mobile phase B contained $0.1\%$ formic acid acetonitrile. The linear gradient was set as follows: 0–0.2 min: $2\%$B, 0.2–10 min: $2\%$B~$98\%$, 10–13 min: $98\%$B, 13–13.1 min: $98\%$B~$2\%$B, 13.1–15 min: $2\%$B. The Waters Xevo G2-XS QTOF high-resolution mass spectrometer acquires primary and secondary mass spectrometry data in MSe mode under the control of acquisition software (MassLynx V4.2, Waters, MA, USA). Simultaneous dual-channel data acquisition was performed at low-collision energy (2 V) and high-collision energy (10–40 V) with a mass spectrometry scan frequency of 0.2 s a mass spectrometry map. ESI ion source parameters were as follows: capillary voltage at 2000 V (ESI+ mode) and −1500V (ESI− mode), cone voltage (30 V); cone gas flow rate (50 L/ at 120 °C source temperature conditions) h), and desolventizing gas flow rate (800 L/h at 500 °C desolventizing temperature). ## 2.7. Metabolomics Analysis The raw peak area information was normalized to the total peak area for subsequent analysis. Principal component analysis (PCA) and Spearman correlation analysis were used to determine the reproducibility of samples within the group and quality control samples. The identified compounds were searched in the KEGG, HMDB, and LIPID MAPS databases for taxonomic and pathway information. Based on the grouping information, the multiplicity of differences was calculated and compared, and the p-value of significant differences for each compound was calculated using the t-test. OPLS-DA modeling was performed using the R language package ropls, and 200 permutation tests were performed to verify the reliability of the model, and the VIP values of the model were calculated using the multiple cross-validation method. The samples were screened for differential metabolites based on the OPLS-DA model, where metabolites with FC > 1.5, p-value < 0.05, and VIP > 1 in serum samples were defined as differential metabolites, and similarly, metabolites with FC > 2, p-value < 0.05, and VIP > 1 differential metabolites in seminal plasma samples were defined as differential metabolites. Manual data collation, statistical analysis, and metabolite mapping integration were performed using Excel software (Microsoft, version 2019). We then performed pathway analysis of differential metabolites using MetaboAnalyst 5.0 online software (accessed on 2 October 2022, https://www.metaboanalyst.ca/) to integrate all important metabolites into metabolic pathways. All enriched differential metabolites were identified using MetaboAnalyst 5.0 based on the Kyoto Encyclopedia of Genes and Genomes pathway. The hypergeometric test and relative betweenness centrality were used for statistical analysis of the pathways, and the significance of associations between metabolites and typical pathways was based on the ratio of the number of matches to the number of uploaded metabolites and the total number of molecules in the pathway [23]. In subsequent analyses, only pathways with a nominal significance level of $p \leq 0.05$ were selected [24]. ## 2.8. Data Analysis Differences in non-parametric data between the two groups were analyzed using the Wilcoxon rank sum test. For all other data, the Student’s t-test was used to compare the differences between the LM and HM groups. Statistical analyses were performed using SPSS 20.0 software (SPSS, Inc., Chicago, IL, USA). Potential correlations between differential microorganisms, seminal plasma metabolites, serum metabolites, sperm quality parameters, and serum biochemical indicators were collected and analyzed using mixOmics, p value < 0.05 was considered statistically significant. Graphs were generated by Prism 9.0 software (GraphPad Software, San Diego, CA, USA). ## 3.1. Sperm Vitality Significant differences were observed in sperm motility and sperm kinematic parameters between the two groups (Figure 1, Table S1, Figure S1). Sperm motility ($p \leq 0.001$) and concentration ($p \leq 0.05$) were significantly lower in the low-motility (LM) group than in the high-motility (HM) group, and the HM sperm exhibited a higher velocity (VCL, VSL, and VAP) and velocity index (WOB) than the LM sperm. ## 3.2. Serum Biochemical Index Analysis The serum biochemical indices results showed that the blood FSH ($p \leq 0.05$) and melatonin (MT) ($p \leq 0.05$) levels were significantly lower in the LM group than in the HM group (Figure 2E,F). In addition, serum glucocorticoid (GC) concentration was higher in the LM group than in the HM group (Figure 2G). These results suggest that sheep with significant differences in sperm quality have some differences in serum physiological parameters, implying that these differences are closely related to sheep sperm quality. ## 3.3. Analysis of the Taxonomic Composition of the Bacterial Microbiota After sequencing eight samples, a total of 119,061 CCS sequences were obtained by barcode identification, yielding at least 13,756 CCS sequences per sample and an average of 14,883 CCS sequences. Subsequently, 48,735 final valid data points were obtained after removing the primers, chimeras, and denoising. The ACE, Simpson, and Shannon diversity indices showed no change in alpha diversity between HM and LM and, briefly, no significant change in species richness and diversity (Figure 3A–C, Supplementary Table S1). Subsequently, we further explored whether there were differences in gut microbial composition between HM and LM and found that the dominant microbial phylum in the rumen microbiota was Firmicutes, followed by Bacteroidetes, Synergistota, Verrucomicrobiota, and Protebacteriota (Figure 4A). The Wilcoxon rank sum test showed (Figure 4B) that Firmicutes, Actinobacteriota, and Protebacteriota abundance was significantly higher in HM at the phylum level, whereas Bacteroidota and Synergistota were significantly higher in LM. The dominant microbial genus in the rumen microbiota was Prevotella, followed by Quinella, Rikenellaceae_RC9_gut_group and uncultured_rumen_bacterium (Figure 4C). In addition, we performed LEfSe (LDA > 2) and Wilcoxon rank sum test analyses at the genus level (Figure 4D; Table S2), which showed significant enrichment of Quinella, Ruminococcus and Christensenellaceae_R_7_group in the HM group. By contrast, Prevotella, Anaeroplasma and Fretibacterium was significantly enriched in the LM group. The gut microbial structures of the two groups were further investigated. PCoA analysis (Bray–Curtis) was performed, and the results showed that rumen microorganisms were divided into two different groups: LM and HM (Figure 4E). In addition, the PLS-DA results (R2(Y) = 0.959 and Q2 = 0.888), which also divided the microorganisms into two groups, indicated a significant difference in the microbiota structure between the LM and HM groups. ## 3.4. Multivariate Analysis of Metabolomic Data Two-dimensional plots of PCA scores according to serum and seminal plasma metabolomics in ion and negative ion modes are shown in Figure 5A,B and Figure S2. The two groups of samples were noticeably separated, indicating significant differences between the LM and HM metabolites. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) (Figure 5C,D and Figure S2) showed observed results, indicating that serum and seminal plasma metabolism differed between the two groups. From the replacement plots, the Q2-values were all lower than the R2-values, indicating the original model’s validity (Figure 5E,F and Figure S2). ## 3.5. Identification of Serum and Seminal Plasma Metabolites After UPLC-Q/TOF-MS/MS analysis, a total of 6172 metabolites were obtained from the metabolic profile of seminal plasma. LM and HM shared 5759 species, while 348 and 65 species were unique to LM and HM, respectively (Figure 6A, Supplementary Table S2). In addition, 5561 metabolites were obtained from serum samples. The two groups shared 5228, 228, and 105 metabolites specific to LM and HM (Figure 6A, Supplementary Table S3). Differential metabolites between these two groups were identified based on variable importance (VIP), fold change (FC), and p-value in the projection. We identified 1785 differential metabolites from seminal plasma samples, of which 511 metabolites were upregulated in HM, while 1274 metabolites were significantly enriched in LM (FC = 2, $$p \leq 0.05$$, VIP = 1) (Figure 6B, Supplementary Table S4). Similarly, 741 differential metabolites were identified in serum samples, 273 were upregulated in HM, and 468 were significantly enriched in LM (FC = 1.5, $$p \leq 0.05$$, VIP = 1) (Figure 6C, Supplementary Table S5). Between the LM and HM groups, 39 significantly different metabolites, such as eicosatetraenoic acid, catechol, L-histidine, D-lactic acid, D-leucic acid, L-glutamate, raffinose, and pantothenic acid were found in seminal plasma samples (Figure 6D, Table S3). A total of 35 significantly different metabolites were found in the serum species (Figure 6E, Table S4), mainly (S,E)-zearalenone, dopamine, eicosopentanoic acid, aciclovir, cAMP, cannabidiol, arachidic acid, and other metabolites. ## 3.6. Identification of Differential Metabolic Pathways in Serum and Seminal Plasma To further explore the potential metabolic pathways of these metabolites, KEGG enrichment analysis showed that the altered metabolites in seminal plasma were mainly involved in aminoacyl-tRNA biosynthesis ($p \leq 0.001$), histidine metabolism ($p \leq 0.05$), pantothenate and CoA biosynthesis ($p \leq 0.05$), tryptophan metabolism ($p \leq 0.05$), riboflavin metabolism ($p \leq 0.05$), nitrogen metabolism ($p \leq 0.05$), D glutamine and D-glutamate metabolism ($p \leq 0.05$), and valine, leucine, and isoleucine biosynthesis ($p \leq 0.05$) (Figure 6F, Table S5). In addition, the serum metabolites were mainly involved in riboflavin metabolism, glycerophospholipid metabolism, pyrimidine metabolism, and steroid biosynthesis (Figure 6G, Table S6). ## 3.7. Correlation of Rumen Microorganisms, Serum Metabolome, Seminal Plasma Metabolome, Serum Biochemical Indicators, and Sperm Quality In this study, potential correlations between differential microorganisms, seminal plasma metabolites, serum metabolites, sperm motility parameters, and serum biochemical indicators were analyzed using mixOmics (Figure 7A and Figure S3). The results showed the highest correlation between the microbiome and serum metabolism group ($r = 0.99$), between the sperm kinematic parameters and microbiome ($$p \leq 0.98$$), and between the sperm kinematic parameters and seminal plasma metabolism group ($$p \leq 0.98$$). In addition, the key correlation results of mixOmics were again visualized by Cytoscape in this study (Figure 7B and Figure S3), which revealed a positive correlation between Ruminococcus significantly enriched in the HM group and the serum metabolite dopamine. Higher dopamine levels in the HM group were positively correlated with sperm kinematic parameters (motility, VCL, VAP, and VSL) and negatively correlated with physiological indices (GC). In addition, higher enrichment of microorganisms (Bifidobacterium and Roseburia) in the HM group was positively correlated with seminal plasma metabolites (L-tryptophan L-glutamate, and D-leucic acid), whereas L-tryptophan, L-glutamate, and D-leucine were positively correlated with sperm kinematic parameters. These results imply a close correlation between sperm motility parameters and gut microbes, the serum metabolome, seminal plasma metabolites, and physiological indices in sheep. ## 4. Discussion Intestinal flora is involved in all aspects of host health, and a causal relationship between intestinal flora and sperm quality has been established in animal models and humans and is highly correlated with the host metabolome [3]. Hormones are the most important environmental factors affecting germ cell development and have a synergistic effect on the homeostasis of testicular metabolism and the progression of spermatogenesis [25]. In this study, we characterized the rumen microbiome, serum, and seminal plasma metabolome of sheep with high-motility (HM) and low-motility (LM) sperm. For the first time, we further revealed the correlation between them and sperm motility in sheep by integrating microbiome, metabolome, and physiological parameters. Melatonin (MT), a hormone secreted by the pineal gland of the brain, is closely associated with sperm motility. A study found that serum melatonin levels were significantly higher in the normal sperm group than in the low-viability group [26], consistent with the significantly higher results in the HM group than in the LM group in this study. MT is absorbed by the testes through blood circulation and acts on the testicular mesenchyme to prevent apoptosis and restore testicular function [27], directly affecting the function of the testes. In addition, MT has shown great improvements in sperm concentration and viability in patients treated for varicocele, and long-term use of MT has been shown to improve sperm quality and reduce sperm DNA damage [28]. Notably, MT and gut bacteria appear to have a complex functional relationship, and a study found that MT increases the abundance of Bifidobacterium [29]. In this study, higher Bifidobacterium abundance was also found in the HM group. Intestinal flora can influence MT levels by regulating its essential precursor, L-tryptophan (Trp) [30]. In de-pinealized rats, Trp administration increased serum MT levels and strongly supported MT synthesis in the gastrointestinal tract [31]. In addition, Trp is effective in improving sperm quality, and Trp metabolite fluid in seminal plasma was also significantly higher in normal men than in men with weak spermatozoa [32], consistent with the higher seminal plasma Trp in the HM group than in the LM group in this study. From the above results, the activity of rumen microorganisms (especially Bifidobacterium) may directly or indirectly affect sperm motility in sheep by affecting Trp levels or regulating MT levels through Trp. Dopamine (DA) is an important neurotransmitter that plays a physiological role in regulating viability, fertility, and sperm motility [33]. A study found that in patients with weak sperm and oligospermia, the concentration of DA in the seminal plasma and blood was lower than that in men with normal fertility [34]. In the present study, we obtained similar results as DA in serum metabolites, which was significantly higher in the HM group than in the LM group. DA induces activation of dopamine type 2 receptor (D2DR) in boar sperm, consequently increasing tyrosine phosphorylation and accelerating the movement of sperm [33]. Dopamine improves motility parameters and acrosome responses in highly motile sperm subpopulations (HM), and these effects are thought to be caused by tyrosine phosphorylation [35]. In addition, activated D2DR can lead to elevated Ca2+ levels, and the Ca2+ content in the middle of the sperm is associated with the flagellum and influences flagellum activity [36]. The above studies provide sufficient evidence for the importance of DA as a neurotransmitter for male reproduction. Therefore, in the present study, serum dopamine metabolism was abnormal in the LM group, leading to reduced sperm motility in sheep. Additionally, Trp levels are closely related to DA anabolism. Depletion of Trp reduces DA biosynthesis in vivo, and the administration of Trp in rats increases DA levels in the striatum of the brain [37]. Notably, microorganisms of genera such as Prevotella, Lactobacillus, Bifidobacterium, and Rumenococcus modulate the receptors, transporters, and specific targets of the dopaminergic pathway positively or negatively [38]. They have demonstrated an important link between the central nervous system and dopaminergic pathways in the periphery of the gastrointestinal system [39]. The gut microbiota has been identified as a key regulator of crosstalk between the brain and the gastrointestinal tract (gut–brain axis) [40]. Significant microbiota dysbiosis (significant reduction in Ruminococcus, $p \leq 0.05$) and decreased plasma DA levels in mice were found in CRS (chronic restraint stress) mouse studies, and a positive correlation between Ruminococcus abundance and DA was found, suggesting a positive correlation between Ruminococcus and DA metabolism [41]. We can speculate that the dysregulation of gut microbes (Ruminococcu) in the present study caused abnormal dopamine metabolism in the LM group, leading to decreased sperm motility in sheep. In conclusion, microorganisms can affect DA metabolism and, thus, sperm quality by regulating DA metabolism or Trp levels. In addition, in this study, serum Glucocorticoids (GC) was higher in the LM group than in the HM group. Excessive levels of an effective anti-inflammatory hormone in the body may impair male reproductive function. A study revealed that dexamethasone (DEXA) affects the gonadotropin axis, inhibits testosterone production, adversely affects testicular tissue, and affects testes and spermatogenesis by reducing daily sperm production and disrupting sperm viability [42]. In addition, DEXA can induce excessive production of reactive oxygen species (ROS) and cause oxidative stress, leading to impaired sperm parameters, testicular damage, and ultimately, male infertility [43]. Excessive activation of the hypothalamic–pituitary–adrenal axis (HPA) owing to high GC levels is the main cause of impaired gonadal function and fertility [44]. GC induces apoptosis in mouse spermatogenic cells. The rapid and efficient degradation of apoptotic germ cells by Sertoli cells is essential for developing and differentiating germ cells and is a necessary process for spermatogenesis to proceed in healthy germ cells [44]. If the ability of Sertoli cells to phagocytose is impaired, it leads to an increase in the number of apoptotic germ cells, which cannot be eliminated and converted into energy [45], resulting in a non-infectious inflammatory response in the testis. In addition, Sertoli cells provide morphological support through cell–cell interactions and biochemical components through the secretion of lactic acid [46]. Lactic acid, derived from glucose metabolism by supporting cells, is the main metabolic fuel for post-meiotic germ cells and inhibits apoptosis of testicular germ cells [47]; it is a good energy substrate for sperm survival and motility. In the present study, higher concentrations of D-lactic acid were found in the seminal plasma of the HM group. However, GC can cause a decrease in lactate content in testes and TM4 cells and a downregulation of phagocytic activity in Sertoli cells, accompanied by a decrease in mitochondrial activity through the upregulation of PDK4 (Recombinant Pyruvate Dehydrogenase Kinase 4) [48]. In addition, elevated serum CORT (Cortisol) levels induce p27 (a cell cycle protein-dependent kinase inhibitor) expression in Sertoli cells and terminate Sertoli cell proliferation, leading to a decrease in the number of Sertoli cells in mouse testes [49]. From the above results, GC can affect the value addition of Sertoli cells and induce disturbance of lactate metabolism in Sertoli cells, affecting their phagocytic ability, causing testicular inflammation, and impairing reproductive function. Notably, a correlation existed between GC and the activity of the gut microbes. Gut microbiota regulates glucocorticoid levels [50]. Plasma ACTH (Adrenocorticotropic Hormone) and corticosterone levels were reduced in Bifidobacterium infantis mono-associated mice, whereas plasma ACTH and corticosterone levels were increased in E. coli mono-associated mice [51]. Lactobacillus was found to enhance the potentiation of prednisone in EAH(experimental autoimmune hepatitis) mice [50]. In addition, serum ACTH and corticosterone levels were higher in germ-free mice after acute stress than in specific pathogen-free mice [51]. These studies suggest a close association between microorganisms and glucocorticoids. In the present study, Lactobacillus was detected in the rumen fluid of the LM group but not in the HM group. Therefore, we can hypothesize that the gut microbiota (Lactobacillus) can influence the host behavior and HPA(Hypothalamic-pituitary-adrenal) axis secretion of GC, causing a decrease in the number of Sertoli cells and disturbance of lactate metabolism, consequently impairing reproductive function and affecting sperm quality in sheep. However, this study did not validate the proliferation status and metabolic processes of Sertoli cells; therefore, further studies are required. A study showed that follicle-stimulating hormone (FSH) in the serum of oligozoospermic and hypospermic males was significantly lower than that in normal males [52], consistent with the results of the present study. In contrast, FSH treatment significantly improved routine sperm parameters [53]. This suggests that applying FSH when treating patients with oligo- and hypospermia can be an effective treatment strategy. In addition, probiotics can affect the host’s reproductive function by regulating the host metabolism. Probiotic (Lactobacillus rhamnosus) supplementation increases serum FSH levels in males and results in increased sperm velocity (VSL, VCL, and VAP) and the percentage of progressively motile sperm [54]. Wen et al. found a negative correlation between the relative abundance of Lactobacillus and serum FSH levels (r = −0.27, $$p \leq 0.046$$) [55]. In the present study, we detected Bifidobacterium in the HM midgut, which was not in the LM group. In addition, Lactobacillus was found in the LM group, whereas it was not detected in the HM group. Thus, gut microbes (Bifidobacterium and Lactobacillus) contribute to the differences in sperm quality in sheep by regulating host FSH levels. This process possibly results from gut microbes affecting testicular development and function by regulating the permeability of the blood–testis barrier and modulating serum FSH levels [56]. However, this process requires further investigation. Microorganisms can also regulate sperm viability by affecting host amino acid metabolism. Most of the seminal plasma metabolome differences in this study focused on amino acid metabolic pathways, suggesting the importance of amino acid metabolism in maintaining sperm viability. A study demonstrated the important role of amino acid metabolic pathways in the regulation of parameters related to semen quality [57]. Amino acid metabolism disorders are thought to be associated with structural and functional alterations in the spermatozoa of men with severe oligospermia. Zhao et al. found reduced levels of several amino acids, such as leucine (Leu), glutamic acid (Glu), and Trp, in patients with weak spermatozoa [58]. Another study found lower levels of tryptophan and glutamate in men with lower sperm motility and higher rates of morphological abnormalities [59]. In the present study, the levels of Trp (FC = 2.29, $p \leq 0.001$), Glu (FC = 2.88, $p \leq 0.001$), and Leu (FC = 2.95, $p \leq 0.001$) in the seminal plasma were also significantly lower in the LM group than in the HM group. Trp leads to increased testicular descent and reduced spermatozoa, whereas Trp-supplemented diets can significantly improve sperm motility in rams [60]. In addition, the addition of amino acids to the diet can improve sperm quality, change seminal plasma composition, and improve sperm fertility in boars, and these effects are related to Ca2+ and cAMP synthesis [61]. All of the above studies showed the importance of amino acids in regulating sperm quality. Changes in amino acid composition also play an important role in altering the gut microbiota. Leu has been reported to promote intestinal development in piglets [62]. Glu significantly alters the composition of the intestinal microbial community, increases the diversity of the microbial community, and promotes the colonization of Roseburia [63]. Metabolic changes associated with the rat cecum microbiome indicated that L-glutamate was negatively correlated with Prevotella [64]. Although amino acids can alter gut microbiota, they can also maintain host amino acid homeostasis by facilitating amino acid digestion and absorption. For example, the pig gut microbiota promotes the synthesis of essential amino acids such as Leu, which the host requires [65]. These studies show that amino acids can regulate the gut microbiota, which can also influence the metabolic processes of amino acids, and that their interaction can affect male fertility. Therefore, we hypothesized that in the present study, differences in sperm quality between LM and HM groups of sheep were associated with the dysregulation of amino acid metabolism caused by disorders of the genera Prevotella, Bifidobacterium, and Roseburia. ## 5. Conclusions In conclusion, we provide a unique perspective for studying the correlation between the intestine–testis axis in sheep. Rumen microbial activity can influence sperm motility in sheep by affecting Trp metabolism and regulating serum MT and DA levels. In addition, elevated glucocorticoid levels owing to rumen microbial disorders can cause disturbances in lactate metabolism, impair reproductive function, and affect sperm motility in sheep. FSH levels, which are closely related to sperm motility, are modulated by microorganisms. There were also differences in the amino acid metabolism levels in the seminal plasma of LM and HM rams. 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--- title: Moringa oleifera Leaf Powder as New Source of Protein-Based Feedstuff Improves Growth Performance and Cecal Microbial Diversity of Broiler Chicken authors: - Haiwen Zhang - Liangmin Huang - Shihui Hu - Xinyun Qin - Xuemei Wang journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044617 doi: 10.3390/ani13061104 license: CC BY 4.0 --- # Moringa oleifera Leaf Powder as New Source of Protein-Based Feedstuff Improves Growth Performance and Cecal Microbial Diversity of Broiler Chicken ## Abstract ### Simple Summary In this study, we investigated the effects of Moringa oleifera leaf powder (MOLP) on growth performance, carcass characteristics, and cecum micro-organisms of broiler chickens by using different levels of MOLP instead of canola cake. The results showed that $5\%$ MOLP had an improving effect on broiler growth performance and carcass characteristics in the early and late stages. The sequence of cecal microbiota from broiler chickens via 16S rRNA revealed that $5\%$ MOLP is likely to enhance the growth performance and carcass characteristics of broiler chickens by regulating the relative abundance of intestinal flora. The results of this study can provide some reference for the application of MOLP in livestock and poultry farming. ### Abstract Currently, the lack of protein source feed has become a pressing issue. Moringa oleifera leaf powder (MOLP) has good potential for the development of protein-derived feeds due to its good protein quality and abundance, but little is known about its effects on broiler growth performance and cecal microbiota. In this study, the chickens were fed different rates of MOLP ($1\%$, $3\%$, $5\%$, $7\%$, and $9\%$) instead of the rape seed cake, and the effects of different levels of MOLP on growth performance, carcass characteristics, and cecal microbiota of the broilers were evaluated at two different growth stages (day 28 and day 56). In terms of growth performance, the best results were obtained at the $3\%$ MOLP level in the early stages ($p \leq 0.05$). In terms of carcass characteristics, in the early stage, the level of $5\%$ MOLP had the best effect; in the later stage, $5\%$ MOLP also had the best effect. In terms of cecal microbial changes, the alpha diversity analysis revealed that $5\%$ MOLP enhanced the richness and diversity of broiler intestinal flora. At the phylum level, the addition of $5\%$ MOLP adjusted the relative abundance of Firmicutes and Bacteroidetes to a level close to that of the A1 group on day 28, while $5\%$ MOLP significantly reduced the relative abundance of Bacteroidetes ($p \leq 0.05$) compared to the A2 group on day 56, and the relative abundance of Firmicutes was still higher in the D2 group than in the A2 group ($p \leq 0.05$). At the genus level, MOLP addition consistently and significantly increased the relative abundance of Bacteroides ($p \leq 0.05$), except for $3\%$ on day 28 and $1\%$ on day 56. For Oscillospira, increasing MOLP levels in the pre- and post-period resulted in a significant decrease in the relative abundance of Oscillospira ($p \leq 0.05$). In conclusion, MOLP helps to enhance growth performance and carcass characteristics and improve the cecal microbial structure of broilers. The recommended rate of MOLP addition for broilers is $5\%$ in both the early and late stages. ## 1. Introduction Moringa oleifera (Capparales: Moringaceae), also called drumstick tree, originated in India, Africa, and Southeast Asia [1]. Moringa oleifera is considered an important food plant of high nutritional value, and almost all parts are edible [2]. The leaves of M. oleifera have active constituents with multiple bioactivities; quercetin is the predominant flavonol in the leaves of M. oleifera and exhibits multiple therapeutic properties as a potent antioxidant [3]. Chlorogenic acid is another important active compound in Moringa and could promote glucose metabolism in rats [4]. Moringa oleifera leaves are an extremely valuable food source for both humans [5] and animals [6,7]; they are reported to be rich in highly digestible protein, vitamins, and essential amino acids [8], and dried leaves have crude protein up to $30.3\%$ and 19 amino acids [9]. Additional advantages of M. oleifera are that it is resistant to drought, fast growing, and easy to cultivate in tropical areas, and it may serve as an alternative source of food [3]. Moringa leaves also exhibit antimicrobial properties that inhibit bacterial growth [10], and ethanol extracts have shown broad spectrum activity against pathogens such as Escherichia coli, Pseudomonas aeruginosa, Staphylococcus aureus, and Enterobacter aerogenes [11]. To evaluate the potential of Moringa oleifera leaf powder as a feed, the effects of Moringa oleifera leaf powder (MOLP) have been investigated in some animals. Although MOLP supplemented in a $20\%$ ratio in the diet did not influence rat growth performance [12], MOLP successfully replaced commercial components in concentrate feeds as a protein source for dairy cows [13], and MOLP substituted for alfalfa meal at $20\%$ significantly improved rabbit growth performance, meat quality, and antioxidant and biochemical parameters [14]. Additionally, a corn-based diet supplemented with $1.2\%$ MOLP significantly improved broiler chicken growth performance, intestinal microarchitecture, and acidic mucin production [15], and a study of the effects of MOLP in a ratio of $10\%$ MOLP in 3 strains of chickens showed that MOLP addition positively affected growth performance and carcass characteristics [16]. However, to our knowledge, there are few reports on the effects of MOLP on chicken cecal microflora. The cecum is the main site of intestinal fermentation in chickens, has the highest abundance of microorganism species, and is one of the most important factors that affect animal health and growth performance. Therefore, it is necessary to study the cecal microbial diversity of chickens [17]. Sequencing of 16S rRNA has been widely used to study microbial diversity and can be used to maximize bacterial classification [18]. Early culture-independent methods and denaturing gradient gel electrophoresis (DGGE)-based techniques were initially applied to analyze intestinal microbiota, but they were time-consuming and had limited coverage. However, with the development of sequencing technology, next-generation sequencing (NGS) could offer unparalleled coverage and depth at low a cost [19]. As a valuable potential feed resource, little is known about the influence of MOLP and suitable substitution levels for the cecal microbiota of chicken. Therefore, in this study, different levels of MOLP substitution for rapeseed cake were fed to broiler chickens. First, the effects of different levels of MOLP substitution on growth performance and carcass characteristics of broiler chickens at 2 growth stages (day 28 and day 56) were evaluated. Second, the effects of different levels of MOLP substitution on the cecal microbiota of broiler chickens at two growth stages were evaluated by using the NGS technique. ## 2.1. Experimental Design All procedures of chicken raising and cecum content sampling, evaluation of broiler growth, and carcass characteristics were approved by the animal care and use committee of Hainan University. During the execution and sampling process, we exerted all efforts to minimize the suffering of the animals. A total of 216 male broiler chickens with similar weight (2 weeks) were randomly divided into 6 groups, each group had 3 repetitions, and each repetition had 12 individuals. Chickens were fed for 28 and 56 days with different feed formulas. As listed in Table 1, the A1/A2 group was fed the basic diet, and the B1/B2, C1/C2, D1/D2, E1/E2, and F1/F2 groups were fed an experimental diet substituted with $1\%$, $3\%$, $5\%$, $7\%$, and $9\%$ MOLP for an equal ratio of rape seed cake. The details of the experiment are as follows: during the experimental period, the routine procedure was sterilized, the ambient temperature was gradually decreased from 34 °C to a constant 26 °C, natural light was provided, feed was added once a day at 8:00 am and once a day at 15:00 pm, and the broilers were free to feed and drink during the period. To keep the consistency of energy and protein levels, several ratios of feedstuff were slightly adjusted, such as maize and soybean meal. ## 2.2. Measurement of Growth Performance After fasting 12 h in advance on day 28 and day 56 of the test period, respectively, 3 broilers in each group were weighed by replicates, the total body gain (TBG) of each group was counted, and the feed intake of each group was weighed at the same time to calculate the feed/gain (F/G) of broilers in each group at different stages for comparative analysis. The average daily gain (ADG), average daily feed intake (ADFI), and F/G were calculated with the following formulas. ADFI = total food intake/(trial days × number of test animals) ADG = (final weight − initial weight)/measured days F/G = total feed consumption/(final weight − initial weight) ## 2.3. Measurement of Carcass Characteristics Eighteen test broilers were slaughtered on day 28 and day 56, respectively, and the slaughter rate, half-cleaning rate, and full-cleaning rate of broilers were subsequently measured according to the methods in “Poultry Production Performance Nomenclature and Metric Statistical Methods (NY/T823-2004)”. ## 2.4. Cecum Sample Collection The sampling period was divided into 2 stages: 28 days and 56 days. All groups were starved for 12 h before sampling, and 1 chicken from each repetition (with body weight closest to the mean weight) was selected and sacrificed. Samples were aseptically scraped from cecum mucosa and placed in the sterile tube, and all samples were immediately stored at −80 °C for further analysis. ## 2.5. DNA Extraction and 16S rRNA Gene Sequencing Total genomic DNA was extracted from cecal samples by using the stool DNA Kit (Sigma-Aldrich, Taufkirchen, Germany) according to the manufacturer’s instructions. The V4-V5 regions of bacterial 16S rRNA gene (from 507 to 907) were amplified from extracted DNA by using barcoded primers 515F (5′-GTGCCAGCMGCCGCGG-3′) and 907R (5′-CCGTCAATTCMTTTRAGTTT-3′). The PCR reaction was carried out in a 50 μL system with 10 μL 1× primeSTAR buffer (Mg2+ Plus), 4 μL 200 μM dNTP mixture, 1 μL 0.1 μM forward and reverse primers, 0.5 μL 1.25U primer STAR HS DNA polymerase, 10 ng template DNA, and ddH2O to the final volume of 50 μL. The PCR reaction was carried out by using PCR amplification (GeneAmp PCR System 9700, Foster City, CA, USA). The PCR reaction parameters were as follows: following the denaturation stage at 98 °C for 1 min, the amplifications were carried out with 27 cycles at the melting temperature of 98 °C for 30 s, an annealing temperature of 55 °C for 30 s, and an extension temperature of 72 °C for 30 s. Furthermore, an extra extension step was performed at 72 °C for 5 min. The amplicons were pooled and quantified by using Nanodrop (Thermo Scientific, Carlsbad, CA, USA). Then, the DNA library was sequenced by using the PCR-free method, and next-generation sequencing was performed via an Illumina Hiseq PE250 (SAGENE, Guangzhou, China). ## 2.6. Quality Control The barcodes were cut off, and pair-end tags (reads 1 and reads 2) of each sample were joined by using the Pandaseq program. Raw tags of continuously low-quality value (quality threshold ≤ 19) and the base number reached the set length (length value set as 3). Then, the first low quality base site was truncated, the cut-out tags data set that has continuously high-quality bases with length <$75\%$ of the tag length was filtered. The pair-end tags without overlap were filtered, and the filtered tags were homogenized; finally, the possible chimeras were recognized and filtered. ## 2.7. Bioinformatics and Statistical Analysis Broiler cecum samples were collected as a mixed sample of 3 birds per group, and 3 replicates were performed in each group. The high-throughput sequences were clustered into operational taxonomic units (OTU) with similarity of at least ≥$97\%$. The distribution of OTU tags in each sample was analyzed by using a box plot, and the heat map of abundance of clustering and principal component analysis (PCA) was plotted based on the table of OTU abundance. Cladogram analysis was performed to blast representative OTU tags through Figure Tree software. The α-diversity of community richness (including ACE and Chao1) and diversity (including Shannon and Simpson) was ascertained by using MOTHUR [20]. The taxonomic assignments of the OTUs were performed by using QIIME software (quantitative insights into microbial ecology) based on the databases of SILVA [21], Greengene [22], and RDP [23]. The microbial significance analysis between groups was performed by using the effect size (LEfSe) method and linear discriminant analysis (LDA). Statistical analysis was performed by using SPSS 18.0 t-tests (SPSS Inc., Chicago, IL, USA), and $p \leq 0.05$ was considered significant. ## 3.1. Effect of Different MOLP Levels on the Growth Performance of Broiler Chickens On 28 days, as shown in Table 2, the daily weight gain of the B1, C1, and D1 groups increased significantly. Furthermore, there was a significant difference compared to the E1 and F1 groups ($p \leq 0.05$), but there was no significant difference between the B1, C1, and D1 groups ($p \leq 0.05$). Group C1 had the highest daily food intake value and was significantly different from the other groups ($p \leq 0.05$), while group A1 had the lowest value of 59.91 g. The daily feed intake of group A1 was significantly different from all other groups ($p \leq 0.05$), except that there were no significant differences with group D1 ($p \leq 0.05$). In terms of total weight gain, the B1 and C1 groups had higher values of 13.56 kg and 13.52 kg, respectively. There were no significant differences between the above two groups ($p \leq 0.05$), but there was a significant difference with the D1, E1, and F1 groups ($p \leq 0.05$). In the material-to-weight ratio, the values were lower in group B1 and C1, and there was no significant difference between the above two groups ($p \leq 0.05$), but there was a significant difference with the D1, E1, and F1 groups ($p \leq 0.05$). On day 56, as shown in Table 2, there were no significant differences in daily weight gain, daily feed intake, total weight gain, and feed-to-weight ratio between the groups ($p \leq 0.05$). ## 3.2. Effect of Different MOLP Levels on Broiler Chicken Carcass Characteristics On day 28, as shown in Table 3, the different levels of MOLP did not have a significant effect ($p \leq 0.05$) on the preslaughter weight, the half-clean bore weight, the slaughter rate, or the full-clean bore weight of broilers compared to the control group. Significant differences ($p \leq 0.05$) existed in the slaughter weight and semi-clear bore weight in groups C1 and D1 compared to group E1, while no significant differences ($p \leq 0.05$) existed in both of these indicators in the other groups. The slaughter rate in group E1 was significantly different from that in group A1, C1, D1, and F1 ($p \leq 0.05$), while the rest of the groups were not significantly different from each other ($p \leq 0.05$). There was a significant difference ($p \leq 0.05$) between group E1 and all other groups in the semi-clean chamber rate, except that there was no significant difference ($p \leq 0.05$) between group E1 and group B1 in the semi-clean chamber rate. Although there was a significant difference ($p \leq 0.05$) between groups C1 and D1 compared to groups B1 and E1, there was no significant difference between the C1 and D1 groups, but the semi-net bore rate was higher in the C1 group. On day 56, as shown in Table 4, different levels of MOLP did not have a significant effect on the slaughter rate and total net bore weight of the broilers compared to the control group ($p \leq 0.05$), and with increasing MOLP levels, the values of pre-slaughter weight, slaughter rate, half net bore weight, and total net bore weight of the D2 group were the highest among all groups, and there were significant differences between them and the A2 and B2 groups ($p \leq 0.05$). In the semi-net bore rate, there was a significant difference only between the E2 and C2 groups ($p \leq 0.05$), and the value was higher in the E2 group, while there was no significant difference between the rest of the groups ($p \leq 0.05$). ## 3.3. Qualified Sequence Tags The sequencing results were divided into two stages: day 28 and day 56. The total qualified sequences of the cecal samples of 18 broiler chickens of each stage were 292,319 and 276,672, and there was an average of 48,719 and 46,112 reads per cecal sample, respectively. The total sequences corresponded to 6554 and 6576 OTUs, with an average of 1092 and 1096 OTUs per sample, respectively. The Shannon index (Table 5) and rarefaction curves (Figure 1) for each sample reached the saturation plateau, which showed that our sampling had sufficient sequence coverage to accurately describe the bacterial composition of each group. The indices of bacterial richness (Chao and Ace) and bacterial diversity (Shannon and Simpson) are shown in Table 5. ## 3.4. Taxonomic Composition All filtered tags were classified from phylum to species based on the SILVA taxonomic database and by using QIIME. Bacteria with a relative abundance ≥$1\%$ were considered dominant, as shown in Table 6 and Table 7, and were identified in two different stages of broiler chicken growth. The dominant microflora at the phylum level were Firmicutes, Bacteroidetes, Actinobacteria, and Tenericutes (Figure 2), and at the genus level, they were Ruminococcus, Bacteroides, Dorea, Faecalibacterium, Oscillospira, Parabacteroides, Phascolarctobacterium, Prevotella, Coprococcus, and Megamonas (Figure 3). Because only one kind of bacteria was identified at the kingdom level and two kinds of bacteria were identified at the species level, we focused mainly on the phylum and genus level; therefore, the kingdom and species levels were not listed in Table 6 and Table 7. ## 3.5. Effect of Different MOLP Levels on Microbial Community: The Relative Abundance and Diversity of the Microbial Community at the Phylum Level In this study, a relative abundance of >$1\%$ was considered dominant, and the effect of MOLP on microbial communities was evaluated at the phylum and genus levels. As shown in Figure 4a, the proportions of Actinobacteria in the D1 (0.018 ± 0.004), E1 (0.024 ± 0.006), and F1 (0.035 ± 0.002) groups were significantly lower than those of the A1 (0.046 ± 0.006), B1 (0.041 ± 0.009), and C1 (0.052 ± 0.005) groups ($p \leq 0.05$). The proportion of Bacteroidetes in the B1 group (0.528 ± 0.055) was significantly higher than that of the A1 (0.295 ± 0.031) and D1 (0.315 ± 0.030) groups ($p \leq 0.05$), whereas C1 (0.433 ± 0.059), E1 (0.363 ± 0.065), and F1 (0.419 ± 0.048) groups did not differ significantly from the A1, D1, and B1 groups. The proportions of Firmicutes in the A1 (0.603 ± 0.024), D1 (0.579 ± 0.034), and E1 (0.551 ± 0.055) groups were significantly higher than those of the B1 (0.393 ± 0.044) group ($p \leq 0.05$), whereas there was no significant difference between the C1 (0.470 ± 0.055) and F1 (0.495 ± 0.044) groups and the A1, B1, D1, and E1 groups. On day 56 (Figure 4b), the proportion of Actinobacteria in B2 (0.024 ± 0.002) was significantly higher than in the A2 (0.011 ± 0.001) and E2 (0.011 ± 0.002) groups ($p \leq 0.05$), and no significant differences were found between the C2 (0.013 ± 0.003), D2 (0.013 ± 0.001), and F2 (0.015 ± 0.001) groups and the A2, B2, and E2 groups. The proportions of Bacteroidetes in the E2 (0.544 ± 0.013) and F2 (0.564 ± 0.009) groups were significantly higher than those of the A2 (0.435 ± 0.036) and D2 (0.397 ± 0.049) groups ($p \leq 0.05$). The D2 and A2 groups were also significantly different from each other ($p \leq 0.05$), but there were no significant differences between the B2 (0.499 ± 0.019) and C2 (0.507 ± 0.051) groups and the A2, E2, and F2 groups. The proportions of Firmicutes in the A2 (0.476 ± 0.036) and D2 (0.525 ± 0.049) groups were significantly higher than in the B2 (0.417 ± 0.024), C2 (0.399 ± 0.050), E2 (0.391 ± 0.01), and F2 (0.364 ± 0.007) groups ($p \leq 0.05$). ## 3.6. Effect of Different MOLP Levels on the Relative Abundance and Diversity of the Microbial Community at the Genus Level At the genus level, as shown in Figure 5a, the proportion of Bacteroides in the E1 group (0.191 ± 0.033) was significantly higher than in other groups, and the B1 (0.162 ± 0.030), D1 (0.152 ± 0.019), and F1 (0.139 ± 0.016) groups had significantly higher proportions than the A1 (0.100 ± 0.013) and C1 (0.100 ± 0.010) groups. The proportions of Faecalibacterium in the A1 (0.068 ± 0.005) and E1 (0.068 ± 0.004) groups were significantly higher than those in the B1 (0.052 ± 0.004), C1 (0.055 ± 0.016), D1 (0.059 ± 0.010), and F1 (0.061 ± 0.016) groups ($p \leq 0.05$). The proportion of Oscillospira in the A1 group (0.061 ± 0.007) was significantly higher than in the other groups ($p \leq 0.05$), and the D1 (0.050 ± 0.001) and E1 (0.053 ± 0.004) groups had significantly higher proportions than the B1 (0.039 ± 0.005) and F1 (0.043 ± 0.001) groups ($p \leq 0.05$). The proportion of Parabacteroides in the B1 (0.041 ± 0.004) and F1 (0.040 ± 0.005) groups was significantly higher than in other groups ($p \leq 0.05$), and the proportion of Parabacteroides of the C1 (0.032 ± 0.003) group was significantly higher than in the A1 (0.019 ± 0.002), D1 (0.017 ± 0.001), and E1 (0.013 ± 0.003) groups ($p \leq 0.05$). The proportion of Phascolarctobacterium in the A1 (0.038 ± 0.003) group was significantly higher than in the other groups ($p \leq 0.05$), and the B1 groups (0.029 ± 0.002) and C1 (0.032 ± 0.003) groups had significantly higher proportions than the groups D1 (0.019 ± 0.002), E1 (0.023 ± 0.003), and F1 (0.021 ± 0.003) ($p \leq 0.05$). The proportions of Prevotella in the B1 (0.104 ± 0.010) and C1 (0.106 ± 0.018) groups were significantly higher than those of the other groups ($p \leq 0.05$), and the A1 (0.066 ± 0.005) and E1 (0.074 ± 0.015) groups had significantly higher proportions than did the D1 (0.036 ± 0.005) and F1 (0.046 ± 0.005) groups ($p \leq 0.05$). Finally, the proportion of Ruminococcus in the A1 (0.043 ± 0.001), D1 (0.042 ± 0.004), and E1 (0.039 ± 0.006) groups was significantly higher than that of the B1 group (0.024 ± 0.004) ($p \leq 0.05$), whereas no significant differences were found between the C1 (0.037 ± 0.005) and F1 (0.033 ± 0.003) groups and the other four groups. On day 56 (Figure 5b), the percentage of Bacteroides in the E2 group (0.229 ± 0.006) was significantly higher than in other groups ($p \leq 0.05$), and the C2 (0.139 ± 0.007), D2 (0.144 ± 0.011), and F2 (0.156 ± 0.007) groups had significantly higher proportions than the A2 (0.109 ± 0.008) and B2 (0.116 ± 0.009) groups ($p \leq 0.05$). The proportion of Faecalibacterium in the D2 group (0.064 ± 0.006) was significantly higher than that of other groups ($p \leq 0.05$), and the proportion in the A2 group (0.026 ± 0.004) was significantly lower than in the B2 (0.035 ± 0.002), C2 (0.038 ± 0.005), E2 (0.036 ± 0.003), and F2 (0.036 ± 0.001) groups ($p \leq 0.05$). The proportion of Oscillospira in the A2 group (0.061 ± 0.007) was significantly higher than in other groups ($p \leq 0.05$), whereas the B2 (0.052 ± 0.004) and D2 (0.052 ± 0.007) groups had significantly higher proportions than the C2 (0.043 ± 0.003), E2 (0.042 ± 0.002), and F2 (0.036 ± 0.002) groups ($p \leq 0.05$). The proportion of Parabacteroides in the C2 group (0.050 ± 0.005) was significantly higher than in the other groups ($p \leq 0.05$), and there were significant differences between the D2 (0.044 ± 0.007), F2 (0.045 ± 0.002), and E2 (0.037 ± 0.003) groups ($p \leq 0.05$), but the A2 (0.039 ± 0.004) and B2 (0.042 ± 0.001) groups were not significantly different from the D2, E2, and F2 groups. The proportion of Phascolarctobacterium in the B2 group (0.037 ± 0.002) was significantly higher than in other groups ($p \leq 0.05$). Prevotella proportions in the B2 (0.100 ± 0.010) and E2 (0.103 ± 0.001) groups were significantly higher than those in the A2 (0.072 ± 0.011), C2 (0.058 ± 0.007), D2 (0.053 ± 0.007), and F2 (0.088 ± 0.001) groups ($p \leq 0.05$). The proportion of the F2 group was significantly higher than that of the A2, C2, and D2 groups ($p \leq 0.05$); and the proportion of the A2 group was significantly higher than that of the C2 and D2 groups ($p \leq 0.05$). Finally, the proportion of Ruminococcus in the D2 group (0.046 ± 0.005) was significantly higher than in the other five groups ($p \leq 0.05$). ## 3.7. Effect of Different MOLP Levels on the Clustering of Cecal Microflora at the Genus Level The heat map shown in Figure 6 shows the different cecal microbial groups clustered at the genus level by column. At the early stage (Figure 6a), the A1 group had a similar microbial proportion to that of the B1 group, and the D1 group had a significantly different microbial proportion compared to the A1 group, indicating that a $5\%$ increase in the cecal microbiota induced the most significant change during the early stage. However, in the later stage (Figure 6b), the A2 group had a similar microbial proportion to the F2 group, and the C2 group had a significantly different microbial proportion compared to the A2 group, which showed that an addition level of $3\%$ caused the most significant change in the cecal microbiota, while a higher addition level resulted in a cecal microflora proportion similar to that of the A2 group. ## 3.8. Effect of Different MOLP Levels on the Predominant Taxa of Each Group As shown in Figure 7, linear discriminant analysis effect size analysis (LEfSe) was used to identify the predominant taxa of each group. In the early stage (Figure 7a1), tested at different taxon levels, the most dominant bacteria in the A1 to F1 groups were Oscillospira, Paraprevotellaceae, Prevotella, RF39, Barnesiae, and YRC22, respectively. Alternatively, the most dominant bacteria during the later stage (Figure 7a2) in the A2 to F2 groups were Lachnospiraceae, Actinobacteria, Spirochaetes, Faecalibacterium prausnitzii, Bacteroidaceae, and Bacteroidetes, respectively. All dominant bacteria in each group could be distinguished by biomarkers. ## 4. Discussion The composition of the intestinal microbiota of chickens is directly related to growth performance and health [24,25]. As a potential superior feed resource, the substitution of rapeseed cake with different MOLP levels can have numerous advantages, and suitable replacement levels must be confirmed by analyzing growth performance, carcass characteristics, and changes in the cecal microbiota at different growth stages (day 28 and day 56). In terms of growth performance, an MOLP level of 1–$5\%$ in the early stage had a certain enhancement effect on daily weight gain, daily feed intake, and total weight gain of broilers, although in the later stage, there were no significant difference ($p \leq 0.05$) in all indicators of growth performance compared to the control group, but the growth effect of broilers in the test and control groups was similar. In terms of carcass characteristics, the slaughter rate of broilers reared at different levels of MOLP levels in the early and late stages was $83.6\%$, and the whole-clean-bore rate was above $62.5\%$. Both of these results showed that the use of MOLP as a protein ingredient for broiler rearing is feasible. In terms of changes in the cecal microbiota, as shown in Figure 1, the Shannon index and rarefaction curves for each sample reached the saturation plateau, which indicated that the sequencing depth of each sample had sufficient sequence coverage to accurately describe the bacterial composition. Usually, the number and structural composition of the organism’s intestinal flora have an important impact on the digestion, absorption, and health of the host. In the analysis of intestinal flora, the Ace and Chao indices are used to reflect the richness of the intestinal flora, and their values are proportional to the richness of the intestinal flora. The Shannon index and the Simpson index are related to the diversity of intestinal flora. The larger the Shannon index and the smaller the Simpson index, the higher the diversity of intestinal flora, and the richness and diversity of intestinal flora are positively correlated with the stability of the flora and the ability to resist pathogenic infection [26]. In this experiment, only the average values of the Chao, Ace, and Shannon index of the D1 and F1 groups were higher than those of the A1 group in the early stage, while the Simpson index values were equal to those of the A1 group, but the average values of the Chao, Ace, and Shannon indexes of D1 were higher than those of the F1 group, indicating that the addition of $5\%$ MOLP in the early stage could better enhance the richness and diversity of the intestinal flora of the broiler. The results indicated that the addition of $5\%$ MOLP in the first stage could better enhance the richness and diversity of the broiler intestinal flora. Although the addition of $9\%$ MOLP had similar effects on the richness and diversity of the intestinal flora of broiler chickens, their performance in terms of growth performance and carcass characteristics was not significantly different from that of the A1 group, while the performance of the D1 group in terms of growth performance and carcass characteristics improved significantly compared to that of the A1 group. The reason may be that the digestive system of broilers in the early stage has not been perfected, so there is a certain delay in adapting to the high level of MOLP, although the abundance and diversity of the intestinal flora were increased to some extent, but it takes some time for broilers to transform the nutrients into their own metabolites. Therefore, the performance of the F1 group was not better than that of the A1 group in terms of growth and carcass characteristics, although the abundance and diversity of the flora were higher than those of the A1 group. In the later period, only the mean Chao and Ace indices of the D2 group were higher than those of the A2 group, while the mean Shannon index of the D2 group was lower than that of the A2 group, and the mean Simpson index was equal to that of the A2 group. The mean Chao, Ace, and Shannon indices of the other groups were lower than those of the A2 group, while the mean Simpson index was equal to or close to that of the A2 group. Given that at the later stage, although the D2 group did not differ significantly from the A2 group in growth performance, the D2 group performed better in carcass characteristics, the optimal rate of MOLP addition at the later stage was still $5\%$. At the phylum level, the composition of the intestinal flora consisted mainly of Firmicutes and Bacteroidetes, and in this study, the dominant phylum of fecal micro-organisms was the same in all test groups, and the most abundant phylum was Firmicutes, Bacteroidetes, Actinobacteria, and Tenericutes; these findings are consistent with those of a previous report [27,28]. Current studies have shown that an increase in the abundance of Firmicutes has various effects, such as promoting fiber decomposition by intestinal flora, promoting energy absorption from food, and improving host body weight; while an increased abundance of Bacteroidetes may be related to host lean body size [19,29]. The results of this study showed that the addition of different levels of MOLP had inconsistent effects on the relative abundance of Firmicutes. On day 28, the addition of $1\%$ MOLP significantly reduced the relative abundance of Firmicutes ($p \leq 0.05$), and higher MOLP levels resulted in a higher relative abundance of Firmicutes. However, the relative abundance of Firmicutes was lower in all test groups than in the control group, while the relative abundance of Firmicutes in the D1 group was closest to that of the A1 group. The relative abundance of Bacteroidetes in the test group was numerically higher than that of the control group, and interestingly, the relative abundance of Bacteroidetes in the D1 group was also the closest to that in the A1 group. Furthermore, we observed that the D1 group had the lowest relative abundance of Actinobacteria among all groups and the highest relative abundance value of Tenericutes among all groups in the data at day 28. On day 56, the relative abundance of Bacteroidetes was higher in all test groups than in the A2 group, except for $5\%$ MOLP, which significantly reduced the relative abundance of Bacteroidetes ($p \leq 0.05$), while the relative abundance of Firmicutes was higher only in the D2 group than in the A2 group. Given that the D1 group excelled in growth performance and carcass characteristics and the D2 group excelled in carcass characteristics, we speculate that the $5\%$ level of MOLP may have been achieved by adjusting the relative abundance ratios of Firmicutes, Bacteroidetes, Actinobacteria, and Tenericutes. To more precisely analyze the effects of different MOLP levels on broiler chicken cecal microflora, the abundances of the genera of different groups were compared (Figure 5). In total, there were 8 dominant bacteria (relative abundance ≥$1\%$) at the genus level. Bacteroides were the most abundant; this genus is adept at degrading and assembling polysaccharides, especially crude fiber [30], and has positive impacts on host immune systems [31]. The addition of MOLP consistently significantly increased the relative abundance of Bacteroides ($p \leq 0.05$), except at $3\%$ on day 28 and $1\%$ on day 56. Therefore, the higher crude fiber content in MOLP may facilitate the growth of Bacteroides and, in turn, improve the health status of broiler chickens. Faecalibacterium consists of anaerobic bacteria that are negatively correlated with inflammatory bowel disease and colorectal cancer [32]; they play an important role in producing energy that colonocytes use and anti-inflammatory products, such as butyrate and salicylic acid, that benefit intestinal health [33]. Interestingly, the same levels of addition at different stages resulted in completely opposite effects on the abundance of Faecalibacterium, indicating that higher MOLP levels inhibited the growth of Faecalibacterium growth in early-stage chickens. Alternatively, in late-stage chickens, with the maturation of the broiler chicken digestive system and cecal microenvironment, MOLP addition supported Faecalibacterium reproduction. The data on growth performance and carcass characteristics of early and late broilers support the above results, i.e., higher levels of MOLP inhibited the growth of fecal bacteria in early-stage chickens and, thus, triggered a decrease in production performance and carcass characteristics in the early higher MOLP group. However, in the late stage, due to the gradual improvement of the broiler digestive system, higher levels of MOLP had a beneficial effect on the production performance and carcass characteristics of broilers. For Oscillospira, the same trend was observed in the different stages; increasing MOLP levels resulted in a significantly reduced relative abundance ($p \leq 0.05$). Oscillospira has been reported to be highly positively associated with leanness [34]; therefore, inhibition of this bacteria could improve the growth performance of broilers. Phascolarctobacterium was found to produce short-chain fatty acids such as acetate and propionate, which, in turn, exerted beneficial effects on the host [35]. The additional effects of MOLP were similar to their influence on Faecalibacterium; Phascolarctobacterium growth was more sensitive to MOLP addition in early-stage broiler chickens, whereas late-stage chickens had greater adaptive ability. Prevotella has been reported to play an essential role in carbohydrate utilization and can convert carbohydrates into acetic acid, succinic acid, isobutyric acid, and lactic acid, which can be used directly by their host [36,37]. Our data showed that at day 28, MOLP levels greater than $3\%$ significantly reduced the abundance of Prevotella ($p \leq 0.05$). Additionally, on day 56, the effect of addition was not consistent, and the abundance of Prevotella was significantly lower at MOLP levels of $3\%$ and $5\%$, the reason for which further investigation is needed. From the levels of the genus, we can see that different levels of MOLP can affect broiler growth performance and chicken carcass characteristics by decreasing the relative abundance of harmful bacteria in the Oscillospira genera and increasing the relative abundance of beneficial bacteria in the Bacteroides genera in the early and late stages. To analyze the similarity between each group, the cecal microbiota was clustered at the genus level by column on a heat map (Figure 6). On day 28, the groups A1 and B1 clustered together, indicating that the addition of $1\%$ MOLP slightly changed the cecal microbiota compared to the control group, and the further addition of MOLP significantly altered the cecal microbiota. Consequently, early-stage broiler chickens were sensitive to the addition of MOLP. Alternatively, on day 56, broiler chickens had a greater adaptive ability in the presence of higher MOLP levels, which was reflected by the difference in cecal micro-organisms between the A2 and F2 groups, which clustered. To identify the specific bacterial taxa present under the addition of different levels of MOLP, the cecal microbiota in different groups were compared by using the LEfSe method [38]. As tested at different taxon levels (Figure 7), in early-stage chickens, with the MOLP level increased to $5\%$, the most dominant bacteria changed from Oscillospira to Prevotella; this may be attributed to the higher crude fiber content in the diet [39]. On day 56, it was found that Oscillospira was still one of the most dominant bacteria in the A1 and A2 groups. Oscillospira has previously been reported to be positively associated with leanness [34], which may not help facilitate the rapid growth of broiler chickens, and we deduced that the higher content of antinutritional factors in the rapeseed cake may be the cause of the high abundance of Oscillospira [40]. ## 5. Conclusions Our study showed different changes in the cecal microbial ecosystems of broiler chickens at different stages, and its changes are closely related to the growth performance and carcass characteristics of broilers. To date, this is the first report to analyze the effects of MOLP on broiler chicken cecal microbial diversity by using 16S rRNA gene sequencing. The data revealed that MOLP treatment significantly affected broiler chicken cecal microflora. Changes in microbiota usually have an impact on broiler growth performance, carcass characteristics, and other indicators, while our study also showed that late-stage chickens have a stronger ability to adapt to MOLP than early-stage chickens. This study showed the potential use of MOLP as a substitute for rapeseed cake and other protein source feeds. By comprehensively evaluating the effects of different levels of MOLP on the cecum microbiota, growth performance, and carcass characteristics of broilers, it was found that the optimal level of MOLP addition for broilers was $5\%$ in both the early and late stages. ## References 1. Stohs S.J., Hartman M.J.. Review of the Safety and Efficacy of Moringa oleifera. Phytother. Res. PTR (2015) 29 796-804. DOI: 10.1002/ptr.5325 2. Bassey K., Mabowe M., Mothibe M., Witika B.A.. Chemical Characterization and Nutritional Markers of South African Moringa oleifera Seed Oils. Molecules (2022) 27. DOI: 10.3390/molecules27185749 3. 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--- title: Addressing Antimicrobial Stewardship in Primary Care—Developing Patient Information Sheets Using Co-Design Methodology authors: - Ruby Biezen - Stephen Ciavarella - Jo-Anne Manski-Nankervis - Tim Monaghan - Kirsty Buising journal: Antibiotics year: 2023 pmcid: PMC10044618 doi: 10.3390/antibiotics12030458 license: CC BY 4.0 --- # Addressing Antimicrobial Stewardship in Primary Care—Developing Patient Information Sheets Using Co-Design Methodology ## Abstract Antibiotic resistance is a threat to global health, and inappropriate antibiotic use can be associated with adverse effects. Developing tools to encourage better communication between patients and general practitioners may reduce inappropriate use of antibiotics. The aim of the study was to develop shared decision support tools on common infections using a co-design methodology to address antimicrobial stewardship (AMS) in primary care. Three co-design/interview sessions were conducted with primary care providers and consumers between October 2019–April 2020 in Melbourne, Australia. Participants critiqued existing AMS tools, identified key elements required and optimised resulting prototypes. Primary care providers and consumers prioritised information to include in the AMS tools, such as when to see a doctor, management options, disease symptoms and cause of infection differently. However, both agreed content should be communicated in a plain, concise and logical manner, using inclusive and simple language accompanied by illustrations. Information sheets should be single-sided and A4-sized, appropriate for use before, during or after consultations. Co-design provided a collaborative forum to systematically design and develop products that meet the needs of both primary care providers and consumers. This resulted in the development of seven patient information sheets on common infections that encourage discussion of these infections, conservative management options and appropriate antibiotic use in primary care. ## 1. Introduction Inappropriate antibiotic use is associated with increased antibiotic resistance, resulting in antibiotics no longer being effective, posing both immediate and long-term threats to human health [1,2]. Australia is one of the highest prescribers of antibiotics per capita, at double the rate of the lowest prescribers, such as Sweden and The Netherlands [3]. Most antibiotic prescribing in Australia occurs in primary care; it is, therefore, important that stewardship strategies are targeted for that setting. Studies have indicated inappropriate prescribing of antibiotics may be due to many factors. These may include diagnostic uncertainty [4,5], physicians’ perceptions that patients would be more satisfied with the consultation if antibiotics were prescribed [6,7], physicians’ perceptions that patients expect antibiotics to be prescribed during consultations [8,9,10], and patients’ demands for antibiotics [11]. Recommendations from these studies included: encouraging better communication between patients and healthcare providers, using decision aids to assist shared decision-making between patients and healthcare providers, and delivering patient education on common infections. The Clinical Care Standards in antimicrobial stewardship [12] specify that patients should expect to receive information about their clinical condition, its natural history and the treatment options available to them in a form that they can understand. Shared decision-making, where healthcare providers and patients discuss the benefits and risks of treatment options, can be used to guide healthcare decisions to help ensure clinical management meets patient expectations for health outcomes [13]. Patient decision aids have been developed for use in the areas of mental health, screening choices and treatment options, and antibiotic use [14,15,16,17,18,19]. They have been shown to be effective in reducing decisional conflicts [14], supporting patients with complicated treatment options [16], and encouraging shared decision-making [17]. However, many patient decision aids are not based on current evidence or have not been updated [20]. In addition, many are in clinical use despite limited studies to explore their effectiveness [21]. Therefore, a framework is needed to ensure new tools and resources for shared decision-making are developed with appropriate interpretation of evidence and adequate stakeholder review. Currently, there are few patient decision aids for antibiotic use available in Australia [22]. Importantly, even fewer have been developed in consultation with both healthcare providers and patients using a documented, transparent process, nor have they been formally assessed for acceptability in the clinical setting. Therefore, the aim of this study was to co-design shared decision support tools with primary care providers (including general practitioners (GPs), a practice nurse, and a pharmacist) and consumers (members of the general public who attend a general practice at least once a year) to promote antimicrobial stewardship (AMS) in primary care. ## 2.1. Participants Data from the two co-design sessions and the participant interviews were collected between October 2019 to April 2020. Participants included five primary care providers (three GPs, a practice nurse and a pharmacist) and six consumers (an older male, an older female, a mother of a young child, a father of a teenager, a single male, and a 19-year-old male). Please see Table 1. ## 2.2. Co-Design Sessions and Interview Findings In the first co-design session, participants were asked to rank 11 categories of information found in commonly used patient information sheets (e.g., cause, symptoms, natural history of the illness, when to see a GP, prevention, risks of antibiotics) in order of importance before and after critiquing five patient information sheets commonly used in primary care. For primary care providers, ‘when to see a GP’ was considered the most important information to include in patient information sheets and the risks and benefits of antibiotics were considered the least important (Figure 1). The primary care provider ranking of the most and least important factors did not change after critiquing the five common patient information sheets. In contrast, consumers initially prioritised the definition and management options for their condition as the most important information. However, after critiquing the common information sheets, consumers ranked the most common symptoms and the natural history of the disease as the most important information to include. Information regarding prevention was considered the least important by consumers both before and after critiquing the common patient information sheets (Figure 1). This exercise demonstrated the differences between primary care providers and consumers in what they consider to be the most important information to include in patient information sheets. Qualitative data from the two co-design sessions and subsequent participant interviews were analysed. Six major themes emerged: content, communication of content, design, delivery and access, usability, and engagement. ## 2.2.1. Content While critiquing common patient information sheets during the first co-design session, participants suggested that the most important content needed to be displayed in a format that was engaging for both primary care providers and patients. Primary care providers often stated that information regarding ‘danger signs’ and ‘when to seek healthcare advice’ was important to manage medico-legal risks and to ensure patients were not dissuaded from seeking medical advice when needed. Having a generic warning in the shared decision support tools recommending that patients seek professional advice if they were unsure about whether they needed antibiotics was considered important, especially for at-risk patient groups, including those with chronic medical conditions. Primary care providers all thought including information on bacterial versus viral pathogenesis of infections was too complex. Similarly, they reported that if antibiotics are not indicated for the condition addressed by shared decision support tools, there was little point in including information about the risks and benefits of antibiotics in that document. ## 2.2.2. Communication of Content The use of simple, concise language suited to a Year 5 reading level (children aged between 10–11) was considered essential for communicating the necessary information. Avoiding complex medical terminology was also seen as particularly desirable. It was considered important to use generic language where possible. However, it was acknowledged that confusion could arise because of the different brand names of medications patients may be familiar with. ## 2.2.3. Design Bold font for headings and a balance between the use of words compared to pictures were considered significant design elements. Some design features became especially apparent when participants were asked to critique existing resources in co-design session 1, suggesting that important healthcare messages could otherwise be lost. Participants thought the information sheets needed to be engaging and that important content needed to be highlighted to make it easy for the patients to locate without having to search for relevant information. Both primary care providers and consumers considered it crucial that all the information was kept to a single-sided A4-sized page. Including relevant pictures next to subheadings, along with the use of colour, was viewed as important for increasing readability. However, colour printing was deemed an expensive resource in a general practice environment; therefore, black and white copies were considered an acceptable alternative. ## 2.2.4. Delivery and Access Participants were encouraged to consider different delivery modes for the shared decision support tools. Having them available for patients to view in the community before the consultation was seen as an effective means of promoting the AMS message. Handouts, websites and online platforms were also seen as convenient modes of access to promote AMS both before and after GP consultations. Participants agreed that handouts were the most appropriate form to promote AMS during a consultation. These handouts could be integrated into the practice management software and printed by a GP during a consultation or transferred to patients electronically. Using electronic delivery modes to distribute the shared decision support tools could give rise to barriers for patients without internet access or the ability to print the documents. Participants also suggested that different modes of delivery would be required to ensure diverse groups within the community could access the information sheets. These might include translating these information sheets into different languages to accommodate culturally and linguistically diverse patients. ## 2.2.5. Usability The usability of the shared decision support tools was discussed at length during participant interviews. A key principle that emerged was that the tools needed to be readily accessible to facilitate use. Primary care providers identified that using the shared decision support tools might help save time during consultations for common infections and shorten the duration of such consultations. ## 2.2.6. Engagement During the first co-design meeting, participants suggested the shared decision support tools could make consumers aware of effective management options other than antibiotics. Furthermore, consumers might refer to the tools after the consultation had ended, potentially increasing their education about AMS. Participants also said they were able to recognise non-antimicrobial management strategies for common infections by engaging with the shared decision support tools. During the interviews, participants reported the draft patient information sheets contained sufficient information to allow consumers to decide whether they needed to see a primary care provider for the given condition and whether antibiotics may be needed. At the end of the third co-design/participant interview phase, participants thought the shared decision support tools they helped co-design were easy to understand, concise, relevant, and consistent. Minor changes were suggested to wording, such as replacing ‘complex medical conditions’ with ‘other medical conditions’. The outcome of this study was the development of seven shared decision support tools in the form of patient information sheets: acute bronchitis, middle ear infection, nose & sinus infection, sore throat, urinary tract infection, cellulitis and leg ulcers. ## 3. Discussion The focus of this study was to develop robust shared decision support tools using a co-design methodology to promote appropriate antibiotic use in primary care. The co-design methodology provided a collaborative forum with a diverse group of participants to identify their key priorities and needs. This methodology allowed us to systematically design and develop a product that is acceptable and meets the needs of both primary care providers and patients. Together participants advised on the attributes of the shared decision support tools required to address their specific needs, including attributes related to the content, design, usability, communication, and access and delivery of the tools. Both primary care providers and consumers emphasised the importance of communication using simple, concise and inclusive language. Design elements such as layout and formatting, with a balance between the use of words and pictures, were required to engage users and improve usability. Information needed to be unambiguous and relevant to the patient’s condition. Complex diagrams and numbers were not favoured as they were confusing to some. Tools such as patient information sheets and decision aids that contain relevant information can therefore assist patients in the decision process [13,23,24]. Participants also provided insights into suitable delivery modes and access points for using the shared decision support tools. Depending on the condition addressed and the availability of health care access, participants wanted these tools to be available before, during and after a GP consultation to improve patient education regarding common infections and to support them in deciding when to see their GP. This was particularly important because patients would often present to pharmacists for advice if they could not secure a GP appointment. It would therefore be imperative that the shared decision support tools are made available to all primary care providers to ensure uniform messaging to patients and to avoid patient confusion and misinterpretation. The results from the co-design sessions showed that primary care providers and consumers prioritised what information is needed in the shared decision support tools differently. GPs and patients often have dissonant views when it comes to assessing and managing health outcomes [25,26,27]. In our study, consumers preferred to know more about common symptoms and the natural history of a condition rather than when to see a GP. Previous studies have also shown that primary care providers and patients have conflicting views, especially when it comes to antibiotic prescribing and use [27]. GPs sometimes perceive patients wanting a diagnosis, along with the reassurance that they are not severely ill, as pressure to prescribe antibiotics [4,27,28,29]. This suggests that communication is crucial to align primary care providers’ and patients’ expectations regarding appropriate antibiotic prescribing and use. It is interesting to note that while common patient information sheets often included risks and benefits of antibiotics, these were considered as least important by our participants. This exercise demonstrated how important a co-design process (which involves end-users in the development process) is when developing any shared decision support tool. Participants overwhelmingly wanted information sheets that included information about disease symptoms, signs to look out for, prevention, management and treatment strategies, and when to see a GP. While the initial aim of the study was for the shared decision support tools to only include categories of information found in common information sheets, which were prioritised as most important in the first co-design session, through the co-design process, we were able to include all 11 categories in the final product for the seven information sheets (Please see Files S1 and S2). These information sheets can therefore be used not only to encourage communication between primary care providers and patients but also to provide patients with a useful educational resource to refer to before or after a GP consultation [30,31]. As far as we are aware, this is one of the first studies that applied a co-design methodology to systematically develop shared decision support tools for AMS in primary care. Enabling both primary care providers and consumers to work directly with each other to develop seven patient information sheets was one of the strengths of this study. Instead of a traditional development of a tool/product designed by content experts, we gain an understanding of different participant perspectives, supporting the development of a more robust product. In addition, because the shared decision support tools were cross-referenced with Therapeutic Guidelines (Australia’s nationally endorsed guidelines), the tools are an evidence-based product. The COVID-19 pandemic brought research-specific challenges to this study. We were unable to complete the third face-to-face co-design session as scheduled, but we were able to conduct one-on-one interviews with each of the participants. These interviews provided participants with the opportunity to privately share their perspectives rather than in a group environment. The feedback from the co-design sessions and the interviews were found to be consistent across all participants, hence validating the reliability of the participant responses. The strong relationships built with research participants throughout the study, as well as the collective belief in the value and importance of reducing inappropriate antibiotic use in the community, allowed us flexibility to adapt our study in light of these challenges. The resulting product is robust, evidence-based, and has the potential to reduce inappropriate antibiotic prescribing and use in primary care. ## 4.1. Study Design This co-design study was designed to consist of three co-design sessions with primary care providers and consumers. Co-design sessions 1 and 2 were facilitated by the lead researcher (RB) with assistance from the medical student (SC), a GP (J-AM-N) and an infectious disease physician (KB). Due to the COVID-19 pandemic, the third co-design session was replaced by individual participant interviews. ## 4.2. Participant Recruitment Participants consisted of three GPs, a practice nurse, a pharmacist, and six patients/consumers that attend a general practice for the majority of their medical care. They were recruited via the research team’s professional network, such as the National Centre for Antimicrobial Stewardship and the Victorian primary care practice-based Research and Education Network (VicREN) [32]. An advertisement for the project, a plain language statement and a consent form were sent to potential participants via email. Interested participants contacted the research team, who explained the project in detail and answered any questions. Consumers were recruited via the research team’s professional network, including consumer groups at the University. A diversity of consumers were recruited based on their age (older and younger participants) and family status (single, married and/or with children). All consent forms were signed by participants before commencement at each of the three sessions. ## 4.3.1. Co-Design Session 1 The first co-design session focused on what information participants considered important to include in patient decision aids/information sheets and by what mode(s) this information should be delivered. Using bronchitis as an example, participants were asked to rank 11 categories of information (common classifications in patient information sheets, e.g., cause, symptoms, natural illness, when to see a GP, prevention, risk of antibiotics) from most important to least important. Participants were then asked to critique five common patient information sheets, discussing what they liked and disliked about those information sheets. Participants were then asked to rank the 11 categories of information again in order of importance. Following this, participants provided feedback on how to optimise the information and delivery of the information in those materials. The session was recorded on video and audio. For category rankings, each category was given a number from 1 to 11. The numbers were added together and grouped according to each category, for each participant group (primary care providers or consumers) as ‘before’ and ‘after’ critiquing the five common patient information sheets. Differences were recorded and compared. A positive difference showed an increase, and a negative difference showed a decrease from ‘before’ to ‘after’. Results were analysed descriptively in Excel. The audio of the co-design session was transcribed verbatim, any identifying information was removed, and common themes were identified using thematic analysis. Following data analysis of the first co-design meeting, the research team drafted content for the shared decision support tools, which aligned with Therapeutic Guidelines [33]. This content was reviewed by a content expert group consisting of two GPs, one pharmacist, one infectious diseases physician, one aged care nurse and two microbiologists. The agreed draft content for a shared decision support tool focusing on bronchitis was provided to a graphic designer to develop the first prototype tool. ## 4.3.2. Co-Design Session 2 The second-co-design session focused on the content, format, and layout of the bronchitis prototype of the shared decision support tool. The content was also discussed for six other conditions (tonsillitis, otitis media, rhinosinusitis, urinary tract infection, leg ulcers and cellulitis). These conditions were selected as they were the most common conditions resulting in antibiotics being prescribed in primary care conducted from a previous study [34]. The session was recorded on video and audio. The audio was transcribed verbatim, any identifying information was removed, and common themes were again identified using thematic analysis. Feedback from the second co-design session was reviewed by the research team. The graphic designer refined the bronchitis information sheet based on the feedback received and applied the resulting formatting to information sheets for the other 6 conditions. ## 4.3.3. Session 3/Participant Interviews The third co-design meeting was scheduled for March 2020. However, due to COVID-19 restrictions enforced in Melbourne, Australia, it was replaced with individual semi-structured interviews that were conducted by the Zoom online platform or via telephone. In these interviews, conducted by RB and/or SC, participants were asked to provide feedback on the information sheets and how these tools could be delivered and used. Questions for primary care providers included: ‘Can you explain how you would use the shared decision support tool in your work? Can you provide an example?’ and ‘What form(s) do you think would be appropriate for use before/during/after a consultation with a patient’? Similarly, for consumers, questions included: ‘in what circumstances do you think the shared decision support tools will be helpful to you?’ These interviews were recorded and subsequently transcribed and analysed thematically. Drafts of the seven patient information sheets were also mailed to all participants with a request to provide written comments on the layout and content of the drafts. These were returned to the researcher via reply paid Express Post. Marked-up information sheets were summarised, and changes were provided to the graphic designer for refinement. Qualitative data were analysed using a deductive thematic analysis approach using NVivo 12 (QSR International, Doncaster, Australia). The coding scheme was developed by grouping recurrent ideas during data analysis and refined under themes and subthemes. Data were coded independently by two researchers (SC and TM), with differences in perspective negotiated with a third researcher (RB) until a consensus was reached. The coding structure was discussed with the research team and refined until full agreement was attained. Overlapping themes were discussed as to how best to describe these themes to avoid repetitiveness. The thematic coding scheme adopted by the coders was described under six headings: content, communication of content, design, delivery and access, usability, and engagement. Ethics approval was obtained from The University of Melbourne General Practice Human Ethics Advisory Group (Ethics ID: 1954925.1). ## 5. Conclusions This study demonstrated that primary care providers and consumers have different priorities when it comes to what information is important for them to include in shared decision support tools. Our results indicated the importance of a co-design process involving end-users in the development when creating any such tool. Using this process, primary care providers and consumers were able to come to a mutual agreement on the content, format and layout, delivery mode and access of these shared decision support tools. This resulted in an end product that was simple, clear, and attractive to both primary care providers and patients to use. These patient information sheets summarised disease symptoms and signs, prevention, management and treatment strategies, and when to see a GP. 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--- title: Phenotypic and Genotypic Investigation of Carbapenem-Resistant Acinetobacter baumannii in Maharaj Nakhon Si Thammarat Hospital, Thailand authors: - Sirijan Santajit - Phuangthip Bhoopong - Thida Kong-Ngoen - Witawat Tunyong - Dararat Horpet - Wanfudhla Paehoh-ele - Tasneem Zahedeng - Pornpan Pumirat - Nitat Sookrung - Woranich Hinthong - Nitaya Indrawattana journal: Antibiotics year: 2023 pmcid: PMC10044629 doi: 10.3390/antibiotics12030580 license: CC BY 4.0 --- # Phenotypic and Genotypic Investigation of Carbapenem-Resistant Acinetobacter baumannii in Maharaj Nakhon Si Thammarat Hospital, Thailand ## Abstract [1] Background: Acinetobacter baumannii is well known as a causative agent of severe hospital-acquired infections, especially in intensive care units. The present study characterised the genetic traits of biofilm-forming carbapenem-resistant A. baumannii (CRAB) clinical isolates. Additionally, this study determined the prevalence of biofilm-producing A. baumannii isolates from a tertiary care hospital and investigated the association of biofilms with the distribution of biofilm-related and antibiotic resistance-associated genotypes. [ 2] Methods: The 995 non-duplicate A. baumannii isolates were identified, and their susceptibilities to different antibiotics were determined using the disk diffusion method. Using the modified microtiter plate assay, the CRAB isolates were investigated for their biofilm formation ability. Hemolysin and protease activities were determined. CRABs were subjected to polymerase chain reaction (PCR) assays targeting blaVIM, blaNDM, blaIMP, blaOXA-23-like, blaOXA-24-like, blaOXA-51-like, csuE and pgaB genes. Individual CRAB isolates were identified for their DNA fingerprint by repetitive element sequence-based (REP)-PCR. [ 3] Results: Among all A. baumannii isolates, 172 CRABs were identified. The major antibiotic resistance gene among the CRAB isolates was blaOXA-51-like ($100\%$). Ninety-nine isolates ($57.56\%$) were biofilm producers. The most prevalent biofilm gene was pgaB ($79.65\%$), followed by csuE ($76.74\%$). Evidence of virulence phenotypes revealed that all CRAB exhibited proteolytic activity; however, only four isolates ($2.33\%$) were positive for the hemolytic-producing phenotype. REP-PCR showed that 172 CRAB isolates can be divided into 36-DNA fingerprint patterns. [ 4] Conclusions: The predominance of biofilm-producing CRAB isolates identified in this study is concerning. The characterisation of risk factors could aid in controlling the continual selection and spreading of the A. baumannii phenotype in hospitals, thereby improving patient care quality. ## 1. Introduction Acinetobacter baumannii is a gram-negative coccobacillus responsible for hospital-acquired infections, such as ventilator-related pneumonia, secondary meningitis, skin and soft tissue infections, bacterial septicaemia, urinary tract infections and burn wound infections, particularly in intensive care units (ICUs) [1,2,3]. These infections are often related to a higher mortality rate of up to $26\%$ in hospitalised patients [4]. For ICU patients, the rate ranges from $4\%$ to $43\%$ [5]. A. baumannii is the first organism on the World Health Organisation (WHO)’s prioritised list of concerns that pose a significant risk to human health [6]. Recently, the situation has become more severe due to a significant increase in multidrug-resistant (MDR). In addition, A. baumannii nosocomial isolates were extensively drug-resistant (XDR) and pandrug-resistant (PDR), some of which were even resistant to tigecycline and colistin, the terminal therapies [1]. Numerous factors have been implicated in the spread of A. baumannii as an endemic pathogen throughout the world’s healthcare facilities, including its distinct intrinsic and acquired resistance to several antimicrobial classes, including penicillins, extended spectrum cephalosporins, fluoroquinolones and carbapenems [2], as well as its ability to form a biofilm and persist on biotic and abiotic surfaces, including environmental surfaces and medical equipment [3,7]. Moreover, they can uptake bacterial genetic elements to survive under harsh conditions and antibiotic treatment [8]. Carbapenemase production is the most concerning mechanism in the evolution of carbapenem resistance in A. baumannii. They belong to three of the four β-lactamase groups, A, B and D [9]. The OXA β-lactamases are Class D carbapenemases, which are further classified into multiple subgroups, primarily encoded by blaOXA-23-like, blaOXA-24-like, blaOXA-58, blaOXA-48-like, blaOXA-51-like and blaOXA-143-like [9,10]. The major OXA type β-lactamase in Acinetobacter species is blaOXA-51-like, chromosomally encoded and unique to these species but may confer carbapenem-resistance when its expression is up-regulated via genetic reorganisation [11]. The metallo-beta-lactamases (MBLs) belong to Class B carbapenemases, most notably the New Delhi carbapenemase blaNDM [12]. Moreover, the ability of A. baumannii to live on abiotic surfaces, such as catheters and endotracheal tubes, poses a significant barrier to infection control. The capacity to form biofilms is a major factor influencing A. baumannii’s capability to thrive in harsh circumstances, making it a key virulence factor [13]. Compared to non-MDR A. baumannii (5–$24\%$), MDR A. baumannii has a significantly higher rate of biofilm production (80–$91\%$) [14]. Additionally, several investigations have emphasised the function of biofilms in defending A. baumannii against the host immune defence [15]. As a result, biofilm-forming bacteria may cause problematic infections. Owing to the capacity of A. baumannii to produce a biofilm the bacteria can thrive, adhere to mucosal surfaces, maintain dormancy in deep biofilm layers and persist in a hospital environment under stress conditions [1,8,16]. Several virulence-associated elements are involved in A. baumannii biofilm formation, including the two-component system (BfmS/BfmR), chaperon-usher pilus (Csu) encoded by the csu operon, the outer membrane protein A (OmpA) expressed by the ompA gene, the biofilm-associated protein (Bap) encoded by the bap gene, poly-β-[1,6]-N-acetyl glucosamine (PNAG), the biosynthesis of extracellular exopolysaccharide (EPS) encoded by pgaB and the quorum sensing system [17]. By providing insight into the prospective relationship between A. baumannii clinical isolates, biofilm production, virulence traits and antibiotic resistance characteristics may help enhance infection control practices in healthcare institutions. Although earlier clinical and epidemiological studies thoroughly investigated the relationship between virulence, biofilm production and antibiotic resistance gene associations and some reports included whole-genome sequencing, the studies in Thailand are still negligible and contradictory [3,16,17,18,19,20,21,22]. Thus, the present study evaluated the occurrence of carbapenem-resistant A. baumannii (CRAB) isolates. The biofilm formation capacity of these clinically significant strains was investigated, as were their biofilm-associated genes, csuE and pgaB, drug resistance-related genes, such as blaVIM, blaNDM, blaIMP, blaOXA-23-like, blaOXA-24-like and blaOXA-51-like and molecular typing based on repetitive element sequence-based-polymerase chain reaction (REP-PCR) in CRAB clinical isolates. The virulence feature analyses entailed the hemolysis and protease activities. ## 2.1. Distribution of CRAB Isolates and Antimicrobial Profiles From 995 A. baumannii non-duplicate isolates, the highest resistance rates were observed against meropenem ($100\%$), imipenem ($100\%$), doripenem ($100\%$), ceftazidime ($99.42\%$), gentamicin ($85\%$), ciprofloxacin ($83.40\%$) and amikacin ($76.7\%$), respectively [Figure 1, Table 1]. The analysis for resistance to different antibiotic classes revealed that $100\%$ of the isolates were MDR. Among them, 172 isolates ($17.29\%$) were CRAB. ## 2.2. Hemolysis and Protease Activities In this study, only $\frac{4}{172}$ ($2.33\%$) CRAB isolates were positive for hemolysis activity, and all of them have a beta-hemolysis phenotype. The primary proteolytic screening results showed that all isolates were positive for protease production based on the formation of the halos zone of hydrolysis around the reaction colonies. ## 2.3. Biofilm Formation Phenotypes of CRAB Among all CRAB isolates examined for biofilm formation, 99 ($57.56\%$) were biofilm producers and 73 ($42.44\%$) were non-biofilm producers. The CRAB biofilm-producing strains were divided into three categories: 40 ($23.26\%$) were weak biofilm producers, 39 ($22.67\%$) were moderate biofilm producers and 20 ($11.63\%$) were strong biofilm producers. Heatmap analysis demonstrated differences in the antibiotic resistance profiles of CRAB clinical isolates, which varied in biofilm formation capacity. In Carbapenem-resistant groups (IMP, MEM and DOR), there are a high number of isolates showing strong biofilm formation [Figure 2]. ## 2.4. Prevalence of Drug Resistance, Biofilm-Related Genotypes and Genetic Diversity of CRAB In this study, CRAB isolates harboured the drug resistance-associated genes, including the blaOXA51-like gene, at a rate of $100\%$ (172 isolates) and were the most prevalent genotype. In contrast, the blaVIM, blaIMP, blaNDM and blaOXA23-like genes were present in 137 isolates ($79.65\%$), one isolate ($0.58\%$), 21 isolates ($12.21\%$) and 161 isolates ($93.60\%$), respectively. The blaOXA24-like gene was not found in CRAB isolates. The PCR experiments showed positive results for biofilm-related genes in only the csuE gene in 19 ($11.05\%$) isolates, only the pgaB gene in 24 ($13.395\%$) isolates and both the csuE and pgaB genes in 113 ($65.70\%$) isolates. However, there were 16 ($9.30\%$) isolates that did not carry both genes. Our findings revealed a high prevalence of biofilm-forming and biofilm-related genes (csuE and pgaB) in CRAB strains in the study region [Table 2]. Moreover, Figure 3 depicts an unweighted pair group method with an arithmetic mean dendrogram derived from REP-PCR of 172 CRAB strains. The REP-PCR cluster for bacteria with at least $70\%$ coefficient similarity generated 36 clonal diversities. ## 3. Discussion Recently, the role of A. baumannii in nosocomial infections, its notable ability to develop antimicrobial resistance and its involvement in severe clinical infections have raised considerable attention [23]. Treating these bacteria, particularly MDR and broad-spectrum beta-lactamase strains, is of critical importance [24]. Carbapenems are currently the treatment of choice for MDR A. baumannii infections that are resistant to third generation cephalosporins. However, the number of carbapenem-resistant strains is rising [25]. As a result of the large prevalence rate of this infection and the varying patterns of antibiotic resistance in various geographical locations, the surveillance of the prevalence rate and antibiogram in different parts of the world is crucial. These data would help determine the distribution of resistance patterns and select the most suitable medication regimen [26]. In our investigation, 172 of the 995 A. baumannii isolates from clinical samples were carbapenem-resistant. Moreover, all CRAB isolates were MDR ($100\%$). The resistance to the cephalosporin antibiotics, gentamicin and ciprofloxacin, was more than $80\%$. All isolates were resistant to imipenem, meropenem and doripenem. The antibiotic resistance in the present study was similar to the reports from Iran [27]. Almost all the isolates in our study carried the blaOXA-51-like genes. The intrinsic blaOXA-51-like gene’s identification of the A. baumannii isolates provided proof of their identity. The blaOXA-51-like genes are an easy and robust tool for identifying A. baumannii [28,29,30]. These carbapenem-resistant isolates have been attributed to a strong promoter-driven upregulation of the blaOXA51-like genes when related to the ISAba1 gene 7 bp upstream, as previously reported [31]. The blaOXA-51-like gene was the most frequently detected carbapenemase gene among all clinical CRAB isolates ($100\%$) in the current study, and it is also commonly found in health care facilities worldwide, followed by the blaOXA-23-like, blaVIM, blaNDM and blaIMP genes at $93.60\%$, $79.65\%$, $12.21\%$ and $0.58\%$, respectively. Furthermore, the spread of CRAB carrying carbapenem-resistance genes was proven in many reports from other countries [26,28,32,33,34]. Additionally, none of the isolates in this study possessed the blaOXA-24-like gene, which is consistent with others [35,36,37,38]. The finding is presumed to correlate with clonal expansion [37,38,39]. Moreover, the resistance to carbapenems in these strains may be clarified by the participation of other mechanisms of resistance to carbapenem, such as the modified permeability or additional carbapenemase enzymes not examined in this study. Examples of non-carbapenemase carbapenem-resistance mechanisms in A. baumannii include reduced membrane porin density [40], decreased drug affinity caused by PBP downregulation [41] and efflux pump (EP) mechanisms. A. baumannii’s pathogenicity and resistance to unfavourable environmental conditions correlate with numerous virulence factors, such as the capacity to generate hemolysin, lipase, lecithinase and protease, as well as the ability to form biofilms and quorum sensing [42,43]. The growth behaviour of bacteria in biofilms is altered, reducing their susceptibility to specific antimicrobial treatments [44]. Along with known traditional drug-resistant mechanisms, alternative strategies contribute to the resilience of bacteria in biofilms, such as slow or partial permeation of antimicrobial drugs into the biofilm, a unique microenvironment in the biofilm and altered growth behaviour of microbes within biofilms. Since biofilms are multicellular, these mechanisms result in bacterial resistance and unsuccessful treatment efforts [45]. Approximately $57.56\%$ of the CRAB isolates in this study were biofilm-forming strains. According to previous studies, biofilm-forming bacteria have a substantially longer life than those that do not form biofilms (36 versus 15 days, p-value < 0.001) [46,47]. The capacity of A. baumannii to form biofilms improved colonisation and persistence, allowing for higher rates of nosocomial infections, particularly device-associated illnesses [48]. Several factors, including environmental factors and numerous cell signals, influence A. baumannii biofilm development by influencing signalling, cell-to-cell interaction and scaffolding functions [49]. Furthermore, the genes associated with biofilms offer a comprehensive perspective on surface adhesion and biofilm development. Among these genes were pgaB and csuE [50]. The Csu chaperone-usher pili assembly system is regulated by the BfmS/BfmR two-component system (pgaB), and pgaABCD is responsible for producing poly-1,6-N-acetylglucosamine [51]. The initial surface attachment phase of biofilm formation is mediated by pili, composed of proteins encoded by the csu operon. Previous research demonstrated that BfmR stabilised the transcription of the csu operon genes, which are vital in biofilm formation [52,53]. The presence of pgaB and csuE as biofilm encoding genes in CRAB isolates from admitted patients was studied. Our results revealed that pgaB and csuE were present in most isolates similar to those reported by Zeighami et al. [ 3] who reported these genes in A. baumannii recovered from ICU patients. In this study, the vital virulence activities of CRAB illustrated the evidence of biofilm production, hemolysis and protease activities. This finding is consistent with a previous study by Dahdouh and Hajjar [54], which revealed that the isolates produced biofilm, caused blood hemolysis in an agar plates and exhibited proteolytic activity. PCR-based fingerprinting methods, such as REP-PCR, are easy, quick and low-cost, with strong discriminatory power for identifying A. baumannii. The results of REP-PCR can be achieved in a reasonably short amount of time, as demonstrated by a review by Sabat et al. This is also the reason this procedure is less expensive. REP-PCR is highly discriminating for several bacterial species [55]. Previous research found that the REP-PCR discrimination power was adequate and correlated with PFGE [56,57]. REP-PCR was helpful in the epidemiological analysis of hospital epidemics. Many investigations employed PCR-based fingerprinting to identify A. baumannii clinical isolates [3,58]. By typing A. baumannii with REP-PCR, the obtained patterns were classified as distinct REP-PCR clusters that provided evidence of possible clonal expansion among the different isolates. Despite several reports of clonality in the literature, it is possible to identify the levels of clonal diversity among the CRAB strains. However, the underlying molecular reason for the bacterium’s rising prevalence and antibiotic resistance still needs to be fully understood. Molecular detection and whole-genome sequencing should be employed to better understand drug resistance and bacterial pathogenesis mechanisms. Furthermore, integrating molecular, genomic and bioinformatics tools resulted in genomic epidemiology approaches, which gradually increased in various fields of pathogen surveillance and developed control strategies. ## 4.1. Study Settings and Ethical Approval The non-duplicate 995 A. baumannii isolates were attained from February to September 2021 at the Maharaj Nakhon Si Thammarat Hospital. The study was approved by the Human Research Ethics Committee of Walailak University (protocol number: WUEC-21-027-01). ## 4.2. Bacterial Isolation and Identification The stored isolates were recovered and confirmed as A. baumannii by conventional microbiological methods such as gram stain, gram-negative coccobacilli; the oxidase test, negative; triple sugar iron test, K/N; Simmons citrate agar, positive; motility-indole-lysine, negative-negative-positive; OF-maltose, non-oxidiser; OF-glucose, oxidiser and growth on MacConkey agar at 42 °C [59]. ## 4.3. Antimicrobial Susceptibility Testing The Kirby–Bauer disc diffusion method was used to determine the antimicrobial susceptibility of all 995 A. baumannii clinical isolates in accordance with the instructions of the Clinical and Laboratory Standards Institute (CLSI) 2020 [60]. The following antibiotics were tested: ceftazidime (CAZ, 10 μg), imipenem (IMP, 10 μg), meropenem (MEM, 10 μg), amikacin (AK, 30 μg), ciprofloxacin (CIP, 5 μg), doripenem (DOR, 10 μg) and gentamicin (CN, 10 μg). MDR was defined as acquired resistance to more than three classes of antibiotics [61]. ## 4.4. Hemolysis Assay and Protease Activity The phenotypic hemolysin activity was determined using the streaking and spot methods on a blood agar plate assay, as described previously [62]. All plates were incubated for 24 h at 37 °C. On blood agar plates, the hemolysis was visualised. The presence of a zone that is lightly cleared in the media and around bacterial colonies when using the streaking method indicated that CRAB could haemolyse red blood cells. Based on the appearance of a greyish-greenish colony encircled by a clear zone, beta-hemolysis was detectable using the spot method. Additionally, the skim milk plate method tested all isolates for proteolytic activity. The inoculated plates were incubated for 4 h at 37 °C. A clear zone surrounding a bacterial colony indicated a positive outcome. The experiment was carried out in triplicate. ## 4.5. Biofilm Formation Using Microtiter Plate Assay The microtiter plate technique assessed the biofilm-forming capacity of CRAB clinical isolates. The isolates were cultivated overnight at 37 °C in tryptic soy broth (TSB) (Oxoid, Basingstoke, UK) and adjusted to No. 0.5 McFarland standards. A 96-well flat-base plate was inoculated with 20 microliters of fresh bacterial culture and incubated for 24 h at 37 °C in 180 microliters of TSB supplemented with $0.25\%$ (w/v) glucose. The plates were rinsed thrice with phosphate-buffered saline after incubation. Crystal violet ($1\%$; v/v) was used to stain the attached cells for 20 min. The stained dye of the adhering cells was dissolved with absolute ethanol. The solution’s optical density was measured at 580 nm [63]. This absorbance value of the solution indicated the biofilm-forming capacity of the isolate. As a negative control, sterile TSB supplemented with glucose was used. The average reading was achieved with three replicated experiments. The term ‘ODc’ was defined as the mean optical density (OD) of the negative control plus three standard deviations (cut-off OD). The respective biofilm formation degrees of the CRAB isolates were reported as follows: strong biofilm formation (4 × ODc < OD), moderate biofilm formation (2 × < ODc < OD 4 × ODc), weak biofilm formation (ODc < OD < 2 × ODc) and non-biofilm formation (OD < ODc) [16]. ## 4.6. Genotypic Characterisation of Antimicrobial Resistance and Biofilm-Associated Genes by Polymerase Chain Reaction (PCR) The CRAB isolates were cultivated in Luria-Bertani broth at 37 °C. The DNA template for the PCR experiment was prepared using a genomic DNA extraction kit (Geneaid, Taiwan). Briefly, the bacterial pellet was collected from 1 mL of an overnight bacterial culture by centrifuging for 1 min at 14,000× g. Then, the pellet was resuspended in 180 μL of GT buffer and mixed by vortex. The suspension was added to 20 μL of Proteinase K and incubated at 60 °C for 10 min. The sample was added with 200 μL of GB Buffer, mixed and incubated at 70 °C for 10 min. Two-hundred microliters of absolute ethanol were added into the sample and mixed well. The sample solution was transferred into a GD Column, then centrifuged at 14,000× g for 2 min. The sample was washed with washing buffers. The extracted DNA was eluted from the column by elution buffer and determined the DNA quantity using a NanoDrop-1000 spectrophotometer (Thermo Fisher Scientific, Wilmington, NC, USA). PCR was used to detect the presence of drug resistance-associated genes, including blaVIM, blaIMP, blaNDM, blaOXA-23-like, blaOXA-24-like and blaOXA-51-like genes and biofilm-related genes, such as csuE and pgaB genes. The primer sets, annealing temperature and sizes of the expected amplicons are demonstrated in Table S1. The amplification reaction (25 μL) contains Taq DNA polymerase, 10× Taq buffer, 25 mM MgCl2, 10 mM dNTPs, 10 mM of each forward and reverse primer solution, 1.25 units/L (Thermo Scientific, Wilmington, NC, USA) and 100 ng/L of DNA template. The thermal cycles were carried out with the initial denaturation at 95 °C for 5 min, followed by 35 cycles of denaturing at 95 °C for 30 s, annealing at a temperature specific for each primer pair for 30 s and extension at 72 °C for 30 s, with a final extension at 72 °C for 7 min. The PCR amplicon was separated on a $1\%$ (w/v) agarose gel, stained with the nucleic acid staining dye, SafeViewTM FireRed (ABM Good, Richmond, BC, Canada), and examined under UV transillumination using the ChemiDoc MP imaging system (Bio-Rad, Hercules, CA, USA). Subsequently, the purified PCR amplicons were sequenced (Macrogen, Seoul, Republic of Korea). The NCBI web-based genome analysis, including the DDBJ/EMBL/GenBank databases, was used for sequence analysis to confirm the target amplicons. ## 4.7. Molecular Typing and Clonal Relationship between CRAB Strains by Repetitive Element Sequence-Based PCR (REP-PCR) To distinguish between bacterial strains, DNA fingerprinting was investigated by using REP-PCR, a molecular typing method. The REP-like elements in the genomic DNA isolated from CRAB isolates were amplified using the primer pairs REP1 (5′-IIIGCGCCGICATCAGGC-3′) and REP2 (5′-ACGTCTTATCAGGCCTAC-3′), as previously described [6]. Briefly, a 25 µL reaction mixture comprising 100 ng of chromosomal DNA, 2.5 µL of 10× Taq buffer, 0.5 µL of 10 mM dNTP mix, 1.5 U of Taq DNA polymerase (Thermo Fisher Scientific, Wilmington, NC, USA), 50 pmoL of each primer and 1.25 µL of dimethyl sulfoxide was added. The amplification procedure included initial denaturation at 94 °C for 10 min, followed by 30 cycles of denaturation (94 °C, 1 min), annealing (40 °C, 1 min), extension (72 °C, 2 min) and a single final extension (72 °C, 16 min). The PCR products were electrophoresed in a $1.2\%$ (w/v) agarose gel. The lanes and bands were determined against a 100 bp plus ladder lane (Thermo Fisher Scientific, Wilmington, NC, USA). For determining genotyping and clonal relationships between CRAB strains, the gels were stained and the DNA bands were photographed and visualised using a UV transilluminator. The images of DNA banding patterns were analysed with GelJ software version 3.0 (San Diego, CA, USA) and the dendrogram was created using the unweighted pair group method with arithmetic averages and Dice’s similarity coefficient with a tolerance of $1.0\%$ [64,65]. A similarity of $70\%$ or greater indicated the same REP-PCR genotype, whereas a similarity of less than $70\%$ indicated different REP-PCR genotypes. ## 4.8. Statistical Data Analysis The data were analysed using GraphPad Prism 9. All analyses were performed using three separate experiments. The prevalence of CRABs, genotypic and phenotypic biofilm formation and drug resistance were demonstrated as percentages. The relationship between biofilm formation and antibiotic susceptibility was demonstrated by heatmap analysis and the chi-squared test where a p-value of <0.05 was considered to be statistically significant. ## 5. Conclusions The high occurrence of biofilm-producing MDR clinical isolates found in this study is concerning. The rising prevalence of carbapenem resistance is alarming. Our findings highlighted the significance of csuE and/or pgaB genes, carbapenemase-encoding genes and the emergence of drug resistant strains as potential risk factors linked with biofilm-producing A. baumannii clinical isolates. 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--- title: 'Clinical Resistant Strains of Enterococci and Their Correlation to Reduced Susceptibility to Biocides: Phenotypic and Genotypic Analysis of Macrolides, Lincosamides, and Streptogramins' authors: - Amr Selim Abu Lila - Tareq Nafea Alharby - Jowaher Alanazi - Muteb Alanazi - Marwa H. Abdallah - Syed Mohd Danish Rizvi - Afrasim Moin - El-Sayed Khafagy - Shams Tabrez - Abdullah Ali Al Balushi - Wael A. H. Hegazy journal: Antibiotics year: 2023 pmcid: PMC10044631 doi: 10.3390/antibiotics12030461 license: CC BY 4.0 --- # Clinical Resistant Strains of Enterococci and Their Correlation to Reduced Susceptibility to Biocides: Phenotypic and Genotypic Analysis of Macrolides, Lincosamides, and Streptogramins ## Abstract Enterococci are troublesome nosocomial, opportunistic Gram-positive cocci bacteria showing enhanced resistance to many commonly used antibiotics. This study aims to investigate the prevalence and genetic basis of antibiotic resistance to macrolides, lincosamides, and streptogramins (MLS) in Enterococci, as well as the correlation between MLS resistance and biocide resistance. From 913 clinical isolates collected from King Khalid Hospital, Hail, Saudi Arabia, 131 isolates were identified as Enterococci spp. The susceptibility of the clinical enterococcal isolates to several MLS antibiotics was determined, and the resistance phenotype was detected by the triple disk method. The MLS-involved resistance genes were screened in the resistant isolates. The current results showed high resistance rates to MLS antibiotics, and the constitutive resistance to all MLS (cMLS) was the most prevalent phenotype, observed in $76.8\%$ of resistant isolates. By screening the MLS resistance-encoding genes in the resistant isolates, the erythromycin ribosome methylase (erm) genes that are responsible for methylation of bacterial 23S rRNA were the most detected genes, in particular, ermB. The ereA esterase-encoding gene was the most detected MLS modifying-encoding genes, more than lnuA (adenylation) and mphC (phosphorylation). The minimum inhibitory concentrations (MICs) of commonly used biocides were detected in resistant isolates and correlated with the MICs of MLS antibiotics. The present findings showed a significant correlation between MLS resistance and reduced susceptibility to biocides. In compliance with the high incidence of the efflux-encoding genes, especially mefA and mefE genes in the tolerant isolates with higher MICs to both MLS antibiotics and biocides, the efflux of resistant isolates was quantified, and there was a significant increase in the efflux of resistant isolates with higher MICs as compared to those with lower MICs. This could explain the crucial role of efflux in developing cross-resistance to both MLS antibiotics and biocides. ## 1. Introduction Enterococci are facultatively anaerobic Gram-positive opportunistic bacteria that are normally found in the human gastrointestinal tract and the female genital tract and abundant in the environment, such as in soil and water [1]. According to Lancefield classification, Enterococci were classified as group D Streptococci based on the carbohydrate substances in their cell walls [2]. E. faecalis and E. faecium are the most important Enterococcal species and are among the foremost causes of nosocomial infections, causing severe infections such as septicemia and endocarditis [3,4]. The unusual adaptation conferred the survival and persistence of Enterococci in adverse environments as inanimate surfaces in hospitals and at sites of infections [3,5,6]. This survival ability allows Enterococci to interact with other overtly resistant bacteria acquiring additional resistances on mobile elements. Noticeably, a quarter of a genome of additional DNA obtained by mobile elements certainly allows Enterococci to persist and spread in the hospital setting and resist antimicrobials causing hostile infections [5,7,8,9,10]. The swift increase in the resistance among hospital-adapted enterococci to a wide diversity of antimicrobials has rendered nosocomial infections a leading therapeutic challenge [1,11,12,13]. Macrolide and lincosamide antibiotics are chemically distinct antibiotic groups but have similar modes of action. For years, these antibiotics represented an alternative to penicillin and cephalosporins; however, the development of macrolide resistance limited the use of these antibiotics to certain indications [14,15,16,17]. Naturally occurring macrolides comprise two amino or neutral sugars attached to a 14–16 membered lactone ring. Newer semisynthetic macrolides had substitutions on the lactone ring that improved acid stability and antimicrobial activity [18]. Lincosamides include the naturally occurring lincomycin and its semi-synthetic derivative, clindamycin. Although lincosamides lack the lactone ring of macrolides, lincosamides share the same mechanism of action as macrolides in targeting 50S bacterial sub-ribosomal unit [14]. Macrolides and lincosamides inhibit bacterial protein synthesis by reversibly binding to the 50S subunit of the bacterial ribosome, [14] stimulating the dissociation of the peptidyl-tRNA from the ribosomes during elongation, causing chain termination [18]. Another antibiotics class that reversibly binds to the 50S bacterial ribosomal subunit is streptogramins [19]. Streptogramin antibiotics act by inhibiting bacterial protein synthesis and are divided into two groups, streptogramin A and streptogramin B, which work together synergistically to produce a bactericidal effect [19,20]. Streptogramins are synthesized by different Streptomyces spp., where group A streptogramins contain 23-membered unsaturated rings with lactone and peptide bonds, and group B streptogramins are cyclic hexa- or hepta-depsipeptides produced [20]. Macrolide/lincosamide/streptogramin (MLS) resistance is increasing among the clinical isolates of Gram-positive bacteria, and the multiplicity of resistance mechanisms of these drugs results in a variety of resistance phenotypes [14]. Three different mechanisms of the acquired MLS resistance have been found in Gram-positive bacteria: [1] target-site modification by methylation or mutation of 23S rRNA, [2] efflux of the antibiotic, and [3] drug inactivation. The most clinically important and widespread resistance mechanisms are the methylation of the 23S rRNA ribosomal subunit and the drug efflux [14,18,21]. While modifications confer broad-spectrum resistance to macrolides and lincosamides, enzymatic modification affects only structurally related antibiotics [14,21]. The improper use, either suboptimal or misuse, of antibiotics in human and veterinary medicine is considered the major cause of antibiotic resistance [22,23]. Recently, the use of biocides in many products as household products, plastics, cosmetics, etc., has been reported as a risk factor contributing to antimicrobial resistance development in humans and the environment [24]. Biocidal agents used for disinfection are usually not assumed to enhance the cross-resistance to antibiotics, although resistant or more tolerant bacteria were isolated from in-vitro cultures after exposure to suboptimal or sublethal levels of biocides [25]. The present study aimed to determine the most prevalent resistance patterns, phenotypes, and the most predominant resistance genes to MLS antibiotics among the collected clinical Enterococci isolates. Moreover, it is aimed to recognize the correlation between the resistance to MLS and the susceptibility to frequently used biocides. ## 2.1. Isolation and Identification of Enterococci spp. Three hundred and twenty-five ($35.6\%$) Gram-positive cocci isolates were recovered from 913 clinical samples. One hundred and thirty-one from isolated Gram-positive cocci ($40.3\%$) showed darkening of the medium around the bacterial colonies, indicating Enterococcus spp., and further biochemical identifications were conducted [26,27]. The Enterococcal spp. isolates that did not ferment arabinose and showed growth in $0.04\%$ tellurite were considered E. faecalis. In contrast, the isolates that did not grow in $0.04\%$ tellurite and ferment arabinose were considered E. faecium. The Enterococcal spp. isolates that showed darkening on bile esculin agar and showed variable results for other tests listed in Table 1 were considered other Enterococci spp. Among 131 Enterococci isolates, 67 ($51.1\%$), 52 ($39.7\%$), and 12 ($9.2\%$) were presumptively identified as E. faecalis, E. faecium, or other Enterococci species, respectively (Figure 1). ## 2.2. Susceptibility to MLS The Enterococcal isolates were tested for their susceptibility to erythromycin, azithromycin, clarithromycin, spiramycin, lincomycin, clindamycin, and quinupristin/dalfopristin by disk diffusion method. Chi-square (χ212 = 6.42, $$p \leq 0.89$$) is not statistically significant, indicating no significant difference in the resistance of different Enterococci spp. to tested antibiotics (Figure 2). The higher resistance values were observed for erythromycin and lincomycin (about $76\%$). Furthermore, E. faecalis and E. faecium were more resistant than other Enterococci spp. The detailed patterns of resistance to the MLS antibiotics are provided in Table S1 and shown in Figure 3. Importantly, the resistance to all tested MLS antibiotics was observed in 43 ($32.8\%$) isolates, while 22 ($16.8\%$) isolates were sensitive to all antibiotics. The higher resistance rates were observed in all the tested macrolides; it was observed in 66 ($50.4\%$) isolates. The resistance rates to streptogramins and lincosamides were $48\%$ and $43.5\%$, respectively. ## 2.3. MLS Resistance Phenotypes One hundred and eight Enterococci isolates that showed resistance to macrolides, lincosamides, and/or streptogramins were selected for further investigation of the resistance phenotypes and genotypes. These isolates comprised 55 E. faecalis, 43 E. faecium, and 10 other Enterococci spp. The inhibition zones between erythromycin, clindamycin, and lincomycin disks were measured in mm, and the triple disk diffusion method was employed to determine the resistance phenotype of the resistant isolates. The ingrowth within zones up to the edges of each erythromycin, clindamycin, and lincomycin disk was considered constitutive macrolide/lincosamide/streptogramin resistance (cMLS) phenotype. Flattening or blunting of the shape of the clindamycin zone indicates inducible macrolide/lincosamide/streptogramin resistance (iMLS) phenotype. Isolates resistant to erythromycin only but sensitive to clindamycin and lincomycin were considered to belong to M phenotypes. Resistance to lincomycin with sensitivity to clindamycin and erythromycin was considered an L phenotype (Figure 4). Out of the 108 selected isolates, 83 ($76.8\%$), 19 ($17.6\%$), 4 ($3.7\%$), and 2 ($1.9\%$) isolates showed cMLS, M, iMLS, and L resistance phenotypes, respectively, as shown in Table 2. ## 2.4. MLS Resistance Genotypes Resistant bacteria employ several mechanisms to resist MLS antibiotics, including (i) changing the antibiotic’s bacterial target by methylation of 23S rRNA, (ii) efflux, and (iii) production of antibiotic’s modifying enzymes as esterase, adenylating, and phosphorylating enzymes [14,28]. In this context, the PCR was used to detect the erm genes (ermA, ermB, and ermC), which are responsible for methylating the 23S rRNA protecting bacteria from MLS antibiotics. The genes msrA, mefA, and mefE are efflux-encoding genes and are responsible for pumping out MLS antibiotics. Furthermore, the genes encode the enzymes that hydrolyze (ereA), adenylate (lnuA), or phosphorylate (mphC) MLS were detected. The current finding revealed the detection of all the tested genes in the resistant isolates, as shown in Figure 5A and detailed in Table S2. The most detected genetic base of resistance was the methylation of 23S rRNA, as the erm genes were the most abundant detected genes in $97.2\%$ resistant isolates. The most detected erm gene is ermB which was detected in $97.2\%$ of resistant isolates, followed by ermA and ermC, which were found in $44.5\%$ and $7.5\%$ of resistant isolates. Interestingly, the coexistence of ermA, ermB, and ermC genes was observed only in $5.6\%$, which all showed cMLS phenotype, while the coexistence of ermA and ermB was observed in $44.5\%$ ($43.5\%$ cMLS- and $1\%$ iMLS-phenotypes) of resistant isolates. On the other hand, the coexistence of ermB and ermC was observed in $7.5\%$ ($5.6\%$ cMLS- and $1.9\%$ iMLS-phenotypes). It is worth mentioning that the only erm gene detected in M-phenotype isolates was the ermB gene, while no erm genes were detected in L-phenotype isolates. Meanwhile, the genes responsible for the breakdown or efflux of MLS were detected at $67.6\%$ or $66.7\%$, respectively. The esterase, adenylation, and phosphorylation encoding genes ereA, lnuA, or mphC were found in $59.3\%$, $1.9\%$, or $13.9\%$ of the resistant isolates, respectively. The efflux encoding genes msrA, mefA, or mefE were detected in $8.4\%$, $60.2\%$, or $61.1\%$ of resistant isolates, respectively. Furthermore, the prevalence of the resistant genes in different resistant phenotypes was screened (Figure 5B). The ermB was observed in $100\%$ of cMLS-, iMLS-, and M-phenotypes and was absent in L-phenotype isolates. On the other hand, lunA and ereA genes were only observed in L-phenotype isolates. While the erm genes and ereA gene were the most detected in cMLS- and iMLS-phenotypes, the efflux genes and only the ermB gene were predominant in M-phenotypes. In L-phenotypes, ereA and lnuA were the only detected genes, $100\%$ and $50\%$, respectively. Additionally, the resistance-encoding genes were screened in the resistant strains of each MLS antibiotic (Figure 5C). The ermB was the highest detected gene in the resistant strains to tested macrolides, lincomycin, and streptogramin. The lnuA gene was detected in the two strains resistant to lincomycin; one was cMLS-phenotype, and the other was L-phenotype. In the clindamycin-resistant strains, the only detected genes were erm genes and ereA genes. The efflux genes were observed mainly in the macrolide- and lincomycin-resistant strains. The phosphorylation (mphC) was less detected in contrast to the hydrolysis of lactone ring (ereA) as a mechanism to break down the MLS antibiotics. ## 2.5. The Minimum Inhibitory Concentrations (MICs) of MLS and Biocides The MICs (µg/mL) of the tested antibiotics were determined by the agar dilution method. The MIC ranges, MIC50 and MIC90, are presented in Table S3. It is observed that the lowest MIC that is required, $50\%$ or $90\%$, inhibits bacterial growth observed with clarithromycin, spiramycin, and quinupristin/dalfopristin. The MICs ranges were 0.125–1024 µg/mL for all the tested MLS antibiotics. Furthermore, the MICs of the resistant isolates were detected against triclosan, cetrimide, glutaraldehyde, thiomersal, chlorocresol, and povidone-iodine, which represent different biocides (Table S4). The MICs ranges of triclosan, cetrimide, glutaraldehyde, thiomersal, chlorocresol, and povidone-iodine to resistant isolates were 0.1–0.7 µg/mL, 0.5–10 µg/mL, 0.1–1.8 µg/mL, 0.2–7 µg/mL, 150–600 µg/mL, and 900–5600 µg/mL, respectively. ## The Correlation between MLS Resistance and Reduced Susceptibility to Biocides Enterococcal isolates were categorized as reduced susceptible or susceptible to the MLS antibiotics or biocides relative to the biocides MIC50 [29]. The reduced susceptibility was considered for isolates that were inhibited by antibiotics or biocides at concentrations above MIC50. There were 38 isolates that showed higher MIC ≥ MIC50 to all the tested antibiotics and also to all biocides. To correlate between the resistance to antibiotics and the reduced susceptibility to biocides for the isolates, the percentage of antibiotic-resistant isolates among biocides susceptible (with MIC below MIC50) and biocides tolerant (with MIC above MIC50) isolates were compared in the isolates that showed MIC ≥ MIC50 of antibiotics. The chi-square test was used to compare the difference in the percentage of antibiotic-resistant isolates with MIC above or below the MIC50 of tested biocides. Chi-square values were statistically significant in most antibiotic-resistant isolates, indicating a significant difference between biocide tolerant (MIC above MIC50) and susceptible (MIC below MIC50) isolates. In other words, isolates that were inhibited by antibiotics at higher MIC ≥ MIC50 were significantly inhibited by higher concentrations of biocides MIC ≥ MIC50 (Figure 6). It is worth mentioning that there was no significant correlation between the reduced susceptibility to thiomersal and the resistance development to all tested MLS antibiotics, as there was no significant difference between the numbers of isolates that showed MIC to thiomersal <MIC50 and >MIC50 in all MLS resistant isolates. In addition, Pearson’s correlation coefficients between MIC values for MLS antibiotics and biocides of individual isolates. There was a stronger correlation between increasing MIC values for antibiotics and biocides ($p \leq 0.05$ was considered significant) (Figure 7). Significantly, there were correlations between reduced susceptibility to cetrimide, glutaraldehyde, chlorocresol, and povidone-iodine and resistance to all tested antibiotics. The reduced susceptibilities to triclosan and cetrimide were significantly correlated to all tested antibiotics except clindamycin, and quinupristin/dalfopristin, respectively. Furthermore, there was no significant correlation between reduced susceptibility to thiomersal and resistance to all tested antibiotics in all resistant isolates. ## 2.6. The Distribution of Resistant MLS Genes in the Resistant Isolates with MIC ≥ MIC50 In order to explore the most involved resistance mechanism in the resistance to both MLS antibiotics and biocides, the distribution of MLS genes was screened in the antibiotic-resistant isolates with MIC ≥ MIC50 and, at the same time, showed reduced susceptibility to biocides with MIC ≥ MIC50. The genes involved in the three resistance mechanisms were found in the highly resistant isolates. However, ermB, mefA, mefE, and ereA genes were the most detected genes. Chi-square test was employed to statistically compare the incidence of resistant genes in the highly resistant isolates (MIC ≥ MIC50) and their incidence in the rest of the resistant isolates. Considering that ermB was the most detected gene in all resistant isolates, no significant difference existed between its incidence in the resistant and highly resistant isolates with MIC ≥ MIC50. Only the efflux encoding genes mefA and mefE were significantly increased in the highly resistant isolates that showed higher MIC ≥ MIC50, which could indicate that the increased resistance is owed mainly to the enhancement of the bacterial efflux to both MLS and biocides (Figure 8). ## 2.7. Efflux Assay in MLS Resistant Isolates One of the mechanisms that confer cross-resistance of bacteria to both MLS antibiotics and biocides is the efflux mechanism. To evaluate the efflux efficiency, a quantitative assay of ethidium bromide (EtBr) efflux was performed for selected 20 high-resistant isolates (MIC > MIC50 for both antibiotics and biocides) against 20 resistant isolates with MIC < MIC50 for both antibiotics and biocides. The minimum concentration of EtBr producing maximum fluorescence ranged from 0.25–2 µg/mL. The quantitative fluorometric efflux assay of EtBr was performed for each isolate three repeats in the absence or presence of glucose and verapamil at concentrations 450–750 µg/mL. The results were expressed as relative fluorescence by comparing the fluorescence observed for the bacteria in the presence or absence of glucose and the control in which the cells are exposed to conditions of minimum efflux in the absence of glucose and the presence of verapamil. Each assay was performed in triplicate, and relative fluorescence data are presented as the means ± standard deviation. The relative fluorescence (RF) values of the isolates with MICs to biocides >MIC50 were significantly increased than the isolates with MICs < MIC50 ($p \leq 0.0001$), indicating the high efflux activities in the isolates which were highly resistant to both antibiotics and biocides (Figure 9). ## 3. Discussion The current study aimed to determine the susceptibility of the local Enterococci clinical isolates to MLS antibiotics to determine the most prevalent resistance phenotypes and the most common genetic determinant of the resistance. About $40\%$ of Gram-positive isolates were identified as Enterococci spp.; the majority were E. faecalis and E. faecium ($51\%$ and $40\%$, respectively). The antibiotic susceptibility testing revealed an increment of the resistance rates of the tested MLS, particularly macrolides, specifically erythromycin. Clindamycin is a chlorinated derivative of lincomycin, and it is one of the 20 most important antibiotics, which is abundantly prescribed for prophylaxis and treatment of anaerobic infections that could explain the development of resistance to it [30]. Generally, Gram-positive cocci, except Enterococci, are sensitive to lincomycin and clindamycin; however, increased plasmid-mediated Enterococcal resistance traits could be recognized in clinical isolates [30,31]. That agrees with our findings, which showed high resistance rates to lincomycin and clindamycin (about $78\%$ and $60\%$, respectively). Enterococcal resistance to streptogramins has been observed worldwide [32,33,34,35], which complies with the present findings, which showed about $35\%$ resistance in all tested Enterococci isolates. Although MLS antibiotics are chemically distinct, they are usually considered together because most share overlapping binding sites on the 50S ribosomal subunit inhibiting the translation process. These antibiotics bind within the exit tunnel adjacent to the peptidyl transferase center and inhibit the progression of the nascent chain, making peptidyl-tRNA drop-off [36]. Even though many bacterial species acquire resistance genes that confer resistance to more than one MLS antibiotic [21], different antibiotics interact and bind with different rRNA residues, which may explain why a bacterium may be resistant to one MLS antibiotic but susceptible to another [37]. Three main mechanisms of acquired MLS antibiotics resistance have been described in Gram-positive bacteria. The first mechanism protects the bacterial ribosome from the drug binding by 23S rRNA methylation. It is a cross-resistance to all three structurally different MLS antibiotics owed to erm genes and can be expressed constitutively or inducible [21,38,39,40,41,42,43]. In the inducible resistance phenotype, bacteria produce inactive mRNA that becomes active only in the presence of a macrolide inducer [14,18,21]. The strains harboring an inducible erythromycin ribosome methylase (erm) genes are resistant to the inducers (14- and 15-membered ring macrolides) but remain susceptible to non-inducer macrolides (16-membered ring), lincosamides, and streptogramins B. In constitutive expression, active methylase mRNA is produced in the absence of an inducer, and the strains express cross-resistance to MLS antibiotics [14,18]. Resistance to macrolides and lincosamides can also be due to the mutations affecting 23S rRNA ribosomal proteins L4 and L22 [44]. Clinical isolates that are constitutively resistant to MLS antibiotics are widespread, particularly in methicillin-resistant strains [45]. Several studies monitored that the constitutive phenotype (cMLS) appears to be the most predominant type in Enterococcal-resistant isolates from patients [35,38,39,42,46]. The current findings revealed the prevalence of cMLS resistance phenotype ($76.8\%$), followed by M-, iMLS-, and L-phenotypes ($19.7\%$, $3.7\%$, and $1.9\%$, respectively). Target-site modification takes place through the mutation or methylation of 23S rRN methyl transferase enzyme resulting in cross-resistance to MLS but not to oxazolidinones giving the MLS phenotype [47]. The MLS phenotype is exhibited by 33 different erm genes expressed constitutively or inducibly [18,21]. These genes are mostly borne on plasmids and transposons that are self-transferable. Four major classes of erm genes were detected in pathogenic bacteria: ermA, ermB, ermC, and ermF [18]. In this study, PCR screening for selected erm genes revealed the presence of all tested genes ermB ($97.2\%$), ermA ($44.5\%$), and ermC ($7.5\%$). The ermA gene is commonly spread in methicillin-resistant isolates (MRSA) and is horizontally transferred by transposons [14], which is why its presence was documented in Enterococci [48,49,50,51]. The ermB expression is induced by macrolides, lincosamides, streptogramins [14,52], and even by ketolides [53,54]. This could explain the high frequency of the ermB gene among Enterococci isolates, taking into consideration that the majority of ermB-positive isolates displayed the cMLS phenotype [55,56]. Moreover, it has been demonstrated that ermB expression is induced by a wide range of MLS antibiotics [52], which agrees with the current data. The ermB gene was recognized in all the isolates that showed iMLS-phenotype. Conversely to the ermB gene expressed by a wide range of MLS antibiotics, ermC expression is induced by a few macrolides [57,58]. The ermC gene is mostly responsible for erythromycin resistance and is transferred by plasmids [14], which complies with the present findings, which showed ermC in all erythromycin-resistant isolates are mostly cMLS ($5.6\%$ cMLS- and $1.9\%$ iMLS-phenotypes of all resistant isolates). Gram-positive and -negative bacteria can resist diverse groups of antibiotics by producing drug-inactivating enzymes [59,60,61]. About 19 genes code esterase, lyases, transferases, and phosphorylases enzymes which modify and inactivate MLS antibiotics by hydrolyzing the lactone ring (ere genes), adenylating (lnu genes), acetylating (vat genes), or phosphorylating (mph genes) [21,62]. Unlike target modification, drug inactivation confers resistance to the structurally related antibiotics only [18], but none of the inactivating enzymes are unique to certain bacterial species [63]. Whereas esterase, phosphotransferases, acetyltransferases, hydrolases, and nucleotidyl transferases were identified in strains resistant to MLS antibiotics, these inactivating enzymes confer resistance to erythromycin and other 14- and 15-membered macrolides but not to lincosamides that represented as L phenotype [18]. The ere genes, especially the ereA gene, are the most distributed MLS-inactivating genes in both Gram-positive and -negative bacteria [21]. The current results revealed that the ereA gene had been detected in $59.3\%$ of resistant isolates showing the cMLS-, iMLS-, and L-phenotypes isolates but not detected in M-phenotype isolates. The mphC gene has been detected in $13.9\%$ of the resistant isolates that showed either the cMLS phenotype or M phenotype. Notably, the lnuA gene was only detected in $1.9\%$ of isolates that showed cMLS- or L phenotype that can be possibly explained as the lnu gene involves phosphorylation and nucleotidylation of lincosamides resulting in high resistance to lincosamides but not macrolides [64]. Considering that the resistance mediated by lnuA and/or lnuB genes confer resistance to lincomycin but not clindamycin, it is expressed as L phenotype [14]; the lnuA gene was detected in all lincomycin-resistant isolates but not detected in any clindamycin-resistant isolate. The efflux mechanism in which the bacteria pump out one or more MLS antibiotics is owed to about 17 efflux genes via either ATP-transporters or major facilitator transporters [21]. However, efflux pumps are compartments of the bacterial cell wall, and their responsible genes are located on the chromosomes; transferable elements are more often involved in the enhanced efflux of MLS [50,65,66]. Based on the amino acid sequence and source of energy, the bacterial efflux transporters are classified into five different superfamilies [13]. The active efflux of MLS antibiotics is responsible for partial cross-resistance to 14- and 15-membered macrolides and streptogramin B and is conferred most abundantly msr, vga, mef, isa, and other genes [21,67]. The efflux resistance is inducibly expressed by erythromycin and other 14- and 15-membered macrolides [14,21]. Clindamycin is neither an inducer nor a substrate for the pump; thus, the efflux genes carrying strains are fully susceptible [14]. The mef genes encode for efflux in macrolides and msr genes for efflux of macrolides and streptogramin B; they have been involved in the active efflux of MLS in Gram-positive cocci [65,68,69]. These genes may be located on the chromosomes but are more often associated with transferable elements [50,65,66]. Our results showed that mefA, mefE, and msrA were recognized in $60.2\%$, $61.1\%$, and $8.4\%$, respectively. Interestingly, all isolates showed M phenotype carried msrA or/and mefA and mefE genes. These results are in great accordance with other groups. Iannelli et al. and others showed that efflux pumps encoded by mefA and its allele mefE genes are among the most common mechanisms of resistance to macrolides (M phenotype) [14,28,69]. Furthermore, the msrA gene displays the inducible resistance to erythromycin, while macrolide efflux affected by mef genes was reported in Gram-positive cocci [50]. Efflux pumps responsible for macrolides resistance in Enterococci include mefA and mefE pumps, which are involved in the intrinsic resistance to lincosamides and streptogramins in E. faecalis [28]. In the current study, high resistance rates were not observed in MLS antibiotics but also different biocides. Cross-resistance to antibiotics and biocides can be conferred by induction of common resistance mechanisms [70], e.g., efflux pumps and transfer of resistance genes for antimicrobials and antibiotics on mobile genetic elements [24,71]. In this direction, it is intended to correlate the enhanced resistance to MLS antibiotics and biocides. The correlation between the resistance to antibiotics and the reduced susceptibility to biocides for the isolates was determined by comparing the percentage of antibiotic-resistant isolates among biocides less resistant (with MIC < MIC50) and biocide tolerant (with MIC ≥ MIC50) isolates [29]. Our results revealed a significant difference between biocide tolerant and biocide susceptible isolates in MLS resistant isolates. However, there is a significant statistical correlation between elevation in MICs to MLS and all biocides, except there was no correlation between the increase of MICs to thiomersal and MICs of antibiotics. It can be interpreted that thimerosal is not used frequently; it is used mainly as a preservative in a number of biological products that do not enable Enterococci to develop resistance against MLS antibiotics [72]. By screening the most abundant genes in the resistant isolates that are biocide tolerant, the efflux genes mefA and mefE were significantly increased than those in biocide with lower MICs. That indicates the possible roles of efflux in enhancing the resistance to both biocides and MLS antibiotics. Efflux pumps are major protective components of the bacterial cell wall that has been constitutively or inductively expressed and are responsible for the intrinsic and acquired resistance of many bacterial species to antimicrobials [73]. Bacterial active efflux compromises the effectiveness of antimicrobials and is crucial in cross-resistance to antibiotics and biocides [70,71,73,74]. In this direction, a fluorometric assay of the EtBr efflux has been used to quantify the efflux of selected highly resistant MLS isolates that showed higher MICs ≥ MIC50 or lower MICs < MIC50 to biocides. EtBr efflux has been assayed under limiting energy supply (absence of glucose and low temperature) and in the presence and absence of the approved efflux pump inhibitor verapamil [75]. Significantly, the MLS isolates with higher MICs to biocides >MIC50 extruded EtBr more than those with lower biocide MICs, indicating the essential role of efflux mechanism in cross-resistance to both antibiotics and biocides. It has been approved that there is a direct association between tolerance to biocides and antibiotic resistance since the mechanisms contributing to both are similar to changes in the cell permeability or the synthesis of efflux pumps [71,76]. ## 4.1. Microorganisms Nine hundred and thirteen clinical samples were collected from King Khalid Hospital, Ha’il, Saudi Arabia, from June 2019 to January 2020. Patient consent was obtained according to the hospital administration department’s routine hospital protocols in complete compliance with Helsinki declarations without any risk, burden, or danger to patients. The clinical specimens were collected from microbiological labs without direct patient contact. ## 4.2. Identification of Enterococcus spp. The clinical specimens were cultivated on Bile esculin agar, Mannitol salt Agar, and MacConkey agar (Oxoid, Hampshire, UK) to isolate the Enterococcus spp. Further biochemical tests were performed to confirm the identification and to differentiate between E. faecalis, E. faecium, and other species of Group D Enterococci (Table 1) [26,27]. The biochemical tests were performed according to Elmer et al. [ 77]. ## 4.3. Determination of Antibiotic Susceptibility and MICs of Isolates All Enterococcal isolates were tested for their susceptibility to selected antibiotics using the disk diffusion method according to the Clinical and Laboratory Standards Institute (CLSI, 2015) [78,79]. The MICs of the tested antibiotics or biocides were determined by the agar dilution method according to CLSI, 2015. Furthermore, MIC50 and MIC90, the concentration that inhabited $50\%$ or $90\%$ of isolates, were calculated by determining the median, which corresponds to MIC50, and 90th percental, which corresponds to MIC90 [80]. ## 4.4. Determination of MLS Resistance Phenotypes by Triple Disk Diffusion Test The test was performed according to Novotna et al. [ 2005] [43]. Standardized suspensions of the tested isolates (equivalent to the 0.5 McFarland) were prepared from overnight cultures in tryptone soya broth (TSB) and swabbed over the surface of Müeller-Hinton (MH) agar plates. Erythromycin (15 µg), clindamycin (2 µg), and lincomycin (2 µg) disks were placed in close proximity (20 mm) to each other over the agar surface. The plates were incubated for 16–18 h at 37 °C and then examined for the shape of inhibition zones if any. Significant ingrowth within zones up to the edges of each erythromycin, clindamycin, and lincomycin disk was considered constitutive resistance (cMLS) phenotype. Any flattening or blunting of the shape of the clindamycin zone indicates inducible resistance (iMLS) phenotype. Isolates resistant to erythromycin only but sensitive to clindamycin and lincomycin were considered to belong to M phenotypes. Resistance to lincomycin with sensitivity to clindamycin and erythromycin was considered an L phenotype. ## 4.5. PCR Detection of MLS Resistance Genes PCR detection of MLS resistance encoding genes ermA, ermC, ermB, msrA, mefA, mefE, ereA, lnuA, and mphC genes was performed. The crude DNA was extracted using a Qiagen DNA extraction kit (Düsseldorf, Germany) [81] and stored at −80 °C [81,82]. The used primers are listed in Table 3. ## 4.6. Evaluation of the Efflux in MLS Resistant Isolates with Higher MIC to Biocide In order to evaluate the efflux efficiency, a quantitative assay of ethidium bromide (EtBr) efflux was performed for selected isolates by fluorometric assay, according to Paixao et al. [ 73]. Twenty isolates that showed high MLS MIC > MIC50 to both antibiotics and biocides were selected to be compared with 20 isolates with lower MIC < MIC50 to both antibiotics and biocides. The MICs of selected isolates for EtBr and the efflux pump inhibitor verapamil were determined by the broth microdilution method in 96-well microtiter plates according to the CLSI, 2015. Moreover, the MIC of EtBr in the presence of $\frac{1}{5}$ of the MIC of verapamil was determined. In order to assure that the verapamil did not affect cellular viability, it was used at concentrations that did not exceed $\frac{1}{5}$ of its MIC. The selected isolates were grown in 10 mL of Luria-Broth (LB) broth to absorbance at 600 nm (OD600) of 0.6. The bacteria were then centrifuged at 14,000 rpm for 3 min. The pellet was washed twice with the same volume of PBS, and the OD600 of the cellular suspension was adjusted to 0.3. The accumulation assays were performed in 96-well fluorescence microtiter plates with a final volume of 100 µL. The conditions for the maximum accumulation (presence or absence of $0.4\%$ glucose) of EtBr were first determined. Fifty µL of washed cell suspension was added to 50 µL of varying concentrations of EtBr in the absence or the presence of $0.4\%$ glucose, and fluorescence was measured. ELISA reader 800 TS (BioTek, Winooski, VT, USA) was used to monitor the accumulation and extrusion of EtBr on a real-time basis. All the readings were made at excitation and emission wavelengths for EtBr (530 nm and 585 nm, respectively). All fluorescence data were acquired in cycles of 60 s, during a 1 h time interval, and at 25 °C. Each experiment was conducted in triplicate, and the results obtained were averaged. After determining the optimum conditions for EtBr accumulation, the effect of verapamil on the accumulation of EtBr was determined. A volume of 50 µL of washed cell suspension was added to 50 µL PBS solutions containing EtBr (in sub-MIC) in the absence and the presence of $0.4\%$ glucose and verapamil at concentrations that did not exceed $\frac{1}{4}$ MIC. The fluorescence was measured as mentioned above, and the effect of verapamil on the fluorescence was determined. Each experiment was conducted in triplicate, and the results obtained were averaged. The tested isolates were grown in 5 mL of LB, incubated at 37 °C for 18 h, centrifuged at 14,000 rpm for 5 min, and supernatants were discarded. The bacteria were loaded with EtBr (in sub-MIC) at 25 °C at 200 rpm for 1 h. Then, the pellets were washed with cold PBS and centrifuged at 13,000 rpm for 5 min. Supernatants were discarded, and each pellet was resuspended in 1 mL of cold PBS. A volume of 50 µL of each washed cell suspension was added in the 96-well microtiter plate containing (i) 50 µL of PBS without glucose, (ii) 50 µL PBS with $0.4\%$ glucose, or (iii) 50µL PBS without glucose and with verapamil in concentrations that favor the maximum accumulation of EtBr. Aliquots of 100 µL were assayed at 37 °C with continuous fluorescence measurement as described previously, and each experiment was performed in triplicate. The efflux of EtBr is expressed in terms of relative fluorescence (RF), which is obtained from the comparison between the fluorescence observed for the bacteria in the presence or absence of glucose and the negative efflux control in the absence of glucose and the presence of verapamil following the formula RF=Measured fluorescence in PBS−glucose—Measured fluorescence in PBS+glucoseMeasured fluorescence in PBS −glucose+verapamil ## 5. Conclusions This study aimed to characterize the resistance to MLS antibiotics phenotypically and genotypically. The target-site modification of bacterial 50S ribosomal subunit was the most prevalent mechanism of resistance to MLS antibiotics. The constitutive resistance to MLS was the most predominant phenotype. In consistence with this, rRNA methylase erm genes ermB, A, and C were highly distributed among Enterococci isolates. The MLS-inactivating enzymes encoding genes were detected in the tested isolates, particularly esterase encoded by the ereA gene. On the other hand, the lnuA gene, which is mainly associated with lincomycin resistance, was the least detected. While the least resistance of tested isolates was detected against clindamycin, the higher rates were detected against erythromycin, azithromycin, and clarithromycin, represented as MLS or M phenotypes. There was a significant correlation between the reduced susceptibility of isolates to the commonly used biocides and the resistance to MLS antibiotics. Importantly, the increased efflux was observed phenotypically, and its encoding genes in the resistant MLS isolates showed reduced susceptibility to biocides. That could indicate the increased role of efflux in conferring resistance to both antibiotics and biocides. ## References 1. Semedo-Lemsaddek T., Barreto-Crespo M.T., Tenreiro R.. 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--- title: 'Use of Medical-Grade Honey to Treat Clinically Infected Heel Pressure Ulcers in High-Risk Patients: A Prospective Case Series' authors: - Georgios E. Papanikolaou - Georgios Gousios - Niels A. J. Cremers journal: Antibiotics year: 2023 pmcid: PMC10044646 doi: 10.3390/antibiotics12030605 license: CC BY 4.0 --- # Use of Medical-Grade Honey to Treat Clinically Infected Heel Pressure Ulcers in High-Risk Patients: A Prospective Case Series ## Abstract Management of locally infected heel-pressure ulcers (HPUs) remains challenging, and given the increasing occurrence of infections resistant to antibiotic therapy and patients’ unwillingness to surgery, innovative and effective approaches must be considered. Medical-grade honey (MGH) could be an alternative therapeutic approach due to its broad-spectrum antimicrobial activity and healing properties. This study aimed to present the high effectiveness and safety of MGH for the conservative treatment of clinically infected HPUs. In this case series, we have prospectively studied nine patients with local signs of infected HPUs. In all cases, HPUs persisted for more than 4 weeks, and previous treatments with topical antibiotics or antiseptic products were ineffective. All patients were at high-risk to develop HPU infection due to their advanced age (median age of 86 years), several comorbidities, and permanent immobility. All wounds were treated with MGH products (L-Mesitran), leading to infection resolution within 3–4 weeks and complete wound healing without complication. Considering the failure of previous treatments and the chronic nature of the wounds, MGH was an effective treatment. MGH-based products are clinically and cost-effective for treating hard-to-heal pressure ulcers such as HPUs. Thus, MGH can be recommended as an alternative or complementary therapy in wound healing. ## 1. Introduction Pressure ulcers (PUs) are localized damage to the skin and/or underlying soft tissues caused by pressure or shear, usually over a bony prominence [1,2]. Heel-pressure ulcers (HPUs) are the second most common type of PUs after the sacrum and the site where the most critical and severe PUs tend to develop [3,4,5]. The heel is particularly vulnerable to pressure injury due to its thin skin, and lack of fat tissue and muscle for protection and cushioning. Moreover, the limited weight-bearing area of the posterior part of the heel must sustain high-pressure forces that are exerted directly over the calcaneus. Currently, the median incidence rate of HPUs in hospitals is estimated as $17.4\%$ and the median prevalence rate as $11.7\%$ [6]. The vast majority of HPUs remain superficial, involving only the skin (stage I and II) or the underlying subcutaneous tissue (stage III); and about $11\%$ to $18\%$ of all HPUs involve deeper tissues, such as muscle, tendon, or bone (stage IV) [6,7,8]. Particularly burdensome for the public health systems as well as for patient’s quality of life is the management of hard-to-heal HPUs, defined as an injury of the skin that persists for at least 4–6 weeks, which shows no tendency to heal despite the use of different treatment protocols [9,10]. Additional aggravating factors can be the presence of different comorbidities, especially in the elderly population, such as diabetes and peripheral arterial disease, previous surgical procedures, impaired nutritional status, and mobility problems [11,12,13]. Therefore, these patients are at a high risk to develop pressure ulcers complicated by local infection and consequently, a delay in the wound-healing process. Clinical assessment of the wound characteristics is an important step in the selection of the appropriate treatment. Chronic ulcers can be complicated with inflammation and, therefore, microbial colonization; in addition, the early recognition of local signs and symptoms of infection is mandatory for a successful healing trajectory. A superficial increased bacterial burden is mainly characterized by wound-healing delay, moderate exudate levels, presence of debris (yellow or black necrotic tissue), and unpleasant odor from the wound [14]. A deep infection is usually presented with large ulcer dimensions, locally increased temperature, pain, edema, malodor, high exudate levels, and often bone exposure [14]. Considering the increased occurrence of infections resistant to antibiotics, new and more efficient therapies are required to effectively treat locally infected HPUs. Honey has been used for wound healing and local infections since ancient times [15]. Medical-grade honey (MGH) is carefully selected, clean of pollutants, follows specific physicochemical characteristics, and is gamma-sterilized to guarantee its safe use for medical applications [16]. MGH has broad-spectrum antimicrobial properties principally due to its high sugar content, which creates an osmotic gradient leading to microbial dehydration and growth inhibition [17,18,19,20]. Other antimicrobial mechanisms of MGH are the acid pH, the production of low levels of hydrogen peroxide, and the release of components, such as flavonoids, methylglyoxal, and bee defensin-1, which are factors that are toxic to almost all microorganisms, but not to the healthy surrounding tissue [17,18,19,20]. Another important property of the MGH is its wound-healing activity. MGH allows for effective autolytic wound debridement, leading to the removal of necrotic tissue [21,22,23]. In addition, MGH has anti-inflammatory and antioxidative activity, creates a moist environment, and enhances the regenerative process in the wound by stimulating the formation of healthy granulation tissue and neo-epithelization [24,25,26]. MGH speeds up healing in different types of acute and chronic wounds, including pressure ulcers [27,28]. In this prospective case series, we present our experience in the treatment of clinically infected HPUs with MGH (L-Mesitran, Triticum Exploitatie BV, the Netherlands). The aim of this study is to demonstrate the effective and safe use of MGH in high-risk elderly patients with multiple comorbidities. ## 2.1. Case 1 An 85-year-old female patient presented with a stage III HPU at her right foot due to prolonged immobility after hip arthroplasty (Figure 1a). Medical comorbidities included dementia, hyperlipidemia, hypertensive heart disease, and deficiency of vitamin B12 and calcium. The wound had been present for >2 months and unsuccessfully treated with neomycin sulfate topical spray. On initial evaluation, the ulcer dimensions were 5 cm in length and 5 cm in width. Local clinical signs of infection included hard necrotic eschar, low levels of exudate, pain, and delayed healing. Surgical (scalpel) debridement at the bedside was performed to remove the thick eschar. L-Mesitran® Soft wound gel (MGH) was applied directly to the wound, followed by L-Mesitran® Tulle (MGH) to ensure contact with the wound bed. Then, a secondary foam dressing was applied to absorb the secretions and offload the heel region. Wound-dressing changes were performed by the healthcare professional at the patient’s home at 48 h intervals. After 4 weeks, pain and exudate were considerably reduced, and healthy granulation tissue was evident (Figure 1b). Due to improved wound healing, dressing changes were transitioned to every 4 days. The HPU was completely healed after 17 weeks of MHG treatment without complications (Figure 1c). ## 2.2. Case 2 An 88-year-old female patient presented with a stage III HPU at her left foot due to permanent immobility (Figure 2a). Relevant comorbidities included dementia, cerebrovascular disease, arterial hypertension, anemia, iron deficiency, and nephritis. The patient’s familiar ambient was non-compliant and severe malnutrition was noticed. Concomitant pressure ulcers were presented at the sacral–coccyx area and the tibial area bilaterally. The HPU was previously treated for 4 weeks with a povidone–iodine solution and a mupirocin-based topical cream, without clinical improvement. Upon presentation, the wound dimensions were 5 cm in length and 4 cm in width. Clinical signs of infections were the presence of a moderate amount of exudate, local hyperthermy, malodor, slough, and pain. Moreover, the wound edges were macerated and indented with a significant delay in the healing process. Local treatment was initiated with L-Mesitran® Soft wound gel (MGH), followed by L-Mesitran® Tulle (MGH). Then, a secondary foam dressing was applied to absorb the secretions and offload the heel region. Wound-dressing changes occurred at the patient’s home every 48 h by the healthcare professional. After 3 weeks of MGH treatment, the wound size reduced, granulation tissue was visible, the wound edges showed a normal re-epithelialization process, and clinical signs of infection disappeared (Figure 2b). Consequently, ulcer changes were extended to every 4 days. The HPU was completely healed after 12 weeks of MGH therapy (Figure 2c). ## 2.3. Case 3 A 72-year-old female patient presented with a stage III HPU at her left foot due to permanent immobility associated with several comorbidities, including dementia, cerebrovascular disease, atrial fibrillation, myocardial infarction, arterial hypertension, and osteoporosis (Figure 3a). Previously, the wound was ineffectively cleansed for one month with soap and normal saline. On the initial presentation, her wound measured 5 cm in length and 5 cm in width. Local clinical signs of infection included erythema, low amount of exudate, debris, and delayed healing. Local treatment was initiated with L-Mesitran® Soft wound gel (MGH), followed by L-Mesitran® Tulle (MGH). Then, a secondary foam dressing was applied to absorb the secretions and offload the heel region. Wound-dressing changes were performed by the patient at home every 48 h intervals. At her 3-week follow-up, necrotic tissue was eliminated due to the osmotic property of the MGH products, the wound defect reduced considerably in size, and erythema disappeared (Figure 3b). Treatment was continued as per above, and complete healing was uneventfully achieved after 8 weeks (Figure 3c). ## 2.4. Case 4 An 86-year-old female patient presented with a stage III HPU at her left foot due to prolonged immobility after being operated on for a hip fracture (Figure 4a). Medical comorbidities included arterial hypertension, cerebrovascular disease, epilepsy, anxiety, and deficiency of vitamin D. During her hospitalization, the HPU was treated for 10 days with different wound care products, such as silver and foam dressings, all without success. Three weeks after her dismission from the hospital, the wound dimensions were 6 cm in length and 5 cm in width. Local signs of infection included the presence of a thick and large necrotic eschar, a low amount of exudate, an unpleasant odor, pain, and delayed healing. Partial surgical (scalpel) debridement was performed at the bedside and was limited secondary to pain and anticoagulation therapy. Local treatment with L-Mesitran® Soft wound gel (MGH), followed by L-Mesitran® Tulle (MGH), and a foam dressing was initiated to promote autolytic debridement and eliminate the remaining necrotic tissue and resolve the underlying infection. Initially, dressing changes were performed by the healthcare professional at the patient’s home daily, due to the high amount of exudate that was secreted, and to control any potential hemorrhagic diathesis. Within 1 week after MGH therapy was started, the malodor disappeared, while the autolytic debridement process was evident. Given the prosperous wound-healing progress, dressing changes interval were extended to every 4 days. After 4 weeks, the wound bed was completely clean, the defect started to be covered with new granulation and epithelial tissue, and the infection resolved (Figure 4b). The patient’s HPU was completely healed after 24 weeks of MGH treatment (Figure 4c). ## 2.5. Case 5 A 92-year-old female patient presented with a stage III HPU at her left foot due to permanent immobility (Figure 5a). Medical comorbidities included cerebrovascular disease, rheumatic polymyalgia, and glaucoma. The wound was previously treated with povidone–iodine for one month, without any improvement. On initial observation, the HPU sized 6 cm in length and 4 cm in width. Local signs of infection included a central necrotic area with peripheric erythema, pain, and delayed healing. Treatment with L-Mesitran® Soft wound gel (MGH), followed by L-Mesitran® Tulle (MGH), and a foam dressing was commenced; and dressing changes were performed at the patient’s home by the health-care professional every 48 h. During the next 2 weeks, local signs of infection gradually disappeared and the wound area was reduced, and replaced by granulation and epithelial tissue (Figure 5b). Due to the positive therapeutic response, dressing changes were prolonged to every 4 days. Consequently, the patient was temporarily lost from the follow-up probably because she was non-compliant with the proposed MGH therapy. After 3 months, she showed up and the HPU was stable, but not yet healed. MGH therapy restarted as per above, and the HPU was completely healed after 31 weeks of MGH treatment without complications (Figure 5c). ## 2.6. Case 6 A 94-year-old male patient presented with a bilateral stage III HPU (Figure 6a). HPUs were caused by permanent immobility and aggravated by a prolonged hospitalization (about one month) related to uncontrolled diabetes. The patient had several comorbidities, including diabetes, cerebrovascular disease, depression, prostatic hypertrophy, anemia, and deficiency of vitamin B12. Previous treatments were not reported by his relatives. An initial examination was held 10 days after his dismission from the hospital, where the right HPU measured 6 cm in length and 5 cm in width; and his left HPU measured 7 cm in length and 7 cm in width. Local infection was evident by the presence of large heels defect, extended necrosis, heavy exudate, malodor, erythema, pain, and delayed healing. The initial therapeutic protocol included serial surgical (scalpel) debridement at the bedside with a 2-week interval in between, followed by the application of L-Mesitran® Soft wound gel (MGH), L-Mesitran® Tulle (MGH), and a foam dressing. MGH dressing changes were performed at the patient’s home daily by his relatives to permit effective drainage of the exudate and achieve an osmotic cleansing of the wound bed from the extended necrotic tissue. After 8 weeks of combined treatment, clinical signs of infection were resolved, the wound bed was noticeably cleansed, and new granulation tissue started to fill the heel defect (Figure 6b). Since the healing process progressed successfully, the dressing was changed every 3 days. Finally, complete HPU healing was achieved after 22 weeks of MGH treatment without complications (Figure 6c). ## 2.7. Case 7 A 59-year-old female patient presented with a stage III bilateral HPU (Figure 7a). She was hosted in a boarding house for chronically mentally ill patients. She suffered from severe psychotic disorders, which combined with a lack of compliance and prolonged immobility led to the development of the HPUs. Previously, the wounds were ineffectively treated for one month with a povidone-iodine solution. On initial examination, the right HPU measured 6 cm in length and 5 cm in width; and his left HPU measured 5 cm in length and 4 cm in width. Local signs of infection included a high amount of exudate, malodor, necrosis, slough, and delayed healing. The level of pain was impossible to be assessed due to her psychiatric conditions. Surgical (scalpel) debridement was impossible due to the lack of patient compliance, and the commencement of local therapy with L-Mesitran® Soft wound gel (MGH), L-Mesitran® Tulle (MGH), and a foam dressing were decided. Initially, dressing changes were performed by the healthcare professional every 48 h. After 2 weeks, the autolytic effect of MGH was evident, and signs of infection resolved (Figure 7b). Moreover, the wound area was reduced and gradually replaced by healthy granulation and epithelial tissue. Since the wounds became more superficial and the amount of exudate was considerably reduced, the interval of dressing changes was prolonged to every 4 days. The right HPU was completely healed after 108 days of MGH treatment, while the left HPU healed after 17 weeks of MGH treatment (Figure 7c). ## 2.8. Case 8 An 87-year-old female patient presented with a stage III HPU on her right foot due to prolonged immobility after being operated on for a hip fracture (Figure 8a). Medical history included arterial hypertension, organic psychotic disorder, and an already established permanent immobility. The HPU was unsuccessfully treated for 1.5 months with different wound care products, such as silicone foam dressings and neomycin sulfate topical spray. On initial evaluation, the wound dimensions were 4 cm in length and 4 cm in width. Local signs of infection included the presence of a thick necrotic eschar, erythema, pain, malodor, and delayed healing. Bedside conservative debridement was impossible due to the patient’s intolerance associated with her psychotic disease and it was decided to start the therapy with L-Mesitran® Soft wound gel (MGH), L-Mesitran® Tulle (MGH), and a foam dressing. Wound-dressing changes were performed by the healthcare professional at the patient’s home at 48 h intervals. Within 4 weeks, the osmotic property and moist environment provided by the MGH products allowed the softening of the eschar and permitted an easy surgical removal of the necrotic tissue (Figure 8b). Moreover, all signs of local infection disappeared, wound size decreased, and new granulation tissue was evident at the wound bed. During the next 4 weeks, the wound healing further progressed, and dressing changes were transitioned to every 4 days (Figure 8c). The HPU healed after 13 weeks of MGH treatment (Figure 8d). ## 2.9. Case 9 A 78-year-old female patient presented with a stage III HPU at her right foot caused by permanent immobility (Figure 9a). Relevant comorbidities included peripheral arterial disease, arterial hypertension, Parkinson’s disease, and osteoporosis. The HPU was ineffectively treated for 15 days with a povidone–iodine solution. She was found to have a wound sized 6 cm in length and 3 cm in width. Local clinical signs of infection included severe edema, erythema, debris, moderate level of exudate, and delayed healing. Topical treatment was initiated with L-Mesitran® Soft wound gel (MGH), L-Mesitran® Tulle (MGH), and a foam dressing. Dressing changes were performed by the healthcare professional at the patient’s home every 48 h. After 4 weeks, the ulcer improved with the elimination of necrotic tissue, the appearance of healthy tissue, and no evidence of signs of local infection (Figure 9b). Due to the positive healing process, the dressing changes were extended to every four days. The HPU completely healed after 20 weeks of MGH therapy without complications (Figure 9c). ## 3. Discussion Non-healing wounds are often complicated by local contamination or infection caused by various species of microorganisms. The bacteria in the wound can be protected by a barrier of extracellular matrix forming a biofilm, which is particularly resistant to different antibiotics [29]. The increasing occurrence of infections resistant to antibiotic therapy necessitates the development of alternative and improved treatment approaches. In our case series, all HPUs were successfully treated with MGH products. Previous treatments with different antiseptic or antibiotic products were without adequate response. Local clinical signs of infection gradually decreased and completely resolved within a time range from 1–4 weeks, in concordance with other studies [30,31,32]. In this study, wound swabs and microbial culturing were not taken and performed since the diagnosis was made by clinical assessment of different signs of local infection, such as necrosis, pain, malodor, erythema, warmth, edema, exudate, and delayed healing. In addition, swabs can be costly, and not always precise due to the presence of normal cutaneous bacterial flora. Moreover, it can also be considered redundant when infections manifest only locally because MGH exerts broad-spectrum antimicrobial activity irrespective of their antibiotic resistance profile and is even effective against biofilms. However, from a scientific point of view, taking swabs must be considered in future studies, as this can help to demonstrate the clinical broad-spectrum antimicrobial efficacy of MGH. In all presented cases, MGH products were used as monotherapy and none of the patients received systemic antibiotic therapy. Given the increased antibiotic resistance, MGH is a promising therapeutic approach to treat wound infections and enhance the healing process. We did not observe any adverse effects, and with the quick recession of local signs of infection and the positive healing response, the wound-dressing changes were extended, reducing in this way the total cost of the therapeutic protocol. Several studies proved the broad-spectrum antimicrobial activity of MGH against common wound microorganisms, including Staphylococcus aureus (including methicillin-resistant Staphylococcus aureus, MRSA), Pseudomonas aeruginosa, and Escherichia coli, even in cases where antibiotics were ineffective [18,30,31,32,33,34]. Recent reviews provide an extensive list of microorganisms with susceptibility to the antimicrobial activity of MGH [19,35]. Furthermore, the development of bacterial resistance after repeated use of MGH materials is thought to be unlikely, attributed mainly to the natural origin of the honey and its multiple antimicrobial components and mechanisms [17,36]. Our study included patients with a high risk to develop infections at their HPUs. In those patients, it is important to promptly identify and, if possible, correct any systemic or local factors that can lead to non-healing HPU, further complicated with inflammation and infection. Eight of the nine patients were elderly, with an average age of 82 years old. Age >65 years is frequently associated with impaired nutritional status and limited mobility usually in hospitalized and nursing/community care patients, leading to HPUs [11,37]. Moreover, all our patients had several comorbidities, presented mainly with dementia, cerebrovascular ischemic disease, and cardiovascular disease. Mental status emerged as a significant risk factor associated with HPU development [37,38], while cardiac and cerebral vascular disease can be associated with peripheral perfusion issues, which caused delaying wound healing [6,39,40,41]. Six patients presented with permanent immobility due to their advanced age, plegia, and mental disorders, and four of them had concomitant pressure ulcers in other body regions. Immobility is considered a crucial prognostic factor for the development of pressure ulcers, and different interventions must be taken to offload the pressure and prevent any ischemic injury [37,40]. In addition, three patients underwent orthopedic surgery, and they developed HPUs secondary to their prolonged immobility. Surgery is an important independent risk factor for the emergence of HPUs, especially in elderly hospitalized patients [11]. All patients had a stage III HPU with a mean length of 5.54 cm and width of 4.64 cm (length range 4–7 cm, width range 3–7 cm). Six patients presented with necrotic tissue, in four of which the eschar was effectively removed by MGH combined with limited surgical (scalpel) debridement at the bedside, and in the other two cases only MGH products were able to stimulate the autolytic debridement process and efficiently clean the wound bed. Large (>4 cm) and deep (stage III and IV) HPUs are usually prone to superficial bacterial contamination or deep wound infection and, thus, complicated with local inflammation, osteomyelitis, or systemic sepsis, require urgent surgical intervention to save the patient’s life [42]. Operative management of HPUs includes partial or total calcanectomy, revascularization techniques, free flaps, and amputation [43]. MGH can be an alternative therapeutic option to surgery, mainly in patients with age-related comorbidities, locally large and infected HPU, where operative intervention is contraindicated or not desired from the patients [44]. We used MGH materials, which positively affected the wound healing process, initially through autolytic debridement, resolution of clinical infection, and anti-inflammatory medication; and, in a second phase, promoting granulation tissue formation, neo-angiogenesis, and re-epithelialization. The healing time ranged from 2–7 months (mean 128 days, median 118 days), in accordance with other case series [30,31,32]. All wounds were completely healed without complications, improving the quality of life of the patients and their relatives. MGH exerts a broad-spectrum healing activity and can be effectively used to treat different types of wounds, such as diabetic foot, vascular ulcers, infected traumatic or surgical wounds, burn injuries, and neonatal/pediatric wounds [24,30,31,32,45]. In addition, MGH therapy shows much promise outside the regular scope of topical cutaneous wound care in non-conventional applications and indications [46]. ## 4.1. Patients In this prospective observational case series study, we used MGH wound care products to treat patients with non-healing HPUs. Inclusion criteria were having an HPU lasting more than 4 weeks, the presence of local signs of bacterial contamination or infection, and patient consent. Exclusion criteria were having an allergy to bee stings or MGH, systemic signs of infection or inflammation, and patient non-consent. A total of nine patients (eight women and one man) developed HPUs, of which six patients were due to permanent immobility and three patients due to prolonged immobility post-orthopedic surgery. All patients were recruited prospectively during a 41-month period (April 2019 to August 2022), with a mean follow-up period of 114 days (range 57–218 days, median 118 days). During this period, there was no treatment failure with L-Mesitran products. Data were limited due to loss to follow-up, and death associated to advanced age and severe comorbidities. The average age was 82 years (range 59–94 years, median 86 years), and they all had several comorbidities. In all, seven patients had unilateral HPU and two patients had bilateral HPU. All HPUs were stage III and, upon presentation, the mean length was 5.54 cm (range 4–7 cm) and the mean width was 4.64 cm (3–7 cm), while five patients had concomitant pressure ulcers elsewhere in the body. Different previous treatments, including topical antiseptic or antibiotic products, were ineffective. The diagnosis of wound infection was made through clinical assessment and based on signs and symptoms in and around the HPU. In four patients, we performed local surgical (scalpel) debridement at the bedside. All HPUs treated with MGH were completely healed without any complication within a mean time of 128 days (range 57–218 days, median 118 days). ## 4.2. L-Mesitran Wound Care Products and Therapeutic Interventions L-Mesitran (www.mesitran.com, Triticum Exploitatie BV, Maastricht, The Netherlands) manufactures a variety of MGH-based products designed to treat different types of skin wounds such as pressure ulcers. L-Mesitran Soft (L-MS) is a hydro-active antibacterial wound gel containing $40\%$ MGH. L-MS is applied in contact with the HPU, creating a moist wound-healing environment. This facilitates the autolysis of necrotic and devitalized material, provides bacterial growth inhibition, and promotes the wound-healing process. L-Mesitran Tulle (L-MT) is a non-adhering antibacterial dressing impregnated with L-MS gel. L-MT can be easily applied and used for infected superficial or deep wounds. Moreover, L-MT prevents the secondary dressing from adhering to the wound bed. In all presented cases, L-MS and L-MT were applied in combination and covered with a secondary foam dressing to control the exudate amount and offload the heel region. Initially, the dressing changes were performed at the patient’s home by the wound care professional or by the patient’s relatives. Wound characteristics and photographic documentation at the initial presentation and subsequent follow-up visits were collected and reviewed to assess the wound infection response to MGH therapy and evaluate the wound-healing progress. The patient’s demographic data and treatment protocol are summarized in Table 1. ## 5. Conclusions In the present case series, MGH-based products improved the clinical outcome of hard-to-heal HPUs in elderly patients with multiple and severe comorbidities. MGH is a safe and effective therapeutic approach for locally clinical infected HPUs, and can be proposed as an alternative or complementary to antibiotics and surgery. Furthermore, MGH-based products are easy to apply at home and are cost-effective. This will lead to improving the patient’s quality of life. ## References 1. Bhattacharya S., Mishra R.K.. Pressure ulcers: Current understanding and newer modalities of treatment. Indian J. Plast. Surg. (2015) 48 4-16. DOI: 10.4103/0970-0358.155260 2. 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--- title: Assessment of Stress Caused by Environmental Changes for Improving the Welfare of Laboratory Beagle Dogs authors: - Gwang-Hoon Lee - Woori Jo - Tae-Ku Kang - Taeho Oh - KilSoo Kim journal: 'Animals : an Open Access Journal from MDPI' year: 2023 pmcid: PMC10044678 doi: 10.3390/ani13061095 license: CC BY 4.0 --- # Assessment of Stress Caused by Environmental Changes for Improving the Welfare of Laboratory Beagle Dogs ## Abstract ### Simple Summary Stress is an inevitable element in the course of life that must be accepted, but efforts to minimize it are necessary. In particular, since captive animals in animal testing centers can experience relatively high levels of stress, efforts should be made to alleviate their stress. The aim of this study was to find a suitable environment that can reduce the stress of captive dogs. We conducted a scientific evaluation of the stress caused by environmental changes in dogs. According to the present results, social housing and environmental enrichment reduce dogs’ stress. ### Abstract Animal stress is influenced by environmental factors, yet only a few studies have evaluated the effects of environmental stress on captive dogs. This study aimed to evaluate the effects of environmental and social enrichment on the stress levels of captive dogs housed in a lab. We assessed stress levels in eight Beagle dogs by measuring their body weight, cortisol levels, a stress hormone, the alkaline phosphatase activity in serum, the number of steps per hour, as well as clinical sign observations in a changed environment for 6 weeks. Four dogs assigned to a control group were raised alone in a relatively narrow place without toys; four dogs assigned to an experimental group were raised together in a relatively large place with toys. The body weight of the control group remained unchanged, while that of the experimental group decreased. Cortisol levels in the control group increased throughout, whereas those in the experimental group increased for up to 2 weeks and decreased thereafter. Consequently, cortisol levels in the experimental group significantly decreased compared to the control group at 6 weeks ($$p \leq 0.048$$). Fighting was observed among the dogs in the experimental group at 3 weeks; thus, one dog was separated from the group. The number of steps per hour was more than twice as high in the experimental than in the control group. Thereby, we determined that social housing, with appropriate companions and environmental enrichment materials, can reduce stress levels in captive dogs more efficiently than in single housing without such materials. Our study provides useful insights for captive animal organizations, such as kenneled dogs’ management, to improve animal welfare. ## 1. Introduction Stress can be defined as the process of changing the physical and psychological state to protect the body against external threats and attacks, as well as non-specific biological reactions occurring in the body in response to various injuries and stimuli applied to the living body [1,2,3]. Stress can be categorized as either eustress or distress [4]. Eustress, called positive stress, can result in increased happiness or motivation when encountered. Conversely, distress is elicited by a negative or unpleasant stressor and is commonly associated with the stress response [4]. Stress is commonly used to refer to distress [5]. Animals, including companion animals and captive animals, are known to have increased stress levels due to noise, restriction in kennels, and the presence or lack of companions [6,7]. In particular, captive animals in animal research institutions, animal shelters, or zoos require strict stress management, unlike wild animals or companion animals, because they spend their entire lives in confined spaces [8,9,10]. A previous study reported that the most stressful thing for captive animals is not being able to escape the potential stresses of the artificially created environment, so these stresses should be assessed, and improvements or changes should be made as they are essential for the welfare of captive animals [6]. Dogs are suitable species to be bred as companion animals because they are well known to form bonds with humans [11]. However, in the non-clinical stage for entering clinical trials, animal studies using dogs are often conducted. The efficacy evaluation of new drug candidates and the development of medical devices are mainly conducted using rodents because of their short life span, low cost, and easy handling [12,13,14]. Non-clinical studies using rodents have provided important mechanistic insights, however, their application for non-clinical evaluation is limited due to their anatomical and pathophysiological differences from humans [15,16]. In contrast, non-rodent laboratory animals such as dogs are relatively similar to humans and therefore have higher values in non-clinical studies [17]. Dogs are valuable non-clinical research animals because of the wide range of research subjects and their relative availability among non-rodent laboratory animals [18,19]. Since safety evaluations require testing on one species of rodents, such as mice or rats, and one species of non-rodents, such as rabbits, dogs, pigs, dogs, or non-human primates, animal experiments on dogs are routinely conducted [20]. However, confinement to limited space, social isolation, exposure to unfamiliar surroundings, and prolonged stays in kennels are all potential factors that can lead to reduced welfare for dogs in shelters [21]. Dogs that are taken to animal shelters often display both physical and behavioral indications of being under stress [22]. The cortisol levels in dogs living in a shelter environment during the initial three-day period are nearly three times higher than the levels in household dogs [23]. Laboratory studies have investigated the physiological and behavioral impacts of stress, how to alleviate it, as well as the psychological and behavioral consequences of unchecked stress reactions, with several of the stressors used in these studies closely resembling the conditions experienced by dogs that are kept in animal shelters [24,25,26]. Although human interaction can reduce the stress levels of captive dogs, dogs in animal research facilities and animal shelters are inevitably exposed to long periods of time without humans, contrary to companion animals [23]. It is important to develop strategies that can help alleviate their stress when they are in a confined environment and without the presence of humans. In particular, the provision of toys as environmental enrichment materials was commonly recognized as essential for reducing stress levels and enhancing the welfare of captive animals [27]. However, there is conflicting evidence regarding the efficacy of toys in improving animal welfare. While some studies report the positive effects of toys in reducing abnormal behaviors and increasing activity levels [25,26], other studies suggest that toys have no significant impact on animal behavior or well-being [28,29]. The absence of social contact with conspecifics is known to induce stress in dogs [30]. It leads to negative behavioral changes, such as decreased activity levels and increased stereotypic behavior [29]. Therefore, the social housing of dogs is commonly pursued in animal shelters and animal research facilities [21,31]. Few studies that have evaluated animal stress levels are mainly limited to rodents in animal research facilities, which are not as similar to humans compared to non-rodent animals, such as dogs [32,33]. Physical activity and stress interact with each other. Exposure to stress can hinder achieving a healthy level of physical activity, while physical activity can lead to positive physiological changes in mental health, such as improving self-esteem and reducing stress and anxiety levels [34,35]. Nevertheless, it is undeniable that engaging in moderate exercise can provide healthy advantages [36,37]. Therefore, through the quantification of activity levels, body weight, and body condition score as methods for assessing relative fatness related to animals’ health conditions [38], it is possible to discern the health status of dogs and potentially establish correlations with stress. The activation of the hypothalamic-pituitary-adrenal (HPA) axis in response to stress can lead to increased levels of cortisol and alkaline phosphatase (ALP) activity in serum [39,40]. In the present study, to evaluate the stress of single housing without companions and environmental enrichment (control group) as well as and social housing with companions and environmental enrichment (E.E group), changes in cortisol concentration, alkaline phosphatase activity, blood tests, weight, and the number of steps per hour were measured. We hypothesized that environmental enrichment within a large space might reduce stress levels and increase the activity of captive dogs. ## 2.1. Animals Eight unneutered male Beagle dogs aged from 18 to 21 months (ORIENT BIO, Jeongeup, Korea), weighing 12–14 kg, were used in this experiment after having been vaccinated with canine distemper virus, canine adenovirus (infectious canine hepatitis), canine parvovirus, canine parainfluenza virus, and Leptospira spp. Since young and immature dogs show a high ALP activity due to bone growth, dogs aged 18 to 21 months with skeletal maturity were used [39,41]. The dogs were raised at the Preclinical Research Center (PRC), Daegu-Gyeongbuk Medical Innovation Foundation (K-MEDI hub), accredited by AAALAC International (#001796) in accordance with the guide for the Care and Use of Laboratory Animals 8th edition, NRC [2010]. The dogs were fed a limited amount (300 g/dog/day) of a laboratory dog diet (Production Number 38070, Cargill Agri Purina Incorporated, SeongNam, Republic of Korea) with free access to water purified by a microfiltration system and reverse osmosis process. Dogs were placed in stainless steel cages (size of a cage: 1.12 m wide × 1.62 m deep × 1.85 m high) at room temperature 22 ± 1 ℃, 50 ± $10\%$ humidity, 1.8–4.2 mmAq positive room pressure, and a ventilation cycle of 10–20 times/h. ## 2.2. Housing Environment After an acclimation period of two weeks, the eight dogs were divided into two groups at random, either assigned to the control group or the environmental enrichment (E.E) group in the PRC, K-MEDI hub. As a control group, four dogs were provided with an environment in which they were raised in n 1.814 m2 (size of a cage: 1.12 m wide × 1.62 m deep × 1.85 m high) cage without toys on the stainless steel floor. On the other hand, the E.E group of four dogs was provided with an environment in which they were raised in a 7.258 m2 (size of a cage: 4.48 m wide × 1.62 m deep × 1.85 m high) cage constructed by connecting four cages with a soft plastic floorboard (TAEWOO, Seongju, Korea). The four dogs were provided with four toys (Buster Soft Cube, Product number 27452, dimension 12.7 × 12.7 × 12.7 cm, Kruuse, Langeskov, Denmark) as environmental engineering in the connected cage (Figure 1). The toys were exchanged daily for a cleaned one and were always present in the cage. Food was inserted into the toy every day, and when the dog rolled the toy, the food came out little by little. ## 2.3. Observation of Clinical Signs and Body Weight with Body Condition Score Measurement Clinical signs were observed by visual inspection every day and by palpation once a week. The body weight and the body condition score (BCS) were measured once a week. If a fight due to social housing was found in the E.E group, the dog was separated from the rest. The BCS is a method for assessing relative fatness related to the animals’ health conditions [38]. The BCS was measured by the criteria of the World Small Animal Veterinary Association [38]. ## 2.4. Cortisol Concentration in Serum Measurement and Blood Tests To determine the cortisol concentration in the serum and to perform other blood tests, blood was sampled from the cephalic vein at 2-week intervals, including before the environmental change of placing them into the cages, for a total of 6 weeks. Sampling was performed once only between 17:00 and 18:00, taking into account the end of breeding management and other researchers’ entry and exit. Blood sampling was conducted in less than 2 min to minimize the stress caused by blood sampling because the handling of animals can induce stress [42]. The blood was collected in ethylenediaminetetraacetic acid (EDTA) tubes and serum-separating tubes (SST), followed by centrifugation at 1500 g for 10 min at 4 °C for collecting the serum after 30 min of clotting time. The cortisol concentration in the serum was measured directly using an immunoassay with the immulite 2000 xpi (Siemens, Eschborn, Germany) with a limit of detection at 0.2 µg/dL. The hematology (white blood cells, red blood cells, hemoglobin, hematocrit, mean corpuscular volume, mean corpuscular hemoglobin, mean corpuscular hemoglobin concentration in blood, red blood cell distribution width, platelets, and WBC differential count) was analyzed using a hematology system with autoslide (ADVIA 2120i, Siemens, WA, USA). The biochemistry (alkaline phosphatase, aspartate aminotransferase, albumin, alanine aminotransferase, total bilirubin, triglyceride, blood urea nitrogen, calcium, creatinine, inorganic phosphorus, glucose, sodium, total cholesterol, potassium, total protein, and chloride) was analyzed using a clinical chemistry analyzer (TBA-120FR, Toshiba, Tokyo, Japan). ## 2.5. Feed Consumption Measurement Three hundred grams of feed per dog was supplied daily, and the remaining amount was checked after 24 h to measure daily feed intake. However, in the case of the E.E group, it was not possible to confirm which dog consumed the feed due to social housing; therefore, the average feed intake of both groups was calculated and compared. ## 2.6. Steps per Hour Measurement The number of steps per hour was measured using a canine activity tracker (FitBark, Kansas City, MO, USA). Each dog was fitted to the activity tracker on their neck on a light leash, and the number of steps per hour was measured for 3 hours in a day at 2-week intervals, including before the environmental change of placing them into the cages, for a total of 6 weeks. ## 2.7. Statistical Analyses All statistical tests were performed with. the GraphPad Prism 8.0 statistical software (GraphPad Software, La jolla, CA, USA). All data were expressed as the mean value ± the standard deviation value (S.D). Multiple t-tests were used to analyze all data between groups after checking the normality test using the Shapiro–Wilk test. The Kruskal-Wallis tests were used to analyze all data within the groups after confirming that they did not pass the Shapiro–Wilk test of normality. Values of $p \leq 0.05$ were considered statistically significant. ## 3.1. Clinical Signs and Body Weights with Body Condition Scores In the E.E group, fighting between two dogs was observed, and one of them was separated and reared alone in the same cage as the one belonging to the control group at 3 weeks, however, toys were provided. No statistically significant difference could be confirmed in the body weights and the body condition scores (BCSs) between the two groups during the experimental period (p-value: 0.657 in 1 week, 0.622 in 2 weeks, 0.657 in 3 weeks, 0.657 in 4 weeks, 0.657 in 5 weeks, and 0.657 in 6 weeks). There were also no significant differences within the groups. However, the control group showed little changes in weight with a BCS of 5.50 ± 0.58 during the experimental period, whereas the E.E group had weight loss compared to the pre- E.E group, with a weight loss of 0.56 ± 0.35 kg with a BCS of 4.75 ± 0.50 at 5 weeks, and a weight loss of 0.55 ± 0.40 kg with a BCS of 5.00 ± 0.82 at 6 weeks when the fighting dogs were included. In addition, the E.E group showed an average weight loss of 0.65 ± $0.38\%$ with a BCS of 4.67 ± 0.58 at 5 weeks and an average weight loss of 0.70 ± 0.33 kg with a BCS of 4.67 ± 0.58 at 6 weeks when the fighting dogs were not included (Figure 2). The separated dog lost weight by week 3, after which the weight increased. Moreover, the BCS was 6 at pre, but it decreased to 5 from the 1st to the 5th week, and then it increased to 6 at the 6th week (Figure S1). ## 3.2. Steps per Hour Measurement In the control group, no significant changes were determined over time. In the E.E group, the number of steps per hour was significantly higher than that in the control group at 2 weeks when the separated fighting dog was included (p-value: 0.001 in 2 weeks, 0.106 in 4 weeks, and 0.151 in 6 weeks), and significantly higher than that in the control group at 2, 4, and 6 weeks when the separated fighting dog was not included ($$p \leq 0.01$$ in 2 weeks, 0.001 in 4 weeks, and 0.007 in 6 weeks) (Figure 3). There were also no significant differences within the groups when the separated fighting dog was included or not included. In the case of the separated dog, the number of steps per hour increased at 2 weeks compared to pre, and it decreased at 4 and 6 weeks compared to 2 weeks (Figure S2). ## 3.3. Feed Consumption The average feed consumption per individual was between 280 g and 293 g in all groups during the experimental period. Therefore, there was no change greater than $4.64\%$ (293–280) in the average feed intake over the study period (Figure 4). In the case of the separated dog, the feed consumption was between 277 g and 300 g (Figure S3). ## 3.4. Cortisol Concentration in Serum The average cortisol concentration in the collected serum increased in the control group during the experimental period without any significant differences (pre: 0.66 ± 0.32, 2 weeks: 0.93 ± 0.54, 4 weeks: 1.27 ± 0.53, and 6 weeks: 1.38 ± 0.21 µg/dL). In the E.E group, the cortisol value at 2 weeks was statistically elevated compared to the cortisol value at the pre when the separated fighting individual was included ($$p \leq 0.031$$). There were no significant differences within the E.E group when the separated fighting individual was not included. When the separated fighting individual was included, there was no significant difference between two groups (cortisol value: pre: 0.64 ± 0.28, 2 weeks: 1.70 ± 0.49, 4 weeks: 1.23 ± 0.13, and 6 weeks: 1.07 ± 0.84 µg/dL; p-value: 0.281 in 2 weeks, 0.989 in 4 weeks, and 0.875 in 6 weeks). When the separated fighting individual was not included, the cortisol concentration was significantly lower at 6 weeks in the E.E group (cortisol value: pre: 0.69 ± 0.32, 2 weeks: 1.56 ± 0.49, 4 weeks: 1.17 ± 0.06, and 6 weeks: 0.67 ± 0.29 µg/dL; p-value: 0.438 in 2 weeks, 0.947 in 4 weeks, and 0.048 in 6 weeks) (Figure 5). In the case of the separated dog, compared to the pre, the cortisol levels increased at 2 weeks, decreased at 4 weeks, and then increased again at 6 weeks (Figure S4). ## 3.5. Alkaline Phosphatase Activity No statistically significant difference could be confirmed between the two groups in terms of the alkaline phosphatase (ALP) activity at any time interval when the separated fighting individual was included (p-value: 0.301 in 2 weeks, 0.846 in 4 weeks, and 0.846 in 6 weeks) and when the separated fighting individual was not included (p-value: 0.460 in 2 weeks, 0.983 in 4 weeks, and 0.267 in 6 weeks). However, changes in the average ALP activity were similar to changes in cortisol levels. In the control group, the ALP activity continued to increase throughout the test period. In the E.E group, the average ALP activity increased for up to 2 weeks and then decreased without a significant difference (Figure 6). In the case of the separated dog, the ALP activity increased over time (Figure S5). ## 4. Discussion Stress refers to the state of physical and psychological tension that an individual feels when faced with a difficult situation to adapt to, and it affects various organs due to physiological changes [43,44,45]. We hypothesized that stress levels would decrease when environmental enrichment and appropriate companions were provided. Previous studies have also studied the benefit of environmental enrichment for stress reduction. Hunt et al. demonstrated that environmental enrichment increases relaxation behavior and decreases alert and stress behaviors in dogs [46]. Albanese et al. reported that environmental enrichment increases their positive behavior and reduces the fecal cortisol metabolite in the study of non-human primates, which are another captive animal species. Additionally, their study emphasized that welfare programs considering animal species are important [47]. In terms of the body weight and body condition score (BCS), the E.E group showed an average decrease. However, the effects of stress on weight are conflicting. When stressed, humans consume more sugary, fatty foods and more energy-dense meals, which can lead to obesity [48,49,50]. However, previous studies have revealed that stress causes weight loss along with poor feed intake [51,52,53]. Another study additionally found that stress induces anorexia nervosa and a reduction of appetite and food consumption in dogs, and dogs with positive affective states had a stronger tendency to seek food than dogs with negative affective states [54,55]. In the present study, while the weight of the control group did not change, the weight of the E.E group decreased over time, but we did not judge this result to be an effect of stress. Since all of the dogs used in this study were between 18 and 21 months of age, their skeletal maturity had already been completed [41]. In addition, the BCS was 5.50 ± 0.58 in both pre-experimental groups, however, the BCS decreased after 1 week. For dogs, a BCS of 4 or 5 is ideal, and a BCS > 5 of 9 is considered overweight [56]. Both groups were in a slightly overweight state pre-experiment (BCS: 5.50 ± 0.58); however, only the E.E group recovered to an ideal BCS of 5.00 ± 0.82 when the fighting dogs were included, and a BCS of 4.67 ± 0.58 when the fighting dogs were not included at 6 weeks. This result is supported by the number of steps per hour and the daily feed intake. The relatively large floor area and companions made active exercise possible, and the number of steps per hour was measured at more than twice that of the control group. However, no significant difference could be confirmed in the feed intake between the two groups during any of the experimental periods. Therefore, we judged that even if the E.E group lost weight, it was not due to stress but a normal physiological response to vigorous exercise, as demonstrated in previous studies [57,58]. Activities such as walking have lots of benefits for dogs in disease prevention, as well as for their mental and social health [59]. However, the heightened levels of activity in dogs residing in animal shelters may be attributed to the challenging conditions of the environment, including exposure to various stimuli such as sights, sounds, and odors that make it difficult for them to rest [60]. Given that marked increases and decreases in activity are both recognized indicators of stress, the level of activity can be considered a sign of stress and a coping mechanism employed by dogs to manage their stress levels [61]. In addition, repetitive locomotor stereotypies such as pacing, circling, and spinning, which result in a high number of steps, are commonly observed in confined environments, such as animal research facilities and rescue shelters, however, this does not necessarily lead to a reduction in stress [62]. Thus, although our study found that the increased physical activity in the E.E group had a positive effect on their health by recovering to an ideal BCS, it is inconclusive whether this intervention led to a reduction in cortisol levels. In puppies, a stress-hyporesponsive period (SHRP) exists during which endocrine responses, including cortisol secretion, are suppressed. The SHRP ends at approximately 5 weeks of age and coincides with the termination of lactation [63]. Dogs that are too old exhibit a higher concentration of cortisol, likely due to a decline in stress resistance [64]. We determined that the age range of 18 to 21 months in dogs would be suitable for conducting experiments related to stress. Nevertheless, longitudinal investigations on stress levels in young puppies are of great value since mild stress experienced during this early stage of development can accelerate maturation, enhance problem-solving abilities, and increase social confidence later in life [65,66]. Cortisol, which is an adrenocorticotropic hormone (ACTH)-dependent glycosteroid secreted from the adrenal cortex, is a hormone that is indispensable for maintaining life [67]. Cortisol is the most important hormone for maintaining the homeostasis of the body and in response to stress [40,68]. Stress stimulates the release of the corticotropin-releasing hormone from the hypothalamus, which elevates the secretion of ACTH from the pituitary gland. The ACTH increases the secretion of cortisol from the adrenal cortex, increasing the concentration of cortisol in the blood. The hypothalamic-pituitary-adrenal (HPA) axis is the major pathway for cortisol secretion [69,70]. Cortisol secretion can be induced in the adrenal cortex by various stress factors, such as fatigue, irritability, sleep insufficiency, high-intensity exercise, panic, suffering, or hunger [67,71]. In particular, dogs can experience stress from separation anxiety, changes in routine, loud noises, fear of new situations or unfamiliar people, medical conditions, lack of socialization, and overstimulation [72,73,74]. The secreted cortisol affects muscles, the liver, and fatty tissue to provide energy for the subject to resist stress [75]. Therefore, many studies have evaluated stress in animals under various conditions, such as heat stress or water restriction, both of which strongly induce stress, with cortisol increase as the major factor in stress evaluation [76]. The average cortisol concentration in the collected serum of the control group continued to increase during the experimental period without any significant difference, while the average cortisol concentration of the E.E group significantly increased at 2 weeks compared to the pre and then decreased thereafter without any significant difference. The routine life and single housing of the control group probably caused stress, as recorded in a previous study, and we determined that the stress became more severe over time. The reason that the average cortisol level in the E.E group increased by 1.83 times, compared to the control group at 2 weeks when the fighting dog was included, was probably because of the presence of an inappropriate companion. It may have been time to adapt to a new group until 2 weeks considering the possible competition for enrichment with the inappropriate companion. In our study, dogs consumed most of the provided feed, and the toys that were provided as environmental enrichment materials contained feed. It should be noted that excessive environmental enrichment, particularly when it involves food-based stimuli, may have unintended consequences such as increased competition and altered behavior, as demonstrated in a previous study [77]. The cortisol level significantly decreased after that companion was removed compared to the control group, so it was determined that the stress decreased rapidly due to that removal. The cortisol level in the E.E. group was lower than in the control group at 6 weeks. ALP can be used as a stress factor because it is released after the ACTH stimulates the adrenocortical cells [39,51]. Since both cortisol level and ALP activity increase by the stimulus of the ACTH, we noticed a similar trend of the two factors, as found in previous research. Zimmerman et al. reported that the cortisol level was correlated with the ALP activity after the ACTH stimulation in dogs [78]. The separated dog lost weight by week 3 but gained weight after the separation. In addition, although the number of steps per hour increased in the 2nd week, the average number of steps per hour decreased similarly to the control group in the 4th and 6th weeks after separation. This explains the elastic changes in the body weight and the BCS with cage size. In addition, the cortisol level may have increased by 2 weeks due to the conflict with another dog, then decreased by 4 weeks after separation and eliminating the conflict, and then increased by 6 weeks because of the single housing routine. Respecting the natural behaviors of animals plays a major role in animal welfare [79]. We provided the E.E group with a relatively large area, companions, toys for foraging, and a soft floor to encourage their natural behavior. According to our results, we can support the hypothesis that the environment in the E.E. group reduced stress levels in dogs. A limitation of our study is that several factors, including social housing and environmental enrichment, were applied at once. The small sample size and having only one dog breed (Beagle) are also limitations of this study because captive animals in the zoo or animal shelters consist of various breeds, although animal research facilities almost always use Beagle dogs. In future studies, it will be necessary to evaluate which factors reduce stress levels by setting up multiple groups and applying various factors individually, and various breeds with larger sample sizes should be included to improve the reliability of the study. Additionally, it is necessary to require non-invasive methods, such as behavioral evaluation and cortisol analysis of feces, rather than blood collection by referring to a previous study [47]. Nevertheless, our study proved that the provision of environmental enrichment with appropriate companions reduces stress levels in dogs. This is based on the results that the E.E group had lower cortisol levels and ALP activity compared to the control group and that a wide environment in which exercise was possible helped the physical health of the animals by maintaining an ideal BCS. Our results suggest appropriate improvements to the breeding guidelines for captive dog management in facilities such as animal testing facilities. ## 5. Conclusions We evaluated the effects of environmental stress on dogs living in a restricted area in an animal research institute. Our experimental results suggested that rearing dogs in a large space with social housing, appropriate companions, and environmental enrichment reduces animal stress more than rearing dogs in a small space in single housing without environmental enrichment. However, it is important to consider that certain enrichment materials, such as toys, may have caused social conflicts among dogs. 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--- title: Fungal-Mediated Silver Nanoparticle and Biochar Synergy against Colorectal Cancer Cells and Pathogenic Bacteria authors: - Moath Alqaraleh - Khaled M. Khleifat - Maha N. Abu Hajleh - Husni S. Farah - Khaled Abdul-Aziz Ahmed journal: Antibiotics year: 2023 pmcid: PMC10044691 doi: 10.3390/antibiotics12030597 license: CC BY 4.0 --- # Fungal-Mediated Silver Nanoparticle and Biochar Synergy against Colorectal Cancer Cells and Pathogenic Bacteria ## Abstract Background: Silver nanoparticles (AgNPs) are attractive substrates for new medicinal treatments. Biochar is pyrolyzed biomass. Its porous architecture allows it to hold and gather minuscule particles, through which nanoparticles can accumulate in its porous structure. This study examined AgNPs’ antibacterial and anticancer properties alone and combined with biochar. Methods: The fungus Emericella dentata was responsible for biosynthesis of AgNPs. The characterization of AgNPs using STEM images and a Zetasizer was carried out. Accordingly, the antibacterial and antiproliferation activity of AgNPs and biochar was studied using MIC and MTT assays, respectively. To evaluate the antiangiogenic and anti-inflammatory effects of AgNPs with biochar, VEGF and cytokines including TNF alpha, IL-6 and IL-beta were tested using an ELISA assay. Results: The size of the AgNPs ranged from 10 to 80 nm, with more than $70\%$ of them being smaller than 40 nm. The combination of AgNPs and biochar enhanced the antibacterial activity against all tested bacteria. Furthermore, this combination showed antiproliferative properties against HT29 cancer cells with high selectivity to fibroblasts at low concentrations. AgNPs with biochar significantly reduced VEGF and proinflammatory cytokine expression levels. Conclusions: Biochar and AgNPs may be novel treatments for bacteria and colorectal cancer cells, according to the current findings. ## 1. Introduction Nanotechnology is a broad multidisciplinary field that has evolved worldwide [1,2]. It provides nanoscale components with a high surface area, improving their physicochemical characteristics over their bulk counterparts [3]. In particular, metal nanoparticles and their biological activity are important areas of growing interest in the research field [4,5]. Silver nanoparticles (AgNPs) are a prominent nanoproduct due to their unique chemical, physical and biological properties. They are considered promising platforms for the development novel therapeutic agents, acting against various cancer cells and drug-resistant pathogens [6,7]. Nanotechnology-based medication has been considered a new prospect for opposing microbial drug resistance. AgNPs can effectively kill Escherichia coli, Klebsiella pneumonia and Staphylococcus aureus, fungi (such as Candida albicans, Aspergillus niger) and viruses (such as hepatitis B, respiratory syncytial infection, herpes simplex infection type 1, monkeypox infection and human immunodeficiency virus) [8,9]. Additionally, nanotechnology-based cancer therapy is a new strategy and is considered one of the most promising research directions for cancer [10]. AgNPs could induce cell death of colon cancer cells by destroying the cytoskeleton, altering membrane nanostructures and reducing cell biomechanics [11]. Likewise, Acharya et al. [ 12] demonstrated that biogenically synthesized AgNPs possess a selective apoptotic effect against colon cancer (HCT-116) cells through proapoptotic protein activation and antiapoptotic protein inhibition via DNA damage and disrupting the mitochondrial membrane. Wypij et al. [ 13] showed that biosynthesized AgNPs from actinobacterial strain SF23 capped with proteins might be a potential cytotoxic agent against cancer cells and bacteria. Another study demonstrated that β-sitosterol-derived AgNPs can effectively induce cytotoxicity in human colon cancer cells through induction of early apoptosis via enhanced p53 protein expression [14]. However, the precise mechanism of AgNPs as antimicrobial and anticancer agents has not yet been elucidated [15]. AgNPs have wide-spectrum and highly antimicrobial and anticancer activity even at very low concentrations, so this study aimed to investigate the antibacterial and anticancer effects of fungus-mediated AgNPs when used singly and synergistically with biochar. Biochar is the biomass produced by heating in the absence or limited presence of air at temperatures above 250 °C, in a procedure called pyrolysis. Combinations of biochar and AgNPs could minimize the bioactive agents in the dosage, thereby lowering their noxiousness and boosting their potential antimicrobial and anticancer effects. ## 2.1. Particle Size, Zeta Potential and Morphology The average hydrodynamic diameter of AgNPs was found to be around 45 nm with a particle distribution index of 0.231, and they were found to be highly stable with a zeta potential of −19.6 mV (Figure 1A). According to zeta measurements of the particle size distribution, $97.3\%$ of the AgNPs nanoparticles were 37.62 nm in size (Figure 1B). The examined particles are composed of a large number of tiny particles that are less than 0.5μm in size, as seen in the transmission electron microscope (STEM) images (Figure 2A,B). The scanning transmission electron microscopy micrograph of biochar combined with AgNPs is shown in Figure 2C. ## 2.2. XRD Analysis The crystalline nature of the biosynthesized Ag nanoparticles was confirmed by the X-ray diffraction analysis (Figure 3). Two distinct diffraction peaks at 2θ values of 38.1°, 44.1° (shown with stars) can be assigned to the [1 1 1], [2 0 0] plane, respectively. The appearance of these patterns indicates that the formation of silver nanoparticles is fcc and crystalline in nature (JCPDS file no. 84-0713 and 04-0783). The appearance of several unassigned peaks that were observed in the XRD spectrum could be related to the crystallization of the bioorganic compounds from fungi during the preparation of the silver nanoparticles, which further confirms the formation of ligand interaction between the silver nanoparticles and the bio-organic compounds from the fungal extract. Many research groups have reported similar results during the preparation of Ag nanoparticles using edible mushroom extract and geranium leaves. ## 2.3. ATR-IR Spectra Analysis The ATR-IR spectra of silver nanoparticles produced biologically (Figure 4) show numerous distinct peaks at 633, 989, 1110, 1663, 1704 and 2450, which demonstrated the presence of several organic functional groups that act as a reducing and stabilizing agent on the surfaces of silver nanoparticles. The analysis of this spectrum showed a very broad band around 3080 to 3437 cm−1 assigned to O–H and ~N–H stretching vibrations of amide. The presence of the main amine group of protein is shown by the brevity of the broad peak at 2450 cm−1. The appearance of several bands in the regions from 600–900 cm−1 corresponds to C-H aromatic out of the plane. The band at 1259 cm−1 could be related to the formation of C-O-C stretching in aromatic rings. The strong wide peak between 1400 to 1450 cm−1 corresponds to C-H stretching, while the weak band at around 1520 cm−1 represents the formation of C=C aromatic. Meanwhile, bands in the range of 2900–2990 cm−1 correspond to C-H stretching. The presence of the amide in the protein’s C=O stretching vibration is indicated by the peaks at 1704, 1663 and 1110 cm−1. The stretching frequencies of the amino and amino-methyl stretching groups of proteins have obvious peaks around 1340 cm−1. These organizations may be in charge of the synthesis and conformation of the biological system that accompanies the silver nanoparticles. ## 2.4. Antimicrobial Activity of Silver Nanoparticles The potential interactions between AgNPs and biochar were evaluated using the disc diffusion method or MIC utilizing microdilution techniques (Table 1). When AgNPs were investigated alone, the inhibitory zones shown by AgNPs against S. epidermidis, S. aureus, E. coli, P. aeruginosa, P. aeruginosa ATCC 10,145 and E. coli ATCC 25,922 were 17.5, 18.5, 14.0, 12.5, 12.3 and 13.5 mm, respectively. However, none of the studied bacteria were inhibited by biochar. AgNPs and biochar together significantly improved ($p \leq 0.05$) the antibacterial activity of AgNPs against all bacterial species. When AgNPs and biochar were used together, the inhibiting zones that resulted were as follows: 19.5 ± 0.0 mm, 14.5 ± 0.4 mm, 21.5 ± 0.5 mm, 16.5 ± 0.0 mm, 16.5 ± 0.5 mm and 15.0 ± 0.6 mm. When using silver nanoparticles alone, the MIC values ranged between 6.38 and 19.15 µg/mL. However, when biochar was combined with silver nanoparticles, the range of MIC values decreased by approximately two-thirds, with a range of 2.13–6.38 μg/mL. The lowest MIC values obtained were for S. epidermidis and S. aureus (2.13 μg/mL). Although MIC and MBC values were discovered to differ for various bacteria when they were exposed to AgNPs biosynthesized by fungi, for each bacterium, the MBC matched the MIC exactly. ## 2.5. Modulation of Proliferation of HT29 Colorectal Cancer Cell Line as Well as Fibroblasts by Biochar and AgNPs Figure 5a–d illustrate the antiproliferative abilities of biochar and AgNPs on the HT29 colorectal cancer cell line and fibroblast cell line. The toxicity effect of biochar on HT29 was relatively reduced in almost all concentrations that were tested, compared to the cytotoxicity effect of AgNPs on the same cell line. Furthermore, the cytotoxicity results demonstrated the lack of selectivity of AgNPs compared to biochar against normal fibroblasts. ## 2.6. The Combined Effects of Biochar and AgNPs A concentration of 6 μg/mL of AgNPs was used in subsequent synergy experiments with biochar against the HT29 cell line. The antiproliferative efficacies of biochar with AgNPs on the HT29 cell line are further illustrated in Figure 6. Nevertheless, AgNPs lacked selective cytotoxicity in fibroblasts. However, the AgNPs combined with biochar had an equipotent effect or slightly better effect than AgNPs alone as well as had selective cytotoxicity in fibroblasts. ## 2.7. Inflammation and Angiogenesis A concentration of 6 μg/mL of AgNPs was used in subsequent inflammation and angiogenesis experiments using the HT29 cell line. The anti-inflammatory activity of biochar with AgNPs on the HT29 cell line is shown in Figure 7. AgNPs, biochar and the combination of AgNPs and biochar showed a significant ($p \leq 0.05$) low expression level of TNF alpha, interleukin 6 and interleukin 1 beta. Furthermore, Figure 8 shows the significant ($p \leq 0.05$) downregulation of VEGF under the influence of both AgNPs and biochar. ## 3. Discussion The average hydrodynamic size diameter of AgNPs was found to be around 45 nm with a particle distribution index of 0.231, and they were found to be highly stable with a zeta potential of −19.6 mV. A polydisperse distribution can be identified as such if the PDI (DLS) is greater than 0.2 [16]. Since these particles are electrostatically stable and hence resist self-assembly, they exhibit Z values of this size, which are typical of particles with a substantial amount of charge. The density distribution of our sample shows the degree of dispersion of various sizes. Given that particle size and light scattering are connected, a small portion of a larger component might dominate. A sample’s optical characteristics are used in the number distribution to display the relative number based on the size distribution obtained from data on the density distribution [17,18]. Size distribution of nanoparticles Zeta-measurements showed that $97.3\%$ of the AgNPs are 37.62 nm in size. The examined particles are composed of a large number of tiny particles that are less than 0.5 μm in size, as seen in the scanning transmission electron microscopy (STEM) images. The results of the SEM measurement were examined using the open-source image-processing tool ImageJ [19]. AgNPs generated by biosynthesis were assumed to be spherical with a mean particle size of 30 ± 4.3 nm based on 100 particle size measurements. It was difficult to determine the structure of the discovered AgNPs because the image was unclear when viewed at higher magnification. This may have been caused by sample charging, the presence of nonconducting carbon stabilizers or nanoparticle aggregation into larger composites [20]. Nanoparticles’ SEM images show that they are spherical and range in size from 10 to 80 nm. AgNPs shrank during the drying process, as shown by SEM micrographs, making them smaller than those seen by DLS analysis. AgNPs had an average size of less than 45 nm. The XRD pattern and the presence of peaks verify the synthesis of AgNPs. Resolved diffraction peaks verified the crystalline character of the manufactured AgNPs [21]. Preparation of Ag nanoparticles using edible mushroom extract [22] and geranium leaves [23] yielded similar findings. The existence of numerous organic functional groups that operate as a reducing and stabilizing agent on the surfaces of silver nanoparticles was shown by peaks in the ATR-IR spectra of recently synthesized silver nanoparticles [24,25]. High pyrolysis temperatures have an impact on biochar sorption because they cause an increase in surface area, microporosity and hydrophobicity [26,27]. Biochar is well adapted for use in the adsorption process as soil additives in agricultural settings because it enhances the soil’s physical and chemical properties by enhancing its water-holding capacity, nutrient retention, surface area and water resistance [28]. Date seed (DS) biochar produced at 550 °C was found to be appropriate for remediation of metal-contaminated water. This was evident in the data, which showed that applying the biochar to Raphanus sp. and Arabidopsis sp. reduced metal stress and toxicity [29]. In this study, a combination of 6 μg/mL AgNPs with different concentrations of biochar was made to assess its activity against colorectal cancer cell lines as well as against pathogenic bacteria. Our results showed that biochar had no effect on the growth of either Gram-positive or Gram-negative bacteria, suggested that biochar may have an indirect antimicrobial effect, via altering some bacteria’s metabolic processes [28,30,31]. In contrast, AgNPs suppressed the growth of S. epidermidis, S. aureus, E. coli, P. aeruginosa and P. aeruginosa ATCC 10145. When using silver nanoparticles alone, the MIC values ranged between 6.38 and 19.15 μg/mL. However, when biochar was combined with silver nanoparticles, the range of MIC values decreased by approximately two-thirds, with a range of 2.13–6.38 μg/mL. Although MIC and MBC values were discovered to differ for various bacteria when they were exposed to AgNPs biosynthesized by fungi, for each bacterium, the MBC matched the MIC exactly, suggesting that antibacterial activity is strain(s) specific [32]. As AgNPs have a broad antibacterial range and significant antimicrobial activity, they can efficiently kill a variety of organisms even at extremely low concentrations [33]. Reactive oxygen species (ROS) generation, change in DNA structure and destruction of bacterial cell walls have all been extensively acknowledged as AgNP-related antimicrobial processes [7,34]. Rare occurrences of bacterial resistance to AgNPs have been observed, in contrast to the potential for antibiotic resistance, which might limit the applications of medical technology [35]. These consequences specify that silver nanoparticles could afford a safer alternative to conventional antimicrobial agents in the antimicrobial formulation [36]. Additionally, the results demonstrate that when AgNPs were incubated with biochar on colorectal cancer cells, their cytotoxicity activity increased at low concentrations but their toxicity to fibroblast cells was reduced. Cell viability evaluations are crucial for bioassays that reveal how cells respond to toxic compounds since they may reveal details on metabolic activity, cell death and survival. These analyses demonstrate how cells respond to dangerous substances [37]. The AgNPs’ increased surface area caused them to interface with bacteria on their surface more frequently, that also enhanced their bactericidal activity. Free radicals were produced and DNA was oxidized as a consequence of the bacterial cell wall being punctured and then destroyed [38]. However, the combination of biochar and a sublethal amount of AgNPs (5 μg/disc) fairly quickly inhibited a wide variety of Gram-positive and Gram-negative bacteria. By combining biochar and silver nanoparticles, a novel potency of silver nanoparticles (AgNPs) with significantly improved antibacterial and therapeutic efficacy was created. The impact of mixed AgNPs (100–1000 mg/mL) and biochar ($2\%$ w/v) on maize seedlings in a hydroponic exposure medium was previously studied by Abbas et al. [ 39,40]. The concentration of Ag+ ions in the growth medium dropped as a result of the complexing of the biochar surface brought on by the interaction between the AgNPs and the biochar. Subsequently, the bioavailability of the AgNPs was reduced. The addition of biochar decreased the phytotoxicity of the AgNPs by a ratio of four to eight. The reduced oxidative stress in plants treated with AgNPs and biochar was also responsible for the increased activities of antioxidant enzymes. Based on our findings, biochar made from date seeds is an efficient tool for decreasing AgNPs’ bioavailability. This reduces the toxicity of the AgNPs while limiting their release, resulting in greater selectivity for colorectal cells over fibroblasts. When silver nanoparticles (AgNPs) come into contact with biochar, they likely become immobilized and lose some of their bioavailability. The increased surface area of the AgNPs combined with the complexing of the biochar surface that was caused by the interaction with the AgNPs resulted in the creation of a novel potency of silver nanoparticles (AgNPs) that substantially enhanced antibacterial and therapeutic efficacy. According to research that agrees with our findings, AgNPs may have antitumor effects by inhibiting cell proliferation and inducing proapoptotic events through the p53, Bax/BCL-2 and caspase pathways [11], as well as by causing DNA fragmentation and altering cellular redox status in cancer models for cervical, breast, lung, nasopharyngeal, hepatocellular, glioblastoma and colorectal cancer [9]. In addition, it was discovered that colon cancer cell lines (HCT-116, HT29 and SW620) were sensitive to the anticancer effects of green-synthesized AgNPs made from Commiphora gileadensis stem extracts. Four genes (CHEK1, CHEK2, ATR and ATM) were measured to determine the anticancer activity of green-synthesized AgNPs using real-time polymerase chain reaction (RT-PCR) [41]. In a recent study, the ability to treat colorectal cancer (HT29), colorectal carcinoma (HCT-116), ileocecal colorectal adenocarcinoma (HCT-8 (HRT-18)) and Burkitt’s lymphoma (Ramos.2G6.4C10) cell lines was demonstrated via a combination of graphene oxide (GO) and silver nanoparticles. According to this study, the combination of GO and AgNPs holds significant promise as a novel class of chemotherapeutic agents for the treatment of colon cancer [42]. Additionally, it has been demonstrated that, compared to 5 FU, the biogenic AgNPs produced using honeybee extract exhibited anticolon cancer activities at the cellular and molecular levels [43]. The ionic Ag+ species, which can enter cells and affect nucleic acids (such as starting DNA condensation), form complexes with electron donor groups that are required for protein function and affect cell signaling cascades, is particularly prone to be released by AgNPs [7]. Therefore, nanosilver is a prime option for use in clinical and therapeutic applications as it is less reactive than silver ions [44]. The administration of AgNPs by employing the HT29 cell line as a model results in a decrease in all proinflammatory cytokines examined, including IL-1 beta, IL-6 and TNF-α. Our results are consistent with information from several studies that provide comparable results with AgNPs created using plant extracts, indicating that they have some anti-inflammatory potential [45,46,47]. However, in other study, mammalian macrophages were employed to investigate the direct effects of biochar on immune cells, and the findings revealed that biochar immediately lowered the levels of inflammatory cytokines produced by in vitro activated macrophages [30]. As a result, there is a clear connection between AgNPs and biochar due to the fact that both substances have anti-inflammatory properties on their own, and according to our research, the combination of AgNPs and biochar has anticancer effects. Many chronic inflammatory ailments can be treated effectively by therapeutic targeting of TNF-α [48,49]. Inflammation and cancer appear to be linked, and TNF-α appears to be a key intermediary in that relationship [50]. We infer from this association that the combination of AgNPs and biochar has a true synergistic effect pertaining to cytotoxicity on HT29 cells. Certain bioactive chemicals have been shown in other studies to have an effect on many antiapoptotic genes, hence preventing these genes from performing their early protective mechanism against apoptosis and eventually causing the death of apoptotic cells [51,52,53]. Additionally, it was proposed that AgNPs could control the activation of the TNFR1/NF-KB transcriptional pathway, leading to a considerable decrease in the proinflammatory cytokines in a lung epithelial cell line [54]. In addition, the current study investigated the antiangiogenic activities of AgNPs by using a colorectal cancer cell line as a model for this experiment. The findings suggest that AgNPs with biochar could significantly reduce the expression level of VEGF when compared to untreated cells [55,56,57,58,59]. We hypothesized that the combination of biochar and AgNPs could have an effect on the biological processes that are responsible for neovascularization and inflammation. Studies examining the effects of the newly combined biosynthesised AgNPs and biochar will undoubtedly aid in elucidating the specific mechanism of antitumor action of a potential combined cancer therapy. In conclusion, biochar might prove to be a potential nanocarrier for treating pathogenic multidrug-resistant bacteria and enhancing treatment by joining with AgNPs or other chemotherapeutic agents. ## 4.1. Fungal Strain The fungal strain known as W7B was extracted from soil samples collected in the Al-Karak district of southern Jordan. The ITS sequencing allowed for the determination of the species of the fungal strain (Macrogen, Seoul, Korea). The NCBI database was used to perform a sequence similarity comparison, which indicated that the ITS sequence of the fungus W7B was $100\%$ identical to the ITS sequence of Emericella dentata. The accession number for Emericella dentata was MH032749. ## 4.2. Bacterial Strains and Reagents All of the compounds and media that were utilized were sourced from Sigma-Aldrich. Both Gram-positive and Gram-negative organisms were utilized in the examination of the antibacterial properties. Karak Government Hospital supplied all of these isolates (KGH). These isolates were identified, and their antibiotic profiles were determined, using the BIOMERIEUX VITEK® 2 SYSTEM. Each bacterial strain was subcultured twice on nutritional agar at 37 °C for 24 h prior to testing to guarantee viability, and bacterial cultures were maintained in a refrigerator at 4 °C for subsequent use. All of the solutions were made with highly purified deionized water. Pyrolysis at 550 °C resulted in the production of biochar, as had been documented previously [29]. ## 4.3. Isolation and Screening of AgNP-Producing Fungi The ability of the fungal strain to generate AgNPs was looked into using a previously described method [60]. In summary, an Emericella dentata fungal isolate was initially cultivated aerobically at 30 °C for a number of days in the proper conditions. The cells were suspended in the same sterile medium and centrifuged at 10,000 rpm for 20 min to produce a fungal extract, and the supernatant was combined in a 1:1 ratio with 1 mM silver nitrate. Then, the pH of the reaction mixture was adjusted to 8.5. The obtained mixtures were shaken (200 rpm) at 37 °C in the dark until a transition from a pale yellow to a dark brown color occurred. ## 4.4. AgNP Characterization The absorbance of the produced brown color, which denoted the formation of AgNPs, was determined spectrophotometrically using a UV-1800 spectrophotometer. The reaction solution was repeatedly centrifuged at 15,000 rpm for 20 min to remove the bioformed silver nanoparticles (MIKRO 200 R, Hettich, Germany). The nanoparticles were then resuspended in deionized water for washing. Following recovery, the pellet of silver nanoparticles was vacuum dried (VWR 1410 Vacuum Oven, USA). Using an FEI Versa 3D Dual Beam apparatus (FEI, USA), STEM pictures of the produced AgNPs’ size, distribution and form were obtained. The crystalline phase of the created AgNPs was identified using the MAXima-X XRD-7000. To determine the proteins and other functional groups that contribute to AgNP stability, a Bruker Alpha FTIR spectrometer was used [16]. ## 4.5. Nanoparticle Zeta Potential and Size Distribution The Zetasizer Nano ZS90 was utilized in order to ascertain not only the zeta potential of the AgNPs but also the particulate size of the AgNPs (Malvern Instruments, Malvern, UK). The investigation was carried out at a temperature of 25 °C and a dispersion angle of 90° using samples of varying concentrations that had been diluted with deionized and distilled water, respectively. ## 4.6. Antibacterial Activity of Silver Nanoparticles, Biochar and Their Combination The biosynthesized AgNPs, biochar and their combination were evaluated against Gram-positive bacteria, namely S. epidermidis and S. aureus, and Gram-negative bacteria P. aeruginosa, E. coli, P. aeruginosa (ATCC 10145) and E. coli (ATCC 25922). The pathogenic strains were identified using the BIOMÉRIEUX VITEK® 2 system or Enterosystem 18 R (Liofilchem) after being isolated from UTI patients. The ATCC strains were taken from a lab stock as pure cultures. The antibacterial activities of AgNPs and biochar were evaluated using the disc diffusion method [61]. Briefly, 250 mL of bacterial suspension adjusted to 106 was mixed with 30 mL of melted Mueller–Hinton agar. After solidification, a sterilized disc (6 mm) containing AgNPs (10 μg/disc), biochar (10 μg/disc) or their combination (5 μg/disc of AgNPs and 5μg/disc of biochar), or negative control (DMSO), was transferred aseptically to the surface of the inoculated agar. Then, the plates were incubated at 37 °C for 24 h and the inhibition zone diameter was measured in millimeters. Each sample was tested in triplicate. The stock solution for AgNPs, biochar and their combination was prepared as follows: 1 mg/mL AgNP solution (AgNPs-S), and 1 mg/mL biochar solution (BS). Before use, BS was kept for 24 h at 25 °C while being shaken. The phosphate-buffered water used as the aqueous solution had a pH of 7.4 (Invitrogen, USA). AgNPs-S-BS solution was made by combining 100 μL of AgNPs-S and 100 μL of BS, which was vortexed multiple times before use. To obtain 10 μg/disc of AgNPs and 10 μg/disc of biochar, as well as a combined 5 μg/disc of biochar and 5 μg/disc of AgNPs, 10 μL each of AgNPs-S, BS and AgNPs-S-BS were then poured onto sterile 6 mm discs that had been put on a plate. A center disc, without AgNPs, biochar or their combination, was maintained as control. After that, the plates were kept for 24 h in a 37 °C incubator. The presence of growth-inhibitory zones was checked for after incubating the plates. The sizes of the zones of inhibition (ZOIs) were reported after being measured with a ruler. The test was repeated three times [62,63]. ## 4.7. MIC Determination The broth microdilution technique, recommended by the Clinical Laboratory Standards Institute, was used to determine minimum inhibitory concentrations (MIC) [64]. The test was run in duplicate in 96-well microtiter plates. Triplicate tests were run in 96-well microtiter plates containing nutrient broth for bacterial development. A maximum bacterial concentration of 5 × 105 cfu mL−1 was maintained in each well of the microtiter plate. The final content of the manufactured AgNPs varied from 0.016 to 1024 g mL−1. There were two sets of standards maintained: a positive (broth containing a bacterial inoculum) and a negative (sterile, noninoculated) set. The abundance of the microbial inoculum was determined by counting the number of colonies. The soup was diluted (1:1000) with the microbial inoculum (5 × 105 cfu mL−1), and then 100 μL was spread across the surface of Mueller–Hinton agar. The inoculum density was calculated to be 5 × 105 cfu mL−1 if there were 50 colonies after incubation. The experiment involved a 24 h incubation period at 37 °C for the bacterial microtiter plates. Finally, the MIC values were manually estimated [13]. The minimum bactericidal concentrations (BCs) of AgNPs against bacterial isolates were also determined, which are defined as the lowest concentrations of antibacterial agents that inhibited the survival of >$99.9\%$ of bacteria. Each test sample was incubated for 100 μL before being spread onto an antibacterial agent-free medium. Bacterial growth was monitored on plates after a 24 h incubation period at 37 °C. ## 4.8. Cancer Cell Line Culture A human colorectal cancer cell line, namely, HT29, and human periodontal ligament (PDL) fibroblasts were used. These cells were cultured in DMEM containing $10\%$ FBS, HEPES buffer (10 mM), L-glutamine (100 μg/mL), gentamicin (50 μg/mL), penicillin (100 μg/mL) and streptomycin (100 mg/mL). ## 4.9. Cell Harvesting and Counting All cell cultures were maintained at 37 °C in a humidified $5\%$ CO2 environment. After rinsing the cells in 75 cm2 flasks with 3–5 mL of phosphate-buffered saline (PBS), 1–2 mL of trypsin was added to each flask, and incubation continued until the cells separated. Each cell line had the same volume of new media added to it, and then it was pipetted gently to break up any clumps and create a uniform single cell suspension. Each cell type had a different rate and proportion of proliferating cells. Once the desired quantity of cells was reached, the process of cell propagation was repeated every 2–3 days. Cells were enumerated by transferring a 25 μL suspension of harvested cells and 100 μL of trypan blue dye ($4\%$ final concentration) to the rim of a hemacytometer counting chamber [38]. ## 4.10. Antiproliferative Activity of Silver Nanoparticles, Biochar and Their Combination The biochar and AgNPs were assayed for cell toxicity. Cytotoxicity measurements were based on the viability of the cells present in the culture. Cells were seeded into 96-well plates at a density of 1 × 104 cells per well and incubated for 24 h at 37 °C in DMEM, then incubated with DMEM containing biochar at different concentrations (6–200 μg/mL) and AgNPs (6–200 μg/mL) for 48 h. The subsequent procedure involved conducting the MTT assay, which utilizes 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide as follows: after removing the medium, the cells were cultured at 37 °C for 4 h with 20 μL of MTT solution (5 mg/mL). Next, 200 μL of DMSO was added was added to each well. The microtitre plate was placed on a shaker in order to dissolve the insoluble formazan crystals. We checked the optical intensity at two different wavelengths, 570 nm and 630 nm. The results were collected from three separate wells for accuracy. Primary cultures of human gingival ligament fibroblasts were tested for their ability to selectively inhibit proliferation using an IC50 value that is among the lowest reported. Antiproliferative activities were determined using triplicate assays, and the means ($$n = 3$$) were given ± SD [65,66,67,68]. To assess the synergistic effects of the biochar in combination with AgNPs, cells were seeded into 96-well plates at a density of 1× 10 4 cells per well and incubated for 24 h at 37 °C in DMEM. After incubation, all cell lines were treated with a combination of biochar at different concentrations (6–200 μg/mL) and AgNPs at a concentration of 6 μg/mL. After 48 h, the MTT solution was added to each well and incubated for 4 h. The MTT–formazan crystals formed were dissolved in 100 μL of DMSO and the absorbance was measured at 570 nm and 630 nm. Data were obtained from triplicate wells. ## 4.11. Inflammation and Angiogenesis Assays HT29 cancer cells were cultured in 6-well plates at a density of 500,000 cell/well until $80\%$ confluent. On the day of the experiment, the cells were supplemented with AgNPs at 6 μg/mL and biochar at 200 μg/mL. After 72 h of incubation at 37 °C, incubation medium was collected and stored at −20 °C for a subsequent ELISA determination of the amount of secreted tumor necrosis factor alpha (TNF alpha), interleukin 6 (IL-6), interleukin 1 beta (IL-1 beta) and VEGF. ## 4.12. Statistical Analysis Statistical differences between control and different treatment groups were determined using GraphPad Prism ANOVA followed by Dunnett’s post hoc test. For all statistical analyses, a p-value of less than 0.05 was considered statistically significant. p-values of less than 0.001 were considered to show a highly statistically significant difference. ## 5. Conclusions This study represents the first investigation into the use of AgNPs and biochar in conjunction with one another to combat pathogenic microbes and colorectal cancer cells. Even though biochar by itself did not show any antibacterial activity, the combination of the two had antibacterial activity that was synergistic against all of the strains that were evaluated. Both colorectal and fibroblast cell lines, which were used in this study, were susceptible to AgNPs when they were used individually and even more so when they were combined with biochar. According to the findings of the current research, silver nanoparticles generated by fungi and biochar possess cytotoxic characteristics that can be utilized in various medical applications. Our research has shown that even minute concentrations of biosynthesized AgNPs, when combined with biochar, have the potential to treat drug-resistant bacteria as well as colorectal cancer epithelial cells. 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--- title: 'Doxycycline Prevents Preclinical Atherosclerosis, Pancreatic Islet Loss and Improves Insulin Secretion after Glycemic Stimulation: Preclinical Study in Individuals with a High-Fat Diet' authors: - Alejandrina Rodriguez-Hernandez - Marina Delgado-Machuca - Rodolfo Guardado-Mendoza - Martha A. Mendoza-Hernandez - Valery Melnikov - Osiris G. Delgado-Enciso - Daniel Tiburcio-Jimenez - Gabriel Ceja-Espiritu - Gustavo A. Hernandez-Fuentes - Armando Gamboa-Dominguez - Jose Guzman-Esquivel - Margarita L. Martinez-Fierro - Iram P. Rodriguez-Sanchez - Ivan Delgado-Enciso journal: Biomedicines year: 2023 pmcid: PMC10044702 doi: 10.3390/biomedicines11030717 license: CC BY 4.0 --- # Doxycycline Prevents Preclinical Atherosclerosis, Pancreatic Islet Loss and Improves Insulin Secretion after Glycemic Stimulation: Preclinical Study in Individuals with a High-Fat Diet ## Abstract Doxycycline (Doxy) is an antibiotic, which has exhibited anti-inflammatory activity and glucose metabolism improvement. The present study was proposed to evaluate its effects on glucose metabolism and other associated processes, such as lipemia and adipogenesis, as well as, to evaluate its effects on the liver, pancreas, and aorta in subjects fed with an occidental high-fat diet (HFD). The trial followed three groups of BALB/c mice for 6 months: [1] Standard diet (SD); [2] HFD-placebo (saline solution); and [3] HFD-Doxy (10 mg/kg/day). Intrahepatic fat accumulation (steatohepatosis) and the epididymal fat pad, as well as the hepatic inflammatory infiltrate and ALT serum levels were higher in both groups with the HFD (with/without doxycycline) in comparison with the SD group. The thickness of the aorta (preclinic atherosclerosis) was significantly elevated in the HFD group with respect to the HFD + Doxy and SD group, these two being similar groups to each other. The HFD-Doxy group had pancreatic morphological parameters very similar to those of the SD group; on the contrary, the HFD group reduced the number of pancreatic islets and the number of β cells per mm2, in addition to losing large islets. The index of β cell function (∆Insulin0–30/∆Glucose0–30 ratio) was significantly higher in the HFD + Doxy group, compared to the rest of the groups. ## 1. Introduction Diabetes mellitus is becoming one of the main threats to human health in the 21st century [1]. This disease has been described as a metabolic disorder with multiple etiologies, characterized by alterations in carbohydrate, fat, and protein metabolism due to insulin production anomalies. There are two principal types of diabetes: type 1 diabetes mellitus (DM1), which is due to the autoimmune destruction of beta pancreatic cells, and type 2 diabetes mellitus (DM2) that implies anomalies in secretion and insulin activity. Ideal control of DM1 and advanced states of DM2, require exogenous insulin administration. However, the search for medications to prevent or protect the pancreatic tissue against this disorder is still an ongoing process. It has been postulated that some immunotherapies could delay the deterioration of beta cell pancreatic activity. Particularly, the use of teplizumab, a monoclonal antibody against CD3, could significantly delay (two years interval) the clinical onset of DM1 in selected patients [2]. Doxycycline is a semi-synthetic antibiotic, a member of the tetracycline family, with a broad bacteriostatic spectrum due to its inhibition of protein synthesis by avoiding the binding of bacterial ribosomic units [3]. Periodontitis, an infection that often coexists with severe diabetes mellitus, is commonly treated with doxycycline. Previous studies suggest that doxycycline improves metabolic control in diabetes mellitus [4]. In addition, there have been isolated case reports of youth without diabetes treated with doxycycline who subsequently experienced hypoglycemia [5]. Another study reports that antibiotics could prevent the onset of type 1 diabetes mellitus in mice models prone to DM1; this effect has been attributed to the drug’s bacteriostatic properties, which decrease the stimulation of the immune system [6]. These results have been replicated previously in studies that use doxycycline in a diabetic non-obese mouse model [7]. Other studies in male db/db mice show that doxycycline administration supplemented in drinkable water for 10 weeks improves glucose and insulin tolerance. It also produces an increase in the number of pancreatic islets and beta cell percentage [8]. The db/db mice model (model with leptin gene mutation) is the most used model for DM2 [9]. However, the genetic nature of this model prevents the comparison with healthy individuals; therefore, it was not possible to establish if doxycycline administration can maintain pancreatic morphology and glucose metabolism in similar parameters in healthy individuals. In human beings, a recent clinical trial in which 100 mg/kg/day of doxycycline was administered for 10 weeks, showed reduced inflammation and improved muscular sensitivity to insulin in people with DM2 [7]. Diabetes confers about a two-fold excess risk of coronary heart disease, stroke, and death due to other vascular causes [10]. It has been demonstrated that doxycycline administration could reduce the formation of atherosclerosis or its pathophysiologic process in different mice models generated through mechanic endothelial damage, bacterial infection, or a high-fat diet. In accordance with this information, it has been observed that doxycycline benefits in cardiovascular disease and other related chronic inflammation disorders [11,12,13]. However, the anti-atherosclerotic effect of doxycycline has not been demonstrated in mice fed with a high-fat diet in a non-transgenic mice model [14,15,16], which would better represent the pathophysiology of the disease in humans. Therefore, it is necessary to improve the knowledge that facilitates finding new therapeutic approaches in atherosclerosis prevention and treatment. The present work was conducted to assess if the chronic administration of doxycycline in non-transgenic mice with an occidental high-fat diet could prevent simultaneously atherosclerosis, steatosis, and hepatic inflammation [13,17], as well as pancreatic morpho-functional alterations that deregulate glucose metabolism. In addition, this work also sought to evaluate its effect on serum lipids, adipogenesis, and body weight. ## 2.1. Reactive and Animals Treatment Doxycycline was purchased from Sigma Aldrich (D9891, Saint Louis, MO, USA). A standard diet was used (2018S Tekland Global $18\%$ Protein Rodent Diet, Harlan®, Madison, WI, USA) containing $18.6\%$ protein, $46.9\%$ carbohydrate, and $13.2\%$ fat. The high-fat diet used (TD.02028 Atherogenic Rodent Diet, Harlan®, Madison, WI, USA) contains $17.3\%$ protein, $46.9\%$ carbohydrate, and $21.2\%$ fat. Forty-two BALB/C male mice (Harlan®, Mexico City, Mexico) aged between 4 and 6 weeks of life and with an initial weight between 22 and 25 grammes were used in this study. Two groups (thirteen mice each) were fed with an occidental high-fat diet (HFD, Atherogenic Rodent Diet, Harlan®, Madison, WI, USA). One of them was administered doxycycline to conform to the experimental group (HFD + Doxy group), and the other one was left untreated (HFD group); this last one served as a reference of the pathological alterations produced by the high-fat diet. A third group of sixteen mice was fed with the standard diet (2018S Tekland Global $18\%$ Protein Rodent Diet, Harlan®, Madison, WI, USA); this group was assigned as a reference of the healthy mice (Standard diet, SD, group). The mice were maintained in cages in groups with a maximum of five mice under standard light and temperature-controlled conditions with food and water ad libitum. The drug was administered in dissolved water, as previously described, with a calculated consumption equal to a dose of 10 mg/kg per day. The daily ingestion of water and the mice weights were measured every 15 days, with the purpose of re-calculating the drug concentrations in the drinking water to maintain the established dose. The supplemented water was changed each 48 h. The diet and treatment were administered for 6 months, at the end of which a glucose tolerance curve was made and the mice were sacrificed by decapitation. Blood samples were collected for biochemical analyses, and the pancreas, liver, and aorta were extracted for histological analyses. In this study, a model of preclinical atherosclerosis and non-alcoholic steatohepatitis was employed with genotype native mice (BALB/c). The mice were subjected to a high-fat diet for a period of 6 months, inducing metabolic dysregulation [13]. This mouse model is ideal for evaluating this aspect as it intervenes in a slow development of atherosclerosis and non-alcoholic steatohepatitis in the initial stages [18]. This and other similar models of non-transgenic mice do not develop significant increases in body weight, but do develop morpho-histological and metabolic changes in the early stages of chronic diseases associated with high-fat diets [19,20,21,22]. All animal procedures were handled in accordance with institutional guidelines and the Official Mexican Standard that regulates the use of laboratory animals (NOM-062-ZOO-1999), in addition to the guidelines of the American Veterinary Medical Association 2020 for the slaughter of animals. This protocol was approved by the ethical and investigation committee of the School of Medicine in the Colima University (Colima, Colima, México). ## 2.2. Histopathological Analysis Pancreatic tissue was fixed with $10\%$ formaldehyde, dehydrated in ethanol, embedded in paraffin to be sectioned (5 µm thickness), dewaxed at 100 °C for 20 min, and transferred to xylene, ethanol–xylene, and absolute ethanol solutions, and finally washed in distilled water for immunohistochemical staining. Pancreatic immunohistochemical staining was done using antibodies against insulin (clone HB125), as previously reported [23]. Additionally, the anti-PD1 antibody (Clone IHC001) (programmed cell death protein-1 antibody) and anti-Ki67 antibody (Clone K-2) (100 mL monoclonal mice antibodies at 1:200 dilution; Biogenex, San Ramon, CA, USA) were used, according to the previous reported indications [23]. Each immunohistochemical process was done individually (not combined); as a result, it is not feasible to identify if the positive cells Ki67 or PD-1 were positive to insulin too. The marked pancreatic sections were chosen at random and histomorphometric studies were performed with a digital camera model Axiocam MRC-5 (Zeiss, Göttingen, Germany) connected to a brightfield optical microscope model AxioPlan 2 M (Zeiss®, Göttingen, Germany) with a motorized stage and an A-plan 10x, 20x and 40x objective (total magnification of X100, X200, and X400). Using MosaiX and Autofocus modules, images of all sample surfaces were scanned, and islet areas were measured using contour spline. All images were obtained under the same lighting conditions and exposure times in the imaging program AxioVs 40 V.4.7.0.0 (Carl Zeiss Imaging Solutions GmbH, imaging program 2006-200, Munich, Germany). Three sections of pancreatic tissue per mice were analyzed and each one of three areas (anterior, medial, and posterior) were measured. The number of islets per mm2 was determined as previously described [24]. For the islet size distribution analyses, the islets smaller than 10.000 µm2 were designated as “small”, islets larger than 10.000 µm2 but smaller than 25.000 µm2 were designated as “medium”, and the ones larger than 25,000 µm2 were designated as “big” [25]. The total number of beta cells per mm2 was obtained by counting the nuclei surrounded by cytoplasmic insulin immunostaining. The insulin-positive cells were measured based on their area positive of insulin divided by their nuclei number. Additionally, the area covered by the nuclei in beta cells, was determined to calculate the nuclei and cytoplasmatic area. Inside each islet, the area covered with immunostaining insulin-positive cells, Ki67 and PD-1 were measured, in which the percentage of islet area covered for beta cells or for cells with proliferative and apoptosis markers. Liver and aorta processing was done as previously reported [17]. Hepatic inflammation and steatohepatosis were evaluated, checking the percentage of hepatic tissue with inflammation and fat accumulation. Both steatohepatosis and hepatic inflammation were classified as grade 0 (absent), grade 1 (until $33\%$), grade 2 (between $33\%$ and $66\%$), and grade 3 (more than $66\%$), and also were identified as mild, moderate, and severe [13,17,25,26]. The abdominal aorta was analyzed in three portions (proximal, medial, and distal). A quantitative evaluation of the atherosclerosis was made by measuring the thickness of the medium intimacy (from the inner endothelial layer to the outer of the medial layer). A systematic randomized sampling was used to select eight sections equidistant by section, regardless of whether atherosclerotic lesions were present or absent [13,17]. ## 2.3. The Body and Liver Weight, and Epididymal Fat Pads Body and liver weight were obtained to calculate the liver–body weight ratio, which has been reported to increase in animal models with hypercaloric diets [27]. Furthermore, the fat pad of the epididymis was determined, as it provides a good parameter to study alterations in rodent fat accumulation (the adipogenic effect, which occurs when fat accumulation increases) [28]. ## 2.4. Glucose Tolerance Curve A glucose tolerance curve was measured intraperitoneally at 6 months. Animals were kept fasting overnight, and glucose was injected IP (2 mg/kg of body weight). Glucose and insulin were measured in blood samples at 0, 30, 60, 90, and 120 min after administration. A venous blood sample was collected from each mice tail. The area under the insulin curve (iAUC) and the incremental glucose area under the curve (gAUC) were calculated using the trapezoidal method. Early response to insulin (∆Insulin0-30/∆Glucose0-30 ratio), also known as the insulinogenic index, was calculated as previously described [29] ## 2.5. Biochemical Analyses Before the slaughter, blood samples were collected from the mice after 6 h of fasting to measure serum lipids (triglycerides, total cholesterol) and liver enzymes (ALT, AST), using an automatic biochemical analyzer (Cobas c111, Roche ®, Mexico City, Mexico). A commercial ELISA kit was used for insulin detection (EZRMI-13K, Millipore, Darmstadt, Germany). ## 2.6. Statistical Analyses Data was collected and analyzed using the SPSS statistical software (Version 20, IBM, Armonk, NY, USA: IBM Corp., Chicago, IL, USA). The Komolgorov–Smirnov test was realized to evaluate the normality distributions of the data. Unidirectional analysis of variance (ANOVA) was used to compare data with a normal distribution, using Dunnett’s post hoc test. Post hoc Kruskal–Wallis and Mann–Whitney U tests were used to compare data with a non-normal distribution. ## 3.1. Biochemical and Histopathological Analysis Table 1 shows values of various parameters at the end of the study and a statistical comparison between mice fed the standard diet (SD), high-fat diet (HFD), or HFD plus doxycycline (HFD + Doxy). Table 2 shows a “post hoc” analysis of the parameters that were significantly different shown in Table 1 (fat accumulation, inflammation, and preclinical atherosclerosis), making comparisons such as SD vs. HFD, SD vs. HFD + Doxy, or HFD vs. HFD + Doxy. According to the results, body weight and serum lipid profile were similar between groups. Doxycycline administration in the high-fat diet groups did not produce significant changes in feed intake (Table 1). The accumulation of liver fat (steatosis) and epididymal fat pad was higher in the two groups with a high-fat diet (with and without doxycycline), compared to the group fed with a standard diet (see Table 1 and Table 2). The high-fat diet caused an elevation of parameters associated with liver inflammation, such as hepatic inflammatory infiltrate and ALT levels. In the HFD-diet mice, administration of doxycycline reduced steatosis, inflammatory infiltrate, and serum ALT levels, although not significantly. Another variable associated with chronic systemic inflammation, such as thickening of the aorta (preclinical atherosclerosis), was significantly elevated in the HFD group, compared to the HFD + Doxy or SD groups, the latter two being similar groups between them (see Table 1 and Table 2). Given the above, one can conceive that doxycycline is not able to reduce the adipogenic effect, steatosis, hepatic inflammatory infiltrate, or alterations of liver enzymes, caused by a diet rich in fat, but it could prevent preclinical atherosclerosis generated by this diet. ## 3.2. Pancreatic Morphology Mice fed with the high-fat diet receiving doxycycline had pancreatic parameters similar to those fed with the standard diet (see Table 1 and Table 2). In contrast, the high-fat diet reduced the number of pancreatic islets and the number of β cells per mm2, also losing the large islets (Figure 1). Cell size was increased in individuals with a high-fat diet (with and without doxycycline) with respect to those fed with a standard diet, being the only aspect of the pancreatic morphology in which the SD and HFD + Doxy groups differed. The size of the islets and the percentage of area occupied by β cells on each islet were similar in all groups. The HFD group had a significantly higher percentage of proliferating cells in its pancreatic islets (Ki67-positive cells) in comparison to the SD or HFD + Doxy groups, as well as a higher proportion of cells in apoptosis (PC-1-positive cells), but without being statistically significant. ## 3.3. Glucose Metabolism No differences in fasting serum glucose were observed between groups. Fasting insulin levels were remarkably increased in the HFD + Doxy group, compared to the standard diet group (see Table 1 and Table 2). On the glucose tolerance curve, the HFD + Doxy group had significantly lower blood glucose at 30 min than other groups, including the group fed with the standard diet. In contrast, the high-fat diet group (without doxycycline) had a significantly elevated blood glucose at 30 min compared to the rest of the groups (see Figure 2A). Serum insulin levels were elevated at baseline in the doxycycline group and incremented greatly at 30 min (see Figure 2B), according to its lower blood glucose levels at the moment of the trial. The HFD + Doxy group had a significantly lower gAUC 0–120 min and a significantly higher iAUC/gAUC 0–90 min ratio than the HFD group (without doxycycline) (see Figure 2C,D). The β cell function index (ratio ∆Insulin0–30/∆Glucose0–30) was significantly higher in the HFD + Doxy group compared to the rest of the groups, while the HFD group (without doxycycline) had the lowest values between groups (see Figure 2F). This indicates that high-fat chronic diets reduce the ability of β cells to release insulin into the blood, while doxycycline administration increases it significantly. ## 4. Discussion Administration of doxycycline prevents the loss of pancreatic islets and β cells in individuals chronically fed with a high-fat diet. Additionally, doxycycline increases the ability of β cells to release insulin after a glycemic stimulus. This indicates a dual effect of doxycycline, possibly acting as a protector of the morphology of the pancreatic islets and as an enhancer of the function of the β cells. It should be noticed that the non-transgenic mice model fed with a high-fat diet used in these experiments allowed the evaluation of preclinical stages of diabetes [30] and atherosclerosis [31]. It is important to mention that in the preclinical stages of metabolic disorders, the fasting serum parameters of lipids, glucose, and insulin may not present alterations, so it is necessary to evaluate other more predictive or sensitive tests to establish differences between individuals [18,32,33,34]. In the present work, the parameters of fasting glucose, cholesterol, and triglycerides that were evaluated did not present differences between the groups. However, notable benefits were found in the group treated with doxycycline in the early stages of metabolic disorders and preclinical atherosclerosis. Further to its antibacterial properties, doxycycline also exhibits anti-inflammatory, anti-apoptotic, and antioxidant properties [35]. Therefore, it has been postulated as a potential treatment of diseases associated with chronic inflammation through the non-selective inhibition of matrix metalloproteinases (MMPs) [36]. Additionally, it has been postulated that treatment with a sub-antimicrobial dose is able to reduce the inflammatory mediator’s serum concentration and thereby lower the risk of onset of cardiovascular diseases [37]. The relationship between atherosclerosis and diabetes has been widely studied, although, atherosclerosis is generally assumed to be a result of processes triggered by diabetes [38]. However, the present study shows that damage to pancreatic cells and atherosclerosis are generated simultaneously in the context of a high-fat diet and that doxycycline is able to prevent both alterations. Doxycycline is a semi-synthetic antibiotic, a member of the tetracyclines, that has proved to improve the metabolic control of diabetes [4]. Furthermore, there are isolated cases of non-diabetic youth with doxycycline-induced hypoglycemia [5], which is compatible with the increase in serum insulin generated due to doxycycline [6,7,8]. The study by Wang et. al., [ 2017] in a db/db mice model of leptin deficiency (with a mutation in the gene encoding the leptin receptor), found that doxycycline increases the number and percentage of pancreatic islets of β cells, while decreasing the size of the islets. Unlike the report of Wang et. al., [ 2017], the results of the present study indicate that the effect of doxycycline on the pancreatic islets and beta cells is due to the protection against “the loss” caused by the high-fat diet, thus, preventing the loss of pancreatic tissue, and not due to an increase in pancreatic cell mass. We can also mention that the size of the islets is not reduced by doxycycline, and also, it prevents the loss of large islets due to the consumption of a diet rich in fat, remembering that large islets are the ones that are preferentially lost in DM2 [39]. In general, the use of doxycycline avoids deleterious histological changes caused by the high-fat diet, maintaining histological parameters at values similar to those of healthy individuals (with a balanced diet). The only histologic aspect of the pancreas that varies between an individual on a balanced diet and an individual on a high-fat diet plus doxycycline is the size of the β cell, which is significantly larger in the doxycycline group. This could be compatible with some degree of beta cell hypertrophy, which would be consistent with higher insulin levels, baseline and during the glucose tolerance curve, in the HFD + Doxy group than in the standard diet group (see Figure 2). This is manifested in a significantly higher index of β cell function (∆Insulin0–30/∆Glucose0–30 ratio) in the HFD + Doxy group, compared to the rest of the groups in this study (see Figure 2). This indicates that chronic diets high in fat reduce the ability of β cells to release insulin into the blood, while the administration of doxycycline increases it significantly. In the animal model used in the present study, doxycycline administration did not reduce body weight, the liver–body weight ratio, nor the liver fat accumulation or serum lipids. Liver inflammation tends to be minor, but not significantly. The former suggests that doxycycline’s effects on atherosclerosis and pancreatic protection is not due to an anti-adipogenic, antilipemic, or systemic anti-inflammatory pathway, but the effect could be secondary to a specific mechanism involved in both pathological processes. Vascular endothelial dysfunction is a hallmark of most conditions associated with diabetes, as well as atherosclerosis. Recent studies show that hyperglycemia and atherosclerosis share many common mechanisms, such as endothelial activation and inflammation, mitochondrial oxidative stress, changes in extracellular matrix components, and disruption of cellular defense systems. As a result of diabetes, ROS production increases and antioxidant activity decreases, factors that are also closely linked to atherosclerosis [38]. Doxycycline can molecularly interfere with various common mechanism factors between hyperglycemia and atherosclerosis. Nuclear factor κB (NF-κB) is a transcription factor that triggers hyperglycemia and induces deleterious effects on endothelial function. In recent studies, doxycycline has been found to act on T-cell lymphoma and breast cancer cell lines by inhibiting the activation of NF-κB [40,41,42]. The inhibition of this protein may explain how doxycycline prevents the genesis of atherosclerosis and glucose metabolism disorders simultaneously. It would be necessary to verify this in future studies. On the other hand, TNF alpha production increases in individuals who eat a high-fat diet, inducing endothelial dysfunction, which could worsen atherosclerosis [43]. In agreement with the beneficial effect of doxycycline at the vascular level, it has been found that it is capable of inhibiting the expression of TNF alpha, along with other proinflammatory cytokines [44], which are phenomena associated with the genesis of diabetes [45] and atherosclerosis [46]. In addition to this, others studies mention that doxycycline is able to prevent testicular deterioration caused by the high-fat diet, which could suggest that doxycycline protects various endocrine organs [47]. All of the above is consistent with the results of the present study. In the search for new drugs and strategies to prevent and control diabetes, multiple therapeutic recommendations have been postulated. Some studies refer that the use of a ketogenic diet could improve metabolic aspects in the individual [48]. In this type of diet, the increase in ketone bodies helps to maintain the morpho-functional characteristics of pancreatic tissue associated with the homeostasis of blood glucose levels [48]. Considering the previous information, one perspective of the present study would be the evaluation of the effects of doxycycline in individuals with diabetes and other pathologies submitted to a ketogenic diet. Doxycycline showed pharmacological characteristics that would allow its incorporation as adjuvant treatment in the prevention of diabetes and atherosclerosis in preclinical stages. An adverse effect of antibiotics on the host refers to damage to the microbiota, generating alterations in the metabolism of sugars and lipids. In this study, this factor was not controlled [49]. The gut microbiome plays an important role in extracting energy from food and inducing obesity [50]. It is conceivable that the effect of doxycycline on the microbiota could interfere with the body weight behavior of the animal model. Furthermore, it is important to note that a limitation of the non-transgenic animal model used is that it does not develop a significant increase in body weight from a chronically high-fat diet. Therefore, the effect of doxycycline on body weight in this model may not be adequate. It would be desirable to carry out future studies with other animal models and with a focus on preserving the intestinal microbiota. One of the strategies that could be established is the structural modification of doxycycline in its “A ring” (antimicrobial activity), reducing its action on the microbiota without losing its effects on other molecular targets [51]. Another interesting aspect to be addressed in future research is the effect of doxycycline on cardiac and physiological alterations related to mitochondrial function [52], since this was not evaluated in the present study. Additionally, epicardial fat was not evaluated, which constitutes an important factor to be analyzed in future studies. Finally, in the present study, the molecular mechanisms that prevent atherosclerosis and pancreatic histological damage were not analyzed, an aspect that needs to be studied in the future. ## 5. Conclusions The administration of doxycycline could be able to simultaneously prevents preclinical atherosclerosis and the loss of pancreatic islets and β cells in individuals chronically fed a high-fat diet, while possibly increasing the ability of β cells to release insulin into the blood. This could indicate a dual effect of doxycycline, as a protector of the morphology of the pancreatic islets and as an enhancer of the function of the β cells. 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--- title: Evaluation of Selected Parameters of Oxidative Stress and Adipokine Levels in Hospitalized Older Patients with Diverse Nutritional Status authors: - Katarzyna Mądra-Gackowska - Karolina Szewczyk-Golec - Marcin Gackowski - Alina Woźniak - Kornelia Kędziora-Kornatowska journal: Antioxidants year: 2023 pmcid: PMC10044703 doi: 10.3390/antiox12030569 license: CC BY 4.0 --- # Evaluation of Selected Parameters of Oxidative Stress and Adipokine Levels in Hospitalized Older Patients with Diverse Nutritional Status ## Abstract Malnutrition is classified as one of the Giant Geriatric Syndromes. It carries serious consequences, such as sarcopenia or depression, which lead to functional disability. The main objective of this study was to identify parameters of oxidative stress and adipokines, which may be potential biomarkers of malnutrition in hospitalized older patients. During the study, selected parameters were determined in 137 senile patients, taking into account their nutritional status determined according to the Mini Nutritional Assessment (MNA), as well as an additional tool, namely the Geriatric Nutritional Risk Index (GNRI). Leptin and resistin were determined as the parameters with statistically significant differences between the patients classified according to the MNA. This phenomenon was confirmed using the GNRI classification. However, additional parameters for which differences were observed include the oxidized low-density lipoprotein level and activity of glutathione peroxidase. In conclusion, the determination of the mentioned markers in hospitalized senile patients as an adjunct to the routine assessment of nutritional status might be suggested to identify the early risk of malnutrition so that a personalized nutritional therapy can be implemented as early as possible. ## 1. Introduction Malnutrition is a serious medical concern in senile patients, affecting the length of stay in hospital, treatment costs, the number of infections, the frequency of complications, and mortality [1]. The nutritional status of older patients often deteriorates during hospitalization due to increased need for nutrients, aversion to certain foods, loss of appetite, nausea, or problems with oral food intake. Despite the high incidence of malnutrition in hospitalized older adults and the availability of recognized methods to identify malnourished patients, screening is still carried out insufficiently and malnutrition is rarely identified in healthcare facilities. The prevalence of malnutrition in hospitalized older patients oscillates between 30 and $90\%$ in relation to the method of identification [1,2,3]. Malnutrition, as one of the Giant Geriatric Syndromes, is a condition related to the aging process, diseases, and medications taken, as well as the environmental factors. In older patients, a decrease in the rate of basic metabolism and total energy expenditure, salivary secretion disorders leading to dysphagia, difficulties in forming a food bite, and impairment of the senses of smell and taste (atrophy of taste buds) are frequently observed. Diseases contributing to malnutrition include cancer, heart failure, chronic obstructive pulmonary disease, chronic kidney disease, hyperthyroidism, depression, stroke, Parkinson’s disease, dementia, as well as other conditions in which the patient has dysphagia, nausea, vomiting, diarrhea, or constipation. Appetite, feeling of hunger and satiety, and the occurrence of nausea may be affected by pharmacotherapy, which may influence the amount of food consumed [4,5]. Excessive polypharmacy, including overprescription of proton-pump inhibitors (PPIs), may increase the risk of serious health conditions. Up to $14\%$ of depression cases, according to Laudisio et al. [ 6], could be avoided by withdrawal of PPIs in senile patients. Mood disorders, especially late-life depression, are especially common among senile institutionalized patients. More than one third of nursing home residents are prescribed with antidepressants, predominantly selective serotonin reuptake inhibitors (SSRI), which may affect the appetite [7]. The environmental causes include the lack or non-use of dentures, unattractive form of serving meals, and unfavorable socio-economic conditions limiting the purchase of certain food products or self-preparation of meals [4]. Malnutrition that has not been identified and/or nutritional management that has not been implemented belong to conditions dangerous for a senior and can have serious consequences [4]. The quality of life deteriorates, treatment of seriously ill people becomes more difficult and can lead to increased mortality, muscle mass and strength, as well as psychomotor efficiency, decrease noticeably, promoting gait instability and increasing the risk of falls. The disadvantageous health changes include disturbances in the function of the digestive system, disturbed respiratory system, an increase in the risk of pneumonia, problems with the circulatory system, impaired consciousness, water-electrolyte disturbances, susceptibility to bedsores and infections due to the decreased liver synthesis of proteins, hypochromasia, and osteoporosis caused by an insufficient supply of calcium and vitamin D [8]. A number of adverse consequences of malnutrition necessitate the assessment of the patient’s nutritional status at an advanced age in order to implement an appropriate nutritional intervention. Oxidative stress accompanies many age-related diseases, such as chronic obstructive pulmonary disease, acute and chronic kidney disease, cardiovascular disease, type 2 diabetes, neurodegenerative diseases (especially Alzheimer’s and Parkinson’s diseases), or cancer [9,10]. Moreover, it is comprehensively described in sarcopenia and the frailty syndrome. The abovementioned diseases are associated with impaired activity of antioxidant enzymes and/or excessive generation of reactive oxygen and nitrogen species (RONS). It is postulated that senile dementia is also associated with the pro- and antioxidative imbalance. Various types of oxidative stress biomarkers that can supply vital information on the role of oxidative stress in the pathogenesis of different clinical conditions as well as the aging process have been identified [11]. According to numerous observations, there is a relationship between oxidative stress, including lipid, protein, and DNA peroxidation, endogenous antioxidants and total antioxidant activity, and nutritional status, such as poor nutrition, overweight, and exogenous antioxidant intake, in older adults. Evidence shows that there is a negative correlation between oxidative stress and nutritional status in older individuals [9]. Oxidative stress, cell aging, and the senescence-associated secretory phenotype (SASP) participate in the pathogenesis of abovementioned diseases, which are also related to the inflammatory state mediated by, among others, interleukin 1α (IL-1α), interleukin 6 (IL-6), and interleukin 8 (IL-8) [11]. Free radicals have a crucial role in the immune response, and severe nutrient deficiency leads to increased oxidative stress in the intracellular space. In turn, chronic oxidative stress leads to the activation of the immune system. Thereby, a vicious circle is formed, in which continuing oxidative stress and inflammation exacerbate each other, thus increasing morbidity and age-related mortality. On the one hand, there is no clinical evidence for the effectiveness of administering antioxidants as dietary supplements; however, on the other hand, seniors are recommended to eat fruits and vegetables and are encouraged to maintain a typical body mass index (BMI) between 23 and 28 kg/m2 [9,12,13]. Recently, adipokines, substances produced specifically by adipocytes, whose synthesis and secretion are regulated by nutritional status, have become a target of interest in studies involving older adults [14,15,16,17]. Leptin, acting on the central nervous system, regulates food intake. Low concentration of leptin stimulates and high concentration suppresses appetite. In addition, leptin increases the secretion of cytokines dependent on lymphocytes Th1, stimulating the development of inflammation, activates monocytes, and lowers insulin levels [18,19]. Resistin induces the synthesis of pro-inflammatory cytokines, such as tumor necrosis factor α (TNF α) and IL-6, and also promotes insulin resistance, mainly in hepatocytes, by activating gluconeogenesis enzymes and enhancing glycogenolysis [20]. Adiponectin is one of the most important adipokines secreted by adipose tissue, which has an anti-inflammatory effect. Its beneficial effect on the metabolism of carbohydrates and fatty acids is observed by lowering the concentration of triacylglycerols, free fatty acids, and glucose in the blood, as well as by enhancing the action of insulin. Moreover, by inhibiting the transformation of macrophages into foam cells, adiponectin has also an anti-atherosclerotic effect [20,21]. Melatonin, a hormone secreted by the pineal gland, has a multidirectional effect, such as the regulation of circadian cycles and energy metabolism. Moreover, its role in oxidative stress is emphasized as an excellent antioxidant and a factor regulating the functioning of the immune system. It was also found that melatonin normalizes the expression and secretion of two abovementioned adipokines, namely leptin and adiponectin [21]. The aim of the present contribution was to determine the relationship between the nutritional status of hospitalized senile patients and selected oxidative stress markers and adipokine levels, and to assess whether any of those parameters could be used as biomarkers of malnutrition, so that measuring their levels or activities might facilitate early identification of malnourished patients during hospitalization. ## 2.1. Participants The presented cross-sectional observational study included 137 older adults (96 women and 41 men), who were hospitalized at the Geriatrics Clinic of the Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Poland, as part of a comprehensive geriatric assessment. The mean age of the examined patients was 80.5 years (SD ≈ 7.78). The study was approved by the Bioethics Committee of Ludwik Rydygier Collegium Medicum in Bydgoszcz, Nicolaus Copernicus University in Toruń, Poland (No. KB $\frac{134}{2016}$). All participants were informed about the principles and purpose of the study and signed an informed consent to participate in the study. In the case of patients with moderate dementia, written consent to participate in the study was also given by relatives or guardians of the patients. The inclusion criteria comprised the age of 65 years and above and the somatic condition enabling a full examination with the use of selected scales. The exclusion criteria included inability to move independently, bedridden state, Parkinson’s disease, cancer, acute illness, as well as deep cognitive disorders preventing full contact with the patient (Mini–Mental State Examination (MMSE) score below 10). ## 2.2. Study Design The study involved interviewing, also using questionnaires, including the full Mini Nutritional Assessment (MNA) test, and collecting blood samples for the determination of selected parameters of oxidative stress and adipokine levels. Blood samples for biochemical tests were collected by qualified medical professionals in the morning (between 8:00 a.m. and 9:00 a.m.) after overnight fasting from the cubital vein into three polypropylene tubes. Two tubes (vol. 4 mL each) with a clotting activator were used to obtain blood serum, and another tube (vol. 4 mL) containing K2EDTA allowed the preparation of blood plasma and erythrocytes. One out of the two collected tubes in order to obtain serum was immediately transported to the Laboratory Diagnostics Unit of the Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Poland, for the measurement of albumin. Other tubes were directly transported under reduced temperature condition to the Department of Medical Biology and Biochemistry at the Collegium Medicum in Bydgoszcz of the Nicolaus Copernicus University in Toruń, Poland, and centrifuged (6000× g for 10 min at 4 °C). Then, the blood serum and plasma samples were separated. The serum specimens were stored at −80 °C for further analysis, and the plasma samples were directly used to determine the concentration of malondialdehyde (MDA). The blood cells remaining after centrifugation were washed three times with phosphate-buffered saline (PBS) at a ratio of 1:3 and each time centrifuged (6000× g for 10 min at 4 °C) to remove leukocytes and thrombocytes. The red blood cells were then mixed with the PBS solution, resulting in a suspension of erythrocytes with a $50\%$ hematocrit index, and immediately used to measure the activity of the antioxidant enzymes and the concentration of MDA. In order to grade the nutritional status of the participants, the MNA questionnaire was utilized as an integrated part of comprehensive geriatric assessment. The MNA test consists of simple measurements and brief questions that can be performed in less than 10 min. It is divided into four sections as follows: [1] anthropometric measurements (weight, height, and weight loss), [2] global assessment (six questions concerning lifestyle, medication, and mobility), [3] dietary questionnaire (eight questions, linked to a number of meals, food and fluid intake, and autonomy of feeding), and [4] subjective assessment (self-perception of health and nutrition) [22]. The sum of 18 questions can provide a maximum score of 30. On the basis of the MNA score, the patients involved in the study were divided into three groups: adequate nutritional status, at risk of malnutrition, and protein-calorie malnutrition (Table 1). Additionally, the Geriatric Nutritional Risk Index (GNRI) was used as a complementary tool to the MNA questionnaire. This method is even simpler and more efficient than the MNA in assessing the nutritional status of senile patients. GNRI is calculated by using the following equation:GNRI = [1.489 × albumin (g/L)] + [41.7 × (weight/WLo)] where WLo means an ideal weight calculated using the following the Lorentz formula:For male: height (cm) − 100 − [(height in cm − 150)/4] For female: height (cm) − 100 − [(height in cm − 150)/2.5] When the “weight/WLo” is equal to or greater than 1, the ratio is set to 1 [23]. The interpretation of the GNRI index was carried out in accordance with the classification presented in Table 2 [24]. ## 2.3. Biochemical Analysis The activity of selected antioxidant enzymes was determined in a freshly prepared erythrocytic suspension with the use of spectrophotometric methods. Activity of Zn/Cu-superoxide dismutase (SOD-1; EC 1.15.1.1) was measured according to the Misra and Fridovich method [25]. Analysis was based on the inhibition of adrenaline oxidation to adrenochrome in alkaline solution at 37 °C, which induced a change in the absorbance at 480 nm. A unit of SOD-1 activity was defined as the amount of enzyme that causes $50\%$ inhibition of the reaction with a maximum increase in absorbance of 0.025 units per minute during a directly proportional step of adenochrome formation. Activity of SOD-1 was expressed in IU/g Hb. Catalase (EC 1.11.1.6) activity was determined by the Beers and Sizer method [26], measuring the decrease in the absorbance at 240 nm of a solution of hydrogen peroxide decomposed by the enzyme at 37 °C. CAT activity was expressed in IU/g Hb. Activity of cytosolic glutathione peroxidase (GPx; EC 1.11.1.9) was assayed using the method of Paglia and Valentine [27]. The principle of the GPx activity measurement method is based on the ability of the enzyme to reduce hydrogen peroxide while simultaneously oxidizing reduced glutathione (GSH) as a coenzyme at 37 °C. The oxidized glutathione (GSSG) produced by GPx is then reduced by glutathione reductase (GR) using reduced nicotinamide adenine dinucleotide phosphate (NADPH). The formation of NADP+ from NADPH during this step is accompanied by a change in absorbance measured at 340 nm. Activity of GPx was expressed in IU/g Hb. Erythrocytic and plasma MDA concentration was determined with the method of Buege and Aust [28] in the modification of Esterbauer and Cheeseman [29]. The MDA concentration was measured as the concentration of thiobarbituric acid-reactive substances (TBARS), determined at 532 nm at room temperature. The MDA concentration in erythrocytes was expressed in nmol/g Hb and in blood plasma in nmol/mL. The commercially available enzyme assay kits were used to determine the serum concentrations of adiponectin (HUMAN ADIPONECTIN ELISA, HIGH SENSITIVITY, BioVendor, Brno, Czech Republic), leptin (HUMAN LEPTIN ELISA, Clinical Range, BioVendor, Brno, Czech Republic), melatonin (ELISA Kit for Melatonin (MT), Cloud-Clone Corp., Houston, TX, USA), oxidized low-density lipoprotein (ox-LDL/MDA Adduct ELISA, Immundiagnostik, Bensheim, Germany) and resistin (HUMAN RESISTIN ELISA, BioVendor, Brno, Czech Republic). The storage time of the frozen serum samples did not exceed the period indicated by the manufacturers of the ELISA kits used, allowing for reliable results. The measurements were performed according to the manufacturer’s instructions. All reagents necessary for the study, standard concentration analytes, blanks, and controls were provided in the ELISA kits used. The principle of the assay is to bind the antigen by specific anti-human monoclonal antibodies that coat the wells of a microplate. The antigen concentration was determined using the calibration curve prepared simultaneously with the test in the analyzed samples. ## 2.4. Statistical Analysis Statistical analysis of the results was carried out by means of the Statistica 13.3 (TIBCO Software Inc., Palo Alto, CA, USA). Statistical analysis encompassed Student’s t-test for independent samples and Shapiro–Wilk test to test the hypothesis of normal distribution. The requirements for a one-factor analysis of variance were not met, and, for that reason, Kruskal–Wallis test was used as a nonparametric alternative to evaluate the equality of variances for a variable calculated for two or more groups. The level of significance was set at $p \leq 0.05.$ ## 3. Results Among the 137 participants, the average age was 80.5 ± 7.78 years, $70.0\%$ females and $30.0\%$ males. As many as 47 ($34.31\%$) people were malnourished, 12 patients ($8.76\%$) were at risk of malnutrition, and 78 patients ($56.93\%$) were characterized by adequate nutritional status according to the MNA score. In turn, based on the GNRI score, 12 patients ($8.76\%$) at high risk, 17 patients ($12.41\%$) at moderate risk, and 21 patients ($15.33\%$) at low risk of nutrition-related complications were identified. The condition of 87 patients ($63.50\%$) did not indicate the risk of nutrition-related complications. ## 3.1. Selected Oxidative Stress Parameters and Adipokines According to the MNA In the case of the nutrition status assessment using the MNA score (malnutrition, at risk of malnutrition, adequate nutritional status), the parameters for which statistically significant differences were determined include leptin and resistin. These variables are marked in yellow in Table 3. The other tested parameters did not show any differences with the assumed significance level. In order to determine precisely between which groups the differences were found, a comparison of medians using box–whisker plots was performed in the further part of the analysis. Leptin is the first parameter that differs between malnourished patients and those having a normal nutritional status. The comparison of the medians, presented in Figure 1, revealed a statistically significant increase in the leptin concentration in the case of people with a normal nutritional status, which means a decrease in its level along with a deepening of malnutrition. In the matter of resistin (Figure 2), its level is inversely related to the improvement of the patient nutritional status. A high concentration of resistin was noted in the fraction containing malnourished people. Along with the improvement of the nutritional status, designated using the MNA, the concentration of the tested adipokine was reduced. ## 3.2. Selected Oxidative Stress Parameters and Adipokines According to the GNRI In the presented study, the GNRI was used as adjunct to the MNA questionnaire to assess the risk of nutrition-related complications. According to the GNRI, four subgroups of patients were distinguished as having high, moderate, low, and no risk of nutrition-related complications. Referring to the data presented in Table 4, statistically significant differences (marked in yellow) between the designated groups were identified for leptin and resistin, analogously to the MNA. However, additional parameters for which significant differences were determined include oxLDL and GPx. For the level of oxLDL, the medians for the patients classified to the group of high, moderate, low, and no risk of nutrition-related complications are 96.87, 77.94, 105.62, and 55.42 ng/mL, respectively (Figure 3). Thus, the greatest difference is observed between the groups at low risk and no risk of nutrition-related complications, with the first one having the highest median. However, referring to the “no-risk” fraction, the highest individual oxLDL value was noted here. Surprisingly, in the group of patients at low risk of nutritional complications, the median is higher than in the patients at high and moderate risk. Moreover, in the group at high risk of nutritional complications, the smallest range of the assessed parameter was observed. Interestingly, grouping according to the MNA classification did not allow observing similar differentiation. Such results may indicate that the type of diet and other factors associated with the risk of atherosclerotic processes may be more significant for the level of oxLDL, and this parameter is not a good indicator of the risk of malnutrition. However, the explanation of such a distribution of the oxLDL variable requires a more detailed analysis on a larger group of older people. Analysis of GPx activity in the erythrocytes of the study participants allowed to determine certain regularities. The median erythrocytic activity of this enzyme (Figure 4) is higher in the groups of patients with high and moderate risk of nutrition-related complications. Lower enzymatic activity is noted in the other groups. The lack of confirmation of this phenomenon using the MNA implies the need to confirm the obtained results. The analysis of leptin in relation to the GNRI showed a similar result as for the MNA. There was a statistically significant difference between the group of people at high risk and those without risk of nutrition-related complications. Leptin level is the highest in the fraction of people without a risk of nutrition-related complications. On the contrary, the lowest level of this parameter was determined in the group of senile patients with the highest risk of nutrition-related complications. In other words, the analysis of medians (Figure 5) indicates a gradual increase in the leptin concentration, along with a decrease in the risk of nutrition-related complications. The highest level of resistin was determined in the older patients at high risk of nutrition-related complications (Figure 6). Significant differences were found between the groups designated by the GNRI as high risk and no risk of nutrition-related complications. The analysis showed the greatest difference between these groups. A gradual decrease in the concentration of resistin was also observed along with minimizing the risk of nutrition-related complications. The confirmation of this result was obtained by also using the MNA classification of the patients. ## 4. Discussion The phenomenon of malnutrition in senile patients, although frequently underestimated, is one of the Giant Geriatric Syndromes, i.e., chronic and complex disorders that lead to functional disability. Malnutrition significantly reduces the quality of life of seniors [4]. Due to an aggregation of diseases and impairments, older adults tend to be more susceptible to nutritional deficiencies [30]. Surprisingly, over a half of patients at nutritional risk do not receive nutritional support or counselling, despite an active contact with healthcare professionals [31]. It generates the need to identify malnourished patients, especially during hospital stay, and implement appropriate nutritional management. For this purpose, it is necessary not only to routinely use screening tools for malnutrition, but also to search for new biomarkers. In the present cross-sectional study of 137 patients admitted to the Geriatrics Clinic of the Antoni Jurasz University Hospital No. 1 in Bydgoszcz, Poland, as part of a comprehensive geriatric assessment, $34.06\%$ were recognized as malnourished and $8.70\%$ were at risk of malnutrition as classified by the MNA. The seriousness of the problem was also confirmed by complementary assessment of the risk of nutrition-related complications using the GNRI. This approach revealed that $36.5\%$ of the study participants are at risk of complications (from low to high) related to the nutritional status. A similar number of malnourished patients was also identified in a single-center cross-sectional study by Son and Kavak [32], which included 102 individuals at the age of 65 years and older who were patients of the Family Health Center in the Cobanlar District in the Province of Afyon, Turkey. Out of 102 patients, whose nutritional status was evaluated with the use of the MNA scale, $38.2\%$ were malnourished, $18.6\%$ were at risk of malnutrition, and $43.1\%$ displayed a normal nutritional status. Another cross-sectional study conducted in Saudi Arabia confirmed high occurrence of malnutrition among hospitalized senile patients [33]. As many as 248 hospitalized patients with an average age of 70.0 ± 7.7 years were recruited for that study and their nutritional status was assessed on the basis of the short form of Mini Nutritional Assessment (MNA-SF). In total, $76.6\%$ of patients were at risk of malnutrition or undernourished. The accumulated data show the real scale of the problem in the daily practice of a geriatrician, indicating that practically every third patient manifests an abnormal nutritional status. Therefore, a possibility of malnutrition should be taken into account during a hospital stay from the perspective of diagnostics, pharmacotherapy, and making adequate nutritional recommendations. The incidence of malnutrition in hospitalized older adults depends on many factors and, in global assessment, the differences may be significant. Undoubtedly, malnutrition is a common problem among senile hospitalized patients, closely related to the length of hospital stay, as well as mortality in this group of patients. In view of the above, finding a biomarker that adequately predicts nutritional status of a senile patient and is not influenced by inflammatory, fluid, or septic imbalance complications would facilitate the identification of hospitalized patients having abnormal nutritional status. In everyday practice, the MNA tool is the most frequently used to assess the nutritional status of patients. Additionally, the GNRI, used in the presented study as a complement to the MNA questionnaire, is one of the most commonly applied methods for identifying malnourished geriatric patients [34]. The quintessence of the conducted study was the analysis of selected parameters of oxidative stress and adipokines in relation to the nutritional status of recruited patients determined using the MNA, as well as to the risk of nutrition-related complications on the basis of GNRI (Table 3 and Table 4). Among ten parameters determined in the subjected patients, in the case of the MNA, differences at a statistically significant level were confirmed for two of them, namely leptin and resistin. Similarly, in the case of the GNRI, significant differences were also found for leptin and resistin, but, in addition, statistical differences were revealed for oxLDL and GPx. Taking into account both classifications of the nutrition status, namely the MNA and the GNRI, leptin was found to vary significantly between the designed groups of senile patients. Leptin is a main peripheral messenger contributing to the regulation of food ingestion and energy balance. During starvation, the level of circulating leptin drops significantly, activating processes that increase food ingestion and decrease energy spending. The discovery of leptin as a hormone governing body mass and energy balance gave hope for a new approach to the treatment of obesity, involving its administration. Surprisingly, it has been shown that there are large levels of circulating leptin in the blood of people with obesity, which has been associated with the development of resistance to this hormone in people with excessive body mass [35]. In the presented study, the leptin concentration showed a downward trend along with the deterioration of the nutritional status, expressed both as the MNA score and the value of the GNRI. Significantly higher values were recorded in properly nourished patients compared to other groups, which is particularly illustrated by the GNRI (Figure 5). In other studies, a similar trend was observed and the possibility of using this marker in the assessment of the nutritional status of senile patients was indicated. These studies found a positive relationship between leptin levels and body fat mass in healthy individuals [36] or anthropometric indices [14,15], but no significant correlation with C-reactive protein [15]. Serum leptin may be a good prognostic factor of energy malnutrition, except for the cases of low creatinine clearance, diabetes, end-stage disease, or thyroid disease. As mentioned above, leptin levels decrease as nutritional status deteriorates. In comparison to the MNA tool, it may be an easier and more efficient method of recognizing energy malnutrition in older adults, because it does not depend on the subject’s memory and subjective judgment and allows avoiding systematic errors commonly made in anthropometric measurements. It can be especially useful for patients who cannot be weighed due to impaired motor skills or postural instability [15]. According to the presented study, leptin may be a promising biomarker of malnutrition of older patients. The trend is clearly visible, but a larger group of subjects and analysis according to the sex of involved patients are necessary to draw unambiguous conclusions. In fact, some authors suggested possible cut-off values of serum leptin depending on the sex of patients, i.e., approximately 4 ng/mL in men and 6.48 in women [14,15]. According to the presented study, resistin is also an adipokine related to the nutritional status of older patients, assessed with the use of both the MNA and GNRI scales. Resistin belongs to a family of cysteine-rich secreted proteins (the RELM family), newly discovered in 2001, related to the activation of inflammatory processes. It is also described as ADSF (adipose tissue-specific secretory factor) and FIZZ3 (a molecule found in inflammatory zones). The role of resistin was linked to the occurrence of obesity, insulin resistance, and diabetes in mice. However, no significant similarities in the structure and biological function of human and animal resistin have been shown in some studies [20,37]. Some authors indicated that plasma resistin levels are elevated in obese people, whereas other authors reported higher plasma resistin concentrations in athletes with high insulin sensitivity in comparison to obese people [20]. Resistin produced by human macrophages stimulates the production of TNF-α, IL-6, and interleukin 12 (IL-12) [19]. According to the latest reports, resistin is treated not only as a pro-inflammatory cytokine, but also as an atherogenic one. It is probably involved in inflammatory processes associated with obesity. However, its importance in physiological and pathophysiological conditions such as malnutrition is still uncertain [38]. Recent reports have suggested a pro-inflammatory impact of resistin on the development of dementia, especially in vascular dementia in geriatric patients [39]. However, abdominal obesity has not been found to have a significant effect on resistin levels in dementia patients. In the presented cross-sectional study, resistin levels were found to be inversely proportional to the patient’s nutritional status (Figure 2 and Figure 6). The literature on the subject mostly includes studies on resistin in patients with kidney disease [40]. For instance, in the study involving 80 senile patients, it was shown that the concentration of resistin increases with a decline in glomerular filtration rate (GFR), which may be related to malnutrition associated with the disease [41]. In non-diabetic patients with chronic kidney disease, a negative correlation between serum resistin concentration and albumin level as a marker of malnutrition was observed, which is consistent with the results of the presented study [41]. In the presented study, the significant differences in blood plasma oxLDL level were found between the groups of older patients classified by the GNRI. Oxidized low-density lipoprotein is regarded as an important biomarker of oxidative stress. It also plays a key role in the development of atherosclerosis. Increased plasma oxLDL concentration has been recognized as a prognostic indicator of mortality in the general population with congestive heart failure [42]. Dainy et al. [ 43] analyzed the relation of nutritional status, physical activity, and oxidative stress with cognitive function of 40 pre-elderly and 35 elderly subjects. The study revealed the prevalence of malnutrition in $60.0\%$ and $80.0\%$ of participants, respectively. However, only visual memory function was associated with oxLDL. Another study revealed a significant relationship between high oxLDL levels and cardiac systolic dysfunction in patients on continuous hemodiafiltration [44]. In the presented study, the highest level of oxLDL was determined in the older people with a high risk of nutrition-related complications, which may be associated with the highest level of oxidative stress and deficiency of antioxidant vitamins in the poor diet of this group of patients. In the patients with a low risk of nutrition-related complications, the level of oxLDL decreased and finally, in the patients with a moderate risk, the level of oxidative stress increased again. The lowest levels of oxLDL were recorded in the well-nourished subjects (Figure 3), which could be associated with an adequate supply of vitamins and other compounds with antioxidant activities. It is necessary to conduct a more thorough analysis on a larger group of people in order to confirm or exclude this phenomenon. Particularly, the results obtained in the case of the group with a low risk of nutrition-related complications are ambiguous. Obligatory research is also emphasized by the fact that variable grouping according to the MNA did not allow determining such a differentiation. With regard to GPx, higher median enzyme activity was found in the senile patients at high and moderate risk of nutrition-related complications compared to low and no risk of such complications (Figure 4). In the patients at high risk of nutrition-related complications, the enzyme activity hardly decreased below 6 U/g Hb, which was observed in other groups. High GPx activity in patients at high risk of nutrition-related complications according to the GNRI might indicate the highest level of oxidative stress in this group of patients. Both current results and previous reports indicate that there is an inverse relation between oxidative stress and the nutritional status of older patients [9,45]. Moreover, severe oxidative stress is linked to a presence of metabolic syndrome in older individuals [46]. Malondialdehyde is a marker of the intensity of lipid peroxidation. Its increased level is observed in the adipose tissue of people with obesity [47]. In the conducted study, no statistically significant differences were observed between the groups determined by the MNA and the GRNI. Similar observations were made in a study of 69 older patients in a good health, living in the city [48]. No differences in the level of MDA were observed when examining the level of oxidative stress in relation to the control patients. However, an increase in the GPx activity was noted, as it was in the case of the presented study. Superoxide dismutase and catalase are part of the antioxidant defense system. These antioxidant enzymes belong to the first line of defense against reactive oxygen species [49]. No statistically significant differences were determined according to the nutritional status for the abovementioned enzymes in the presented study. Similarly, in the study by Pinontoan et al. [ 50], no statistically significant differences were found for the erythrocyte SOD activity in non-frail and frail geriatric patients. The literature on the subject indicates an increase in oxidative stress markers (e.g., MDA) and a decrease in the activity of antioxidant enzymes (e.g., SOD and CAT) with the aging of the human body [9,51]. Although a relation between oxidative stress and the progression of the aging process is postulated, the influence of the nutritional status of senile patients on the activity of antioxidant defense enzymes has not yet been determined. One of the parameters analyzed in the presented study is adiponectin, one of the most important adipokines secreted by adipose tissue. It possesses a strong anti-atherosclerotic and anti-inflammatory effect, lowers the level of glucose in the blood, and has a positive effect on the metabolism of carbohydrates and fatty acids [21,52]. Higher serum adiponectin level in older patients is accompanied, among other things, by a history of weight loss, low skeletal muscle mass, and poor physical functioning [53]. In the case of malnourished patients suffering from cancer, as shown by Bobin-Dubigeon et al. [ 54], increased adiponectin levels as well as decreased levels of leptin were observed. From the other point of view, Huang et al. [ 55] revealed that high serum adiponectin levels and low BMI were both linked to worsening depressive symptoms among older Japanese individuals. What is more, the combination of high adiponectin levels and low BMI was associated with worsening depressive symptoms. Although the adiponectin level was found the highest in the group at high risk of nutrition-related complications in the presented study, the lack of statistical significance does not allow drawing conclusions, and it is suggested to extend the study for a larger group of patients in the future. Aging is accompanied by a considerable decrease in endogenous melatonin secretion, which exacerbates oxidative stress and induces other deleterious metabolic alterations [11]. Melatonin is a compound secreted by pinealocytes in a circadian rhythm. Impaired hormone secretion can lead to the disruption of the circadian rhythm of the secretion of adipokines related to satiety and hunger, such as leptin and ghrelin, leading to an excessive supply of energy. There are few results in the literature concerning the effect of melatonin supplementation on the normalization of body mass [11], but there is no information on the role of melatonin in malnutrition in seniors. In the presented study, no statistically significant relationship between the nutritional status of older hospitalized patients and the melatonin level was observed. Interestingly, Soysal et al. [ 56] found a close relationship between MNA scores and insomnia or insomnia severity in older adults. Patients suffering from insomnia had lower MNA scores than those without insomnia. Moreover, there were significant relationships between moderate/severe insomnia and the presence of malnutrition, the risk of malnutrition, and the MNA score. ## 5. Conclusions The conducted study revealed relationships between the nutritional status of senile hospitalized patients and some of the analyzed parameters of oxidative stress and adipokines. Significant differences between the groups of patients identified on the basis of the MNA were confirmed for the serum levels of leptin and resistin. The additional division into groups regarding the GNRI not only confirmed this phenomenon, but also indicated two additional parameters, namely the serum level of oxidized low-density lipoprotein and the erythrocytic activity of glutathione peroxidase. In the studied population, a higher leptin concentration was related to an adequate nutritional status of the patient, whereas the resistin level was inversely related to the nutritional status. An analogous relationship was not observed in the case of adiponectin. 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--- title: 'The effect of research on life satisfaction in middle-aged and older adults: physical disability and physical activity as a parallel and serial mediation analysis' authors: - Pei-Shan Li - Chia-Jung Hsieh - Ya-Ling Shih - Ya-Ting Lin - Chieh-Yu Liu journal: BMC Geriatrics year: 2023 pmcid: PMC10044714 doi: 10.1186/s12877-023-03873-7 license: CC BY 4.0 --- # The effect of research on life satisfaction in middle-aged and older adults: physical disability and physical activity as a parallel and serial mediation analysis ## Abstract ### Background and objective Maintaining the life satisfaction of frail middle-aged and older adults when they experience physical disability, lower activity status, or complex conditions that are related to each other is now an urgent issue. Therefore, the purpose of this study was to provide evidence for the impact of frailty in middle-aged and older adults on life satisfaction under the simultaneous occurrence and correlation of physical disability and physical activity status. ### Methods Data from the 2015 Taiwan Longitudinal Study in Ageing (TLSA) were analyzed by PROCESS in SPSS to explore three different mediation models ($$n = 4$$,421). The first was a parallel mediation model for exploring life satisfaction in middle-aged and older adults with frailty through physical disability or physical activity. The second was a serial mediation model for examining physical disability and physical activity in causal chains linked with a specific direction of flow and to test all combinations. The third was a moderated mediation model for testing whether the indirect effect of frailty status on life satisfaction through physical disability or physical activity was moderated by age stratification. ### Results Physical disability and physical activity partially mediated the relationship between frailty status and life satisfaction (IEOVERALL = -0.196, $95\%$ CI: -0.255 to -0.139). The causal path with the highest indirect effect was found to be that between frailty and physical disability; increased frailty led to higher physical disability, which in turn affected physical activity, leading to lower life satisfaction (IE = 0.013, $95\%$ CI: 0.008 to 0.019). The different stratifications by age significantly increased the mediating effect of physical activity (Index of Moderated Mediation = -0.107, SE = 0.052, $95\%$ CI: -0.208 to -0.005) but did not reduce the mediating effect of physical disability. ### Conclusion This study provides evidence that physical activity and physical disability influence the development of frailty. It also has a significant impact on the life satisfaction of middle-aged and older adults. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12877-023-03873-7. ## Introduction As the global population structure trends towards ageing, it can be expected that the life expectancy of older people will continue to increase, which in turn will cause the public to change the model and concept of elderly care [1]. Frailty status (FS) is common in older people. The morbidity rate in the frail stage was 12–$24\%$, and the morbidity rate in the pre-frail stage was as high as 46–$49\%$ [2]. When older adults respond to the onset of ageing, they begin to feel systemic skeletal muscle loss, muscle strength decline, and organ system degeneration[3]. This progressively reduces the physical activities that could otherwise be mastered, gradually limiting mobility [4]. This allows ageing to form the basis for the FS of older adults [5]. Frailty not only affects the physiological health of older adults but also causes a decline in physical activity (PA) functions, and such a decline will affect their daily lives, leading to a decline in life satisfaction (LS) [6]. The long-term predicament of frailty issues highlights the importance of achieving healthy sustainability in elderly care goals in order to maintain LS. Many older adults are often burdened by different states such as ageing, frailty, or disability, but ageing and frailty or disability are often compared due to their similarities [7]. When older adults begin to have difficulty performing basic activities of daily living, which marks the progression toward physical disability (PD) [8]. Further study results show that nearly $60\%$ of older adults have lost the ability to live independently due to PD [9]. The evidence suggests that prolonged periods of low PA levels in older adults are highly related to frailty morbidity rates and increased FS [10]. The World Health Organization (WHO) recommends that older adults engage in physical activity with a variety of components on three or more days a week to enhance functional capacity [11]. Thus, increasing the level of PA in daily life will help older adults control the degree of FS and the risk of frailty [12]. Most studies on frail older adults in the community have shown that FS is related to limited PA function [13]. Further studies have set PA as a predictor of frailty in older adults and emphasised the importance of PA in maintaining health in old age and promoting healthy ageing [14]. Accordingly, we speculated that PD and PA may mediate the direct and indirect relationships between LS and FS. At present, the evidence on the effects of reducing the development of PD, increasing the level of PA, or improving LS in older adults is relatively fragmented. The factors that affect LS among older adults have been extensively studied and found to include demographic, physiological, and mental health-related factors [15]. For example, age and gender differences, as well as chronic conditions and health restrictions among middle-aged and older people, were found to increase the risk of LS [16, 17]. Current avenues of study are based on the consideration that the LS of older people will be affected by health-related conditions [18]. When older adults are in a frail state, it is still controversial whether to directly assess the impact of PD or PA [3]. It can be seen that the levels of PD and PA of frail older adults are serious threats to LS [6]. Due to a lack of evidence, the mechanisms underlying the mediation effects of PD and PA were not considered. Accordingly, it is imperative to confirm the association between FS and LS by utilizing a database and to determine how to improve LS in middle-aged and older adults. While there is a correlation between PA and the morbidity rate of PD [19], there is currently a lack of evidence to explore the impact of PD on LS in frail middle-aged and older adults, and past literature has not validated the relationship between PA and LS. Maintaining LS has become an urgent issue for the frail middle-aged and elderly populations who experience PD, PA status or interrelated complex situations. Following the above inference, it is crucial to identify PD and PA as potential explanations for the association between FS and decreased LS. Therefore, one hypothesis of this study was that PD and PA would be mediating factors between FS and LS in middle-aged and older adults. The second research hypothesis was that the mediation effect would be different between middle-aged and older individuals. The purpose of the research was to provide evidence for the simultaneous relationship, interrelationship, and causality between PD and PA in middle-aged and older adults and their impact on LS. ## Data collection This study was an observational and retrospective study based on the “Taiwan Longitudinal Study in Ageing (TLSA)”. The TLSA was conducted by the Bureau of Health Promotion under Taiwan’s Department of Health and established in response to the ageing of Taiwan’s population and policy need. The development of the TLSA database was a collaborative effort of the Taiwan Health Promotion Bureau, the University of Michigan’s Population Research Center, and the University of Michigan’s Institute of Gerontology to jointly conduct cohort surveys, data collection, and follow-up on the physiological, psychological, and social status of the middle-aged and older-age populations in Taiwan. The TLSA subjects were selected based on a three-stage probability and stratified random sampling method to select middle-aged and older samples representative of Taiwan. The database maintained quality through a structured questionnaire, which was validated by experts. All data in the database are collected with strict process control. The first wave of TLSA surveys began in 1989 and recruited subjects over the age of 60. The second wave of TLSA surveys began in 1996 and recruited subjects over the age of 50 [20]. TLSA data have previously been used in gerontological studies related to health in middle-aged and older adults [21–25]. ## Participants This study sampled data added to the TLSA database in 2015 for analysis. The 8th wave of the TLSA targeted residents with households registered in Taiwan at the end of April 2015. The TLSA database was collected through individual interviews with participants who completed the training. The structured questionnaire was validated by experts and gathered data on characteristics, health status, social support, employment status, leisure and social participation, aging mentality, economic status, etc. The newly added sample included 5,304 middle-aged and older adult respondents aged 50 years or older at the end of April 2015. The exclusion criteria were as follows: (i) cognitive impairment with a score of less than 2 on the Short Portable Mental State Questionnaire (SPMSQ); 324 participants were excluded based on an SPMSQ score of less than 2. ( ii) missing values in the variables; a total of 559 participants with missing values in the variables were excluded. After screening by inclusion and exclusion criteria, a total of 4,421 middle-aged and older adults were included in this study. ## Frailty status as predictors variables FS was defined according to the Fried criteria, and characteristics included shrinking, exhaustion, slowness, weakness, and low PA. The frailty score of 0 to 5 points was based on five characteristics, and a higher score indicated greater severity of FS. Additionally, no characteristics indicated the non-frailty stage; less than 2, the pre-frail stage; and over 3, the frail stage [26]. The Fried criteria have high validity and reliability and are the most commonly used measure of frailty [27]. In the TLSA questionnaire, no widely accepted operational definition of frailty was available. Based on the Survey of Health, Ageing and Retirement in Europe (SHARE), modifications have been made to the definition of frailty to fit the data [23]. Subjects who reported frequent loss of appetite in the previous week were defined as “shrinking“[21, 22]. Subjects who answered “I could not get going” or “I felt everything I did was an effort” frequently or for most of the previous week on the Center for Epidemiologic Studies Depression Scale (CES-D) were defined as “exhaustion” [21, 22]. Subjects who were unable to walk a distance of 200 to 300 m or found it difficult were defined as “slowness” [21, 22]. Those who found it difficult to carry 12 kg of groceries were defined as “weakness” [21, 22]. Those who did not partake in walking, hiking, jogging, gardening, or other outdoor activities at least once or twice a week were defined as “low PA” [21, 22]. Cronbach’s alpha for the FS in this study was 0.809, demonstrating good internal consistency. ## Physical disability as mediating variables The definition of PD included activities of daily living (ADL), instrumental activities of daily living (IADL), and strength and mobility. The Chinese versions of the ADL and IADL measures have been reported to have established validity and reliability [28, 29]. The range was from 0 to 17, with a higher score indicating more severe PD. Based on the published literature, we selected suitable replacement items from the original TLSA questionnaire to fit the data[20]. ADLs include bathing, dressing and undressing, eating, going to the toilet, moving in and out of bed, walking, and going to the toilet [24]. IADLs include buying personal supplies, traveling by car or by train, performing light housework, and dialing phone numbers [24]. Measurements of strength and mobility were used to identify disability in physical function, including standing for 15 min, squatting down, raising hands over head, carrying a load of 20 pounds, running 20–30 m; walking 200–300 m, and climbing a flight or two of stairs [24]. Cronbach’s alpha for the PD in this study was 0.897, demonstrating good internal consistency. ## Physical activity as mediating variables The PA score was based on the dose (> 30 min/day), frequency (days per week), and intensity (causing sweating or not) of exercise. The scores indicated three levels, with a higher level indicating more PA [30]. Based on the literature, the PA items from the original TLSA questionnaire were used to calculate the score [25]. Subjects who reported that they did no exercise were defined as “no PA”, and 1 point was given [25, 30]. Subjects who reported exercising 1 to 2 times a week were defined as “moderate PA” and 2 points were given [25, 30]. Those who reported exercising more than 3 times per week and for at least 30 min per session were defined as “high PA” and 3 points were given [25, 30]. On the one hand, no PA and moderate PA were investigated. On the other hand, the levels of physical activity were high enough to analyze whether the older adults met the WHO-specified level of PA. Cronbach’s alpha for the PA score in this study was 0.871, demonstrating good internal consistency. ## Middle-aged and older as moderation variables The stratification by age was based on the definition of the World Health Organization [31], and the population structure of older people in Asia [32], which sets 60 years old as the standard for middle-aged (50–59 years old) and older (≥ 60 years old). ## Life satisfaction as an outcome variable The items and dimensions on the short-form Life Satisfaction Index (LSI-SF) are based on the theory of LISA. The three dimensions are “Zest vs. Apathy,“ “Resolution and Fortitude,“ and “Congruence Between Desired and Achieved Goals.“ A total of 6 items are listed, and only item 2 is reverse coded. It is designed with dichotomies for each item, with scores ranging from 0 to 6 points. A higher score indicates higher LS. The LSI-SF is valid and reliable for measuring LS in older people. The internal consistency reliability of Cronbach’s alpha coefficients was 0.81. Its validity has been supported by findings of sufficient convergent and discriminant validity [33]. ## Statistical analysis In this study, big data analytics were used to test PD and PA as mediating variables in the relationship between FS and LS. First, descriptive statistical analysis was performed in SPSS for Windows (version 22.0; SPSS Inc., Chicago, IL, USA). The numbers, percentages, means, and standard deviations of the sociodemographic characteristics of the subjects and various variables are shown. Correlation analysis of variables using univariate linear regression was used to examine the associations among FS, PD, PA, and LS. The mediation analyses were performed in the PROCESS macro for SPSS developed by Hayes, using models 4, 6, and 15 to explore three different mediation patterns [34]. Model 4 (model as a parameter) in the PROCESS function was used for the parallel mediation model to explore how FS in middle-aged and older adults affects LS through PD and PA. In the current study, model 4 was chosen to test the mediating effect of FS on LS via PD or PA. Model 6 for the serial mediation model was used to explore three different relationships through the two mediators of PD and PA. In the current study, model 6 was chosen to test whether age stratification moderates the indirect effects of FS on LS via PD or PA. Model 15 (moderated mediation) for the moderated mediation model was used to explore the effects of frailty in PD and PA on LS that were moderated by age stratification. In the current study, model 15 was chosen to test the mediation of PD and PA in causal chains linked to a specific direction of flow and to test all combinations. In models 4, 6, and 15, we adjusted for age, gender, and the number of chronic diseases to account for carryover effects of LS [16, 17]. This study followed the suggestion by Hayes and Preacher to conduct analyses of different paths for independent variables. The lower limit (LL) to the upper limit (UL) in the $95\%$ confidence intervals (CIs) for the indirect effect (IE) did not include zero, indicating that the mediation was significant. ## Participant characteristics The study subjects included 2,155 males and 2,266 females. The majority of the subjects were older ($58.20\%$), had completed primary school ($43.3\%$), and lived with a spouse ($73.9\%$). There were significant differences in age, gender, education, and marital status among subjects in the non-frailty, pre-frailty, and frailty stages. In addition, the subjects in the frail stage had higher scores in PD (8.30 ± 4.55), largely had no PA ($3.9\%$), and had significantly lower LS (3.57 ± 1.79) (shown in Table 1). FS was positively correlated with PD ($r = 0.689$, $p \leq 0.01$) but negatively correlated with PA (r =- 0.367, $p \leq 0.01$). LS was negatively correlated with FS (r = -0.248, $p \leq 0.01$) and PD (r = -0.215, $p \leq 0.01$). Table 1Comparison of participant characteristics between non-frail groups, pre-frail groups, and frail groupsVariablesTotal($$n = 4421$$)Non-frail stage($$n = 2018$$)Pre-frail stage($$n = 2182$$)Frail stage($$n = 221$$)N (Mean)% (SD)N (Mean)% (SD)N (Mean)% (SD)N (Mean)% (SD)p / FAge< 0.001Middle-aged184941.8084218.6099422.60280.60Older257258.20119427.00118526.801934.40Gender< 0.001Male215548.70104123.50104823.70661.50Female226651.3097722.10113425.701553.50Education level< 0.001Primary school191343.3076517.3098922.401593.60Middle school78817.803407.704229.50260.60High school123928.0064714.6056212.70300.70University48110.902666.002094.7060.10Spousal status< 0.001With a spouse326873.90158335.80158235.801032.30Without a spouse115326.104359.8060013.601182.70PA< 0.001No164037.103768.50109024.701743.90Moderate66815.103006.803477.80210.50Highly211347.80134230.4074516.90260.60FS (0–5)(0.74)(0.87)2018 (0.00)45.60 (0.00)2182(1.15)49.40 (0.36)221(3.35)5.00 (0.61)< 0.001PD (0–17)(1.11)(2.49)(0.24)(0.69)(1.19)(2.03)(8.30)(4.55)< 0.001LS (0–6)(4.81)(1.49)(5.09)(1.32)(4.67)(1.53)(3.57)(1.79)< 0.001FS: frailty status; PA: physical activity; PD: physical disability; LS: life satisfaction ## Parallel mediation model A parallel mediation model was to explore the FS of middle-aged and older adults in LS through PD or PA. After adjustments for age, gender, and number of chronic diseases, the results showed that the effect of FS was negatively correlated with LS (βtotal = -0.417, SE = 0.026, $p \leq 0.001$). PD and PA had a mediating effect on FS with LS (shown in Fig. 1). Overall, PD and PA partially mediated the relationship between FS and LS (IEOVERALL = -0.196, $95\%$ CI: LL = -0.255 to UL = -0.139, indicating that middle-aged and older adults with increased FS may have higher levels of PD and lower levels of PA. Middle-aged and older adults with lower levels of PD and reduced levels of PA may have lower LS. The two mediating variables, PD and PA, were found to significantly contribute to the overall indirect effects. Specifically, there was a statistically significant indirect effect of FS on LS through PD (IEPD = -0.119, $95\%$ CI: LL = -0.170 to UL = -0.068). Therefore, middle-aged and older adults who experienced increased FS were more likely to feel PD, and through high levels of PD, they were more likely to report lower LS. In addition to this, there was a statistically significant indirect effect of FS on LS through PA (IEPA = -0.077, $95\%$ CI: LL = -0.101 to UL = -0.055). This showed that middle-aged and older adults who experienced increased FS were more likely to feel lower PA, and through reduced levels of PA, they were more likely to report lower LS. Fig. 1Parallel mediation model ($$n = 4$$,421)Standardised effects are presented. The Parallel mediation model was adjusted for age, gender, and the number of chronic diseases. Indirect effects of frailty status on life satisfaction through physical disability and physical activity. The effects on the direct path from frailty status to life satisfaction depict the direct effect and the total effect. $p \leq 0.01$, $p \leq 0.001$ ## Serial mediation model A serial mediation model was to examine the mediation of PD and PA in causal chains linked with a specific direction of flow and to test all combinations. After adjustments for age, gender, and the number of chronic diseases, the results showed that PD and PA in serial causal order mediated the relationship between FS and LS, and the ratio of the overall indirect effects to the total effect was 0.417 ($95\%$ CI: LL = -0.469 to UL = -0.365). The total indirect effects and the ratio of the total effect were the same as in the parallel mediation model described earlier. The positive and negative effects indicated the severity of FS, which led to increased PD and a decrease in PA. According to the results of Serial Mediation Model 1, the more severe the FS was, the more it would contribute to PD. A higher PD resulted in lower levels of PA, which in turn contributed to lower LS. According to the results of Serial Mediation Model 2, the more severe the FS was, the more it would contribute to PA. The lower levels of PA resulted in higher levels of PD, which in turn contributed to lower LS (shown in Fig. 2). Since two mediators of PD and PA were used, two different causal order models were produced. The two models were compared based on the significant paths created by each different causal order of the mediators. Serial Mediation Model 1 and Serial Mediation Model 2 yielded three significant paths, respectively. A total of six paths were statistically significant. Additionally, Serial Mediation Model 1 and Serial Mediation Model 2 yielded a significant indirect path involving both PD and PA as mediators in a causal chain, respectively (Path C). The indirect paths involving PD or PA, one after the other and vice versa, were statistically significant in all the serial mediation models. Path C in Serial Mediation Model 1 (FS → PD → PA → LS) had the highest ratio of indirect to total effect of all the models (IE = 0.013, $95\%$ CI: LL = 0.008 to UL = 0.019). This result indicated that severe FS increases PD, which in turn decreases PA, resulting in lower LS. Fig. 2Serial mediation model ($$n = 4$$,421)Evaluating physical disability and physical activity as mediators of the relationship between frailty status and life satisfaction, respectively. The serial mediation model was adjusted for age, gender, and the number of chronic diseases. The graph illustrates the effects of the direct paths linking frailty status to each mediator and among mediators resulting from the serial mediation model, in which all the direct and indirect effects are statistically significant. ** $P \leq 0.001$ ## Moderated mediation model: moderation for middle-aged and older A moderated mediation model was used to test whether the indirect effects of FS on LS through PD or PA are moderated by age stratification. The goal was to determine whether the previously observed mediation effect differed statistically significantly between subjects with middle-aged and older adults. The moderated mediation model was used to examine whether PD or PA acted as parallel mediators and whether stratification by middle and old age moderated the mediation. After adjustments for age, gender, and number of chronic diseases, the results showed statistically significant age-stratified interactions between FS (independent variable) and PD or PA (mediator variable), which were analyzed by simple linear regression. The moderation effect was observed on the mediator-dependent path (B path), and the direct path was independent of the dependent path (C path). The moderating effect was not observed in the independent variable to the mediating variable (A path). The slope of the indirect effects associated with the moderator variable was the index of moderated mediation (IMM). The statistical significance of the IMM effect was assessed along with the conditional indirect effects, which were assessed along with the stratification by middle-aged and older (shown in Fig. 3). Through the moderated mediation model, we found that the interaction effect between FS and stratification by age had predictive power to affect LS (β = -0.087, SE = 0.077, $$p \leq 0.254$$). The stratification by age significantly moderated the mediational effect of PA (IMM = -0.107, SE = 0.052, $p \leq 0.05$) but not the mediational effect of PD (IMM = 0.044, SE = 0.032, $$p \leq 0.167$$). This indicated a meaningful difference in the magnitude of the conditional indirect effects of each stratification by age in the mediation effect of FS on LS through PA. Specifically, from the analysis of direct effects and indirect effects, it was found that the two direct effects and all indirect effects were significant without including zero. We observed that for PA for subjects who were middle-aged, the conditional indirect effects were strong and statistically significant (β = -0.105, SE = 0.018, LL = -0.140, UL = -0.070), and the same was true for subjects who were older (β = -0.058, SE = 0.015, LL = -0.088, UL = -0.028). Therefore, PA was a significant mediator in the relationship between FS and LS in subjects who were middle-aged and older. However, there was no meaningful difference in the mediating effect of PD on the relationship between FS and LS in subjects who were middle-aged and older. Fig. 3Moderated mediation model ($$n = 4$$,421)Conditional indirect effects on stratification by age (middle-aged coded as 0 and older as 1) of frailty status on life satisfaction through physical disability and physical activity. Standardised effects are presented. The moderated mediation model was adjusted for age, gender, and the number of chronic diseases. The effects on the direct path from FS to LS depict the conditional direct effects for each stratification by age as well as the unconditional direct effect on the C’ path (total effect C-path). The effects of the moderator’s stratification by age on the paths represent the interaction slopes. The effects on the B-paths from the mediators to LS represent the simple slopes. * $p \leq 0.05$, $P \leq 0.01$, $P \leq 0.001$ ## Discussion People are ageing, not just as individuals or communities, but as a global population. While population ageing represents a triumph over disease, it also requires changing public health policy directions. Public health policy should not only focus on physiological directions but also ensure that psychological needs such as LS are met. The purpose of our study was to explore the co-occurrence and inter-relations between LS and physiological conditions such as FS, PD, and PA reported by middle-aged and older adults. We analyze the results related to physiological-related health problems in middle-aged and older adults with parallel mediation, serial mediation, and moderated mediation models to further clarify the research question. ## Geriatric frailty affects life satisfaction through physical disability and physical activity Previous studies have confirmed that FS [35, 36], PD [37], and PA [38] in middle-aged and older adults have significant impacts on LS. In this study, we specifically aimed to demonstrate that FS, PD, and PA can form a common health problem cluster over the course of the ageing process. Our findings also showed that LS was negatively associated with FS and PD in middle-aged and older adults but positively associated with PA. This means that both middle-aged and older adults need to pay attention to these physiologically related health issues, with an emphasis on preventing frailty and focusing on PD and PA levels. Furthermore, parallel mediation analysis showed that PD and PA partially mediated the relationship between FS and LS. The results demonstrated the mediating effect of the physiological changes on FS and LS. This result can help provide a comprehensive direction for elderly care and have a positive impact on LS in the psychological dimension for middle-aged and older adults. ## The mediating variables of physical disability and physical activity have specific causal associations The degrees of influence of ageing on life will depend on physiological changes and may also ignore the co-existence of physiological issues or the correlation between them. Much of the previous research has focused on strategies to reduce FS rather than management of the situations of physical incapacity or PA [39]. Although it is known that the advantages and disadvantages of physiological function can coexist with the severity of frailty [7], there are correlations with physiological states between PD and PA [40]. However, since there is no evidence of causal chains linked to interventions, health promotion programs may have imprecise designs in the future. The results of our study strongly indicate that, whether an individualised health promotion program is for middle-aged or older adults, it must be designed for the stratification of age. In addition, the content of the health promotion programs should not be limited to a single element. It should have multiple elements, including relieving PD and improving PA. A complete health promotion program can help improve the LS in frail middle-aged and older adults. ## The mediating effect between middle-aged and older adults Preceding studies reported a correlation between PD and PA or similar variables that have been explored for middle-aged and older adults [41]. However, the results of our study are based on FS. Although our study was not longitudinal in design, it also confirms that the relationship between physiological-related health problems and psychological-related LS should be considered in both frailty care and public health policy directions. For example, when distinguishing the middle-aged and older strata, stratification can significantly reduce PA and have a mediating effect, but it does not have a mediating effect on PD. These new findings explain research conflicts in preceding studies and may resolve the understanding of variable interactions. Before now, since the relationship between PD and PA has not been completely clear, systematic literature has pointed out that the combination of interventions such as physical training, nutritional supplements, cognitive training, health education, and home visits can improve the frailty of older people [42]. Past studies have suggested examining medications, social skills with nutrition [43], cognitive behavioral therapy with physical training [44], group exercise training, nutritional supplements, antidepressant medication or supportive psychotherapy, discontinuation of high-risk medications, and reduction of adverse home environments [45]. These composite interventions can effectively improve the PD of older people. In addition, the prevalence of insufficient physical activity has been found in more than half of middle-aged and older individuals. Their physical activity levels are lower than those recommended by WHO standards [11]. The extant research suggests that smart health care (eHealth) [46], PA monitors [47], or these measures in combination with telephone consultation [48] are effective in improving the PA status of older people. Overall, the combined interventions described above have shown efficacy in improving frailty, disability, and PA in older adults. Our study pointed out that FS will increase PD, which in turn affects PA, leading to a decrease in LS. This can be seen as evidence that the development of appropriate interventions in middle-aged and older adults can improve LS, and it is necessary to develop individualised compound intervention measures for the middle-aged or older, respectively. It is necessary to further explore whether a composite health promotion program can improve the FS of middle-aged and older adults, reduce PD, increase PA, improve LS, and strengthen the quality of elderly care. ## Limitations This study included a large, nationally representative sample of participants and adjusted for potential confounding factors. However, education level and spousal status [49, 50], which are related to frailty factors and may affect the relationship between FS and PD, PA, and LS, were not assessed. However, our mediation analyses were adjusted for age [51], gender [52], and chronic diseases [53], which are key factors in LS and FS in older adults. Furthermore, the causality of the effect of FS on LS remains unclear due to the cross-sectional research design. The development of frailty and its effect on LS appear to progress slowly in middle-aged and older adults. The relationship between PD and PA may also change over time. The interlinked nature of variables prevents any assertion of causality or direction. The length of follow-up is important in following LS in middle-aged and older adults. A longer follow-up period would tend to weaken the strength of association for any variable that continues to be associated with the outcome. Future research should include longitudinal studies to clarify the physiological changes caused by time and their impact on LS in middle-aged and older adults. ## Conclusion From the results of our study, FS, PD, and reduced PA often occur simultaneously in middle-aged and older adults. It provides evidence from two different samples of middle-aged or elderly populations. Our study also shows the direct and indirect effects of PD and PA on LS. The parallel mediation model showed that PD and PA can partially mediate the relationship between FS and LS in middle-aged and older adults. The serial mediation model showed that the severity of FS in causal chains is linked to increased PD, which in turn affects PA, leading to decreased LS to the extent that it mediates the relationship between FS and LS. The moderated mediation model revealed that different age stratifications can significantly moderate the mediating effect of PA but not that of PD. The findings of our study provide evidence for the assessment, prevention, and design of interventions for frailty, PD, and PA in middle-aged and older adults. Moreover, it is noted that PD and PA may have a causal relationship with the development of frailty. It may have a significant impact on LS in middle-aged and older adults. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1. Appendix 1. Pearson correlation coefficients between the variables ($$n = 4$$,421). Appendix 2. Models of the mediating role of physical disability and physical activity in the relationship between frailty status and life satisfaction ($$n = 4$$,421). Appendix 3. Standardised indirect effects for the paths on the SMMs. Appendix 4. Models of the moderation role of stratification by age (middle- and older) with a mediating role of physical disability and physical activity in the relationship between frailty status and life satisfaction ($$n = 4$$,421). Appendix 5. direct and mediating effects on the different levels of stratification by age ($$n = 4$$,421). ## References 1. Beard JR, Officer A, De Carvalho IA, Sadana R, Pot AM, Michel J-P. The World report on ageing and health: a policy framework for healthy ageing. The lancet* (2016.0) 387 2145-54. DOI: 10.1016/S0140-6736(15)00516-4 2. O’Caoimh R, Sezgin D, O’Donovan MR, Molloy DW, Clegg A, Rockwood K. Prevalence of frailty in 62 countries across the world: a systematic review and meta-analysis of population-level studies. 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--- title: 'The cisplatin-induced acute kidney injury is a novel risk factor for postoperative complications in patients with esophageal cancer: a retrospective cohort study' authors: - Shuhei Ueno - Miho Murashima - Ryo Ogawa - Masaki Saito - Sunao Ito - Shunsuke Hayakawa - Tomotaka Okubo - Hiroyuki Sagawa - Tatsuya Tanaka - Hiroki Takahashi - Yoichi Matsuo - Akira Mitsui - Masahiro Kimura - Takayuki Hamano - Shuji Takiguchi journal: BMC Surgery year: 2023 pmcid: PMC10044717 doi: 10.1186/s12893-023-01949-0 license: CC BY 4.0 --- # The cisplatin-induced acute kidney injury is a novel risk factor for postoperative complications in patients with esophageal cancer: a retrospective cohort study ## Abstract ### Background Cisplatin-induced acute kidney injury (AKI) is common during preoperative chemotherapy for esophageal cancer. The purpose of this study was to investigate the association between AKI after preoperative chemotherapy and postoperative complications in patients with esophageal cancer. ### Methods In this retrospective cohort study, we included patients who had received preoperative chemotherapy with cisplatin and underwent surgical resection for esophageal cancer under general anesthesia from January 2017 to February 2022 at an education hospital. A predictor was stage 2 or higher cisplatin-induced AKI (c-AKI) defined by the KDIGO criteria within 10 days after chemotherapy. Outcomes were postoperative complications and length of hospital stays. Associations between c-AKI and outcomes including postoperative complications and length of hospital stays were examined with logistic regression models. ### Results Among 101 subjects, 22 developed c-AKI with full recovery of the estimated glomerular filtration (eGFR) before surgery. Demographics were not significantly different between patients with and without c-AKI. Patients with c-AKI had significantly longer hospital stays than those without c-AKI [mean ($95\%$ confidence interval ($95\%$CI)) 27.6 days (23.3–31.9) and 43.8 days (26.5–61.2), respectively, mean difference ($95\%$CI) 16.2 days (4.4–28.1)]. Those with c-AKI had higher C-reactive protein (CRP) levels and prolonged weight gain after surgery and before the events of interest despite having comparable eGFR trajectories after surgery. c-AKI was significantly associated with anastomotic leakage and postoperative pneumonia [odds ratios ($95\%$CI) 4.14 (1.30–13.18) and 3.87 (1.35–11.0), respectively]. Propensity score adjustment and inverse probability weighing yielded similar results. Mediation analysis showed that a higher incidence of anastomotic leakage in patients with c-AKI was primarily mediated by CRP levels (mediation percentage $48\%$). ### Conclusion c-AKI after preoperative chemotherapy in esophageal cancer patients was significantly associated with the development of postoperative complications and led to a resultant longer hospital stay. Increased vascular permeability and tissue edema due to prolonged inflammation might explain the mechanisms for the higher incidence of postoperative complications. ## Introduction Transthoracic esophagectomy with preoperative chemotherapy is the standard treatment for advanced esophageal cancer in Japan [1]. Transthoracic esophagectomy is highly invasive, and complications, such as anastomotic leakage and postoperative pneumonia, frequently occur [2]. Risk factors for postoperative complications include older age, malnutrition, anemia, obesity, and underlying diseases such as cardiac disease and diabetes mellitus [3–5]. Postoperative complications prolong hospital stays, impair patients’ activities of daily living [6], and lead to poorer prognosis [7–9]. Preoperative chemotherapy for esophageal cancer usually includes cisplatin [1]. Cisplatin causes tubular damage and acute kidney injury (AKI) [10, 11]. Although AKI is a long-term risk factor for chronic kidney disease and death [12, 13], no clinical studies have examined the association between cisplatin-induced AKI (c-AKI) and postoperative complications. We hypothesized that c-AKI during chemotherapy is associated with postoperative complications and investigated this in a retrospective cohort study. ## Setting and patients In this single-center, retrospective observational study, we included adult patients (age ≥ 18 years old) with no significant physical function problems (performance status ≤ 2) who had received planned preoperative chemotherapy including cisplatin and underwent transthoracic esophagectomy for esophageal cancer under general anesthesia between January 2017 and February 2022 at Nagoya City University Hospital. Patients who had underwent mediastinoscopic surgery, and colon reconstruction were excluded, as different surgical invasiveness could impact postoperative complications. We also excluded those with missing values including serum albumin, urinary protein, estimated glomerular filtration rate (eGFR), hemoglobin, and C-reactive protein (CRP) levels. The observation period ended when the patient was discharged from the hospital after surgery. The study protocol was approved by the institutional review board at Nagoya City University Hospital (NO. 60-18-0008), and waiver of informed consent was approved by institutional review board (Nagoya City University Graduate School of Medical Sciences and Nagoya City University Hospital Institutional Review Board) due to the retrospective nature of the study. ## Operative procedure All included patients underwent thoracoscopic esophagectomy, and intravenous methylprednisolone (Solu-Medrol 250 mg, Pfizer) was given immediately before surgery. Laparoscopy was used for abdominal manipulations, while patients undergoing pharyngo-laryngoesophagectomy and those with severe adhesions underwent open surgery. ## Exposure of interest and outcomes The exposure of interest was c-AKI during preoperative chemotherapy. Outcomes were anastomotic leakage, postoperative pneumonia, and surgical site infection (SSI). ## Definitions c-AKI was defined as stage 2 or higher AKI after preoperative chemotherapy within 10 days after chemotherapy using the KDIGO criteria (increase in serum creatinine to 2.0–2.9 times baseline, or reduction in urine output to < 0.5 mL/kg/h for ≥ 12 h) [14]. All patients were hospitalized for preoperative chemotherapy, and during hospitalization, urine output was measured every 6 h. When daily urine output was < 1500 mL or weight gain was more than 3 kg after chemotherapy, 20 mg furosemide was administered intravenously. Anastomotic leakage was defined by imaging extraintestinal leakage on CT and fluoroscopy. Postoperative pneumonia was defined by the Uniform Pneumonia Score (van der Sluis et al. [ 15]) determined by body temperature, leucocyte count, and pulmonary radiographic findings. SSI was defined according to the guidelines of the Centers for Disease Control and Prevention [16]. Cancer staging was defined by the Japanese Classification of Esophageal Cancer, 11th Edition [17, 18]. Prechemotherapy and preoperative laboratory data were defined as those within 10 days before chemotherapy or surgery, and the closest to the date of chemotherapy or surgery, respectively. eGFR was calculated using the equation developed for the Japanese population by the Japanese Society of Nephrology [19]. ## Statistical analyses Data were presented as numbers (%) or medians (interquartile range). Demographic information for those with and without c-AKI was compared by Fisher’s exact test, the Mann–Whitney U test, or Wilcoxon signed-rank test. The length of hospital stay was compared between patients with and without c-AKI using Kaplan–Meier curves and log-rank tests. The trajectories of postoperative weight change, CRP levels, and eGFR were analyzed using a mixed-effects model, with time-dependent weight change, CRP, and eGFR levels as dependent variables and an interaction term between the cubic term of time and c-AKI as the independent variable. We included data up to the time before the event of interest or 20 postoperative days. The associations between c-AKI and postoperative complications (anastomotic leakage, postoperative pneumonia, and SSI) were analyzed using logistic regression models. The data were adjusted for the logic of the propensity score (PS) for c-AKI. The PS was derived from models including age, sex, body mass index, eGFR, hemoglobin, CRP, albumin, urinary protein, Brinkman Index, height-adjusted total kidney volume measured on CT, type of neoadjuvant chemotherapy, pharyngo-laryngoesophagectomy, comorbidities and medications as listed in Table 1. These clinical variables used to create the propensity score were selected from the factors that have been reported as risk factors for postoperative complications and AKI [3–5, 10, 11]. Further adjustments for time-averaged eGFR, albumin levels, body weight ratio, and CRP were performed. Time-averaged values for these variables were defined as the average of these variables before the event of interest or within 20 days postoperatively. Sensitivity analyses were performed with inverse probability weighting (IPW). Data with IPW values at less than the 5th percentile or more than the 95th percentile were excluded from the analyses. Table 1DemographicsWithout c-AKIn = 79With c-AKIn = 22P-valueAge72.0 (38.0–82.0)70.5 (55.0–79.0)0.70Male sex62 (78.8)21 (95.8)0.11Body mass index (kg/m2)20.3 (15.1–29.3)20.2 (17.0–29.0)0.64eGFR (mL/min/1.73 m2)74.7 (42.0–112.3)72.1 (38.2–119.9)0.87Serum creatinine (mg/dL)0.78 (0.63–0.89)0.72 (0.64–0.87)0.97Performance status0.82 069 (87.3)19 (86.3) 18 (10.1)3 (13.6) 22 (2.5)0 [0]Hemoglobin (g/dL)13.3 (9.3–17.2)13.9 (10.9–15.8)0.16C-reactive protein (mg/dL)0.14 (0.03–6.77)0.20 (0.03–2.88)0.86Albumin (g/dL)3.9 (2.7–4.9)3.9 (2.9–4.6)0.36Urinary protein (+) or more6 (7.6)2 (9.1)1.00Brinkman Index700 (0–85,260)820 (0–2400)0.70HtTKV (mL/m2)104.3 (76.4–160.6)109.1 (73.5–151.5)0.75Preoperative chemotherapy; DCF/FP$\frac{45}{3416}$/60.22Pharyngo-laryngoesophagectomy6 (7.6)2 (9.1)1.00Stage of esophageal cancer0.86 02 (2.5)0 [0] 17 (8.9)2 (9.1) 225 (31.6)6 (27.3) 337 (46.8)10 (45.5) 48 (10.1)4 (18.2)History of cerebrovascular accidents2 (2.5)1 (4.5)0.53History of diabetes mellitus7 (8.9)2 (9.1)1.00History of heart failure1 (1.3)1 (4.5)0.39History of hypertension35 (44.3)8 (36.4)0.63History of COPD18 (22.8)8 (36.4)0.27ACE inhibitors0 [0]0 [0]NAAntiplatelet agents6 (7.6)4 (18.2)0.22ARBs21 (26.6)6 (27.3)1.00Diuretics1 (1.3)0 [0]1.00Insulin0 [0]0 [0]NANSAIDs0 [0]0 [0]NASGLT2 inhibitors1 (1.3)0 [0]1.00Other anti-diabetic medications5 (6.3)2 (9.1)0.64Statins5 (6.3)4 (18.2)0.10Data were collected before chemotherapy. Data were shown as number (%) or median (interquartile range) as appropriate. P-values were calculated by Fisher’s exact test or Mann–Whitney U testc-AKI cisplatin-induced acute kidney injury, eGFR estimated glomerular filtration rate, HtTKV height-adjusted total kidney volume (total kidney volume divided by height square), DCF Docetaxel + Cisplatin + 5 fluorouracil, FP Cisplatin + 5 fluorouracil, COPD chronic obstructive pulmonary disease, ACE angiotensin-converting enzymes, ARB angiotensin receptor blockers, NSAIDs non-steroidal anti-inflammatory drugs, SGLT2 sodium-glucose cotransporters 2 We used mediation analysis to examine the indirect effect mediating the relationship between c-AKI and postoperative complications. The indirect effect is calculated as a comparison between the total effect of the exposure (AKI) and the effect of the exposure adjusted for intermediate variables. For the calculation of the mediation percentage, the indirect effect was then divided by the total effect of the outcome [20]. Mediation percentage was defined as;\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\text{Mediation percentage}} = {{\left[{100\left({{\text{OR}}1 - {\text{OR}}2} \right)} \right]} \mathord{\left/ {\vphantom {{\left[{100\left({{\text{OR}}1 - {\text{OR}}2} \right)} \right]} {\left({{\text{OR}}1 - 1} \right)}}} \right. \kern-0pt} {\left({{\text{OR}}1 - 1} \right)}},$$\end{document}Mediation percentage=100∗OR1-OR2/OR1-1,where OR1: odds ratio of c-AKI for anastomotic leakage (data adjusted for the logit of PS), OR2: odds ratio of c-AKI for anastomotic leakage (data adjusted for the logit of PS and covariates of interest). A 1000 bootstrap resampling procedure was used to estimate the $95\%$ confidence intervals ($95\%$CI). P-values < 0.05 were considered statistically significant. The incidence of anastomotic leakage and postoperative pneumonia after esophageal cancer surgery has been reported to be about $10\%$ [2], and the incidence of cisplatin-induced AKI was about $30\%$ [10]. We considered that it would be a clinically meaningful difference if $33\%$ (one-third) of patients with cAKI develop these postoperative complications. A sample size of 101 gave a post hoc power of 0.73 at a significance level of 0.05. Statistical analyses were performed using STATA MP v.17.1 (Stata Corp., College Station, TX). ## Results During the study period, 107 subjects received preoperative chemotherapy with cisplatin and underwent transthoracic esophagectomy at Nagoya City University Hospital. After applying the exclusion criteria, data from 101 subjects were available for analysis (Fig. 1).Fig. 1Flow of patients Among the 101 patients, 22 ($21.8\%$) developed stage 2 or higher AKI. All patients with AKI met the diagnostic criteria by decreased urine output, while only one patient met the criteria by elevated serum creatinine. All patients were diagnosed with AKI before surgery [median (range) period from AKI diagnosis to surgery was 31 (16–59) days]. There was no patient with AKI due to sepsis or use of contrast media within 72 h before the onset of AKI. Demographics were not significantly different between patients with and without c-AKI (Table 1). Changes in eGFR, urinary volume and body weight among those with and without c-AKI are shown in Fig. 2. The lowest urine volume after chemotherapy was significantly lower in those with c-AKI. The lowest eGFR was significantly lower after chemotherapy than before chemotherapy for those with and without c-AKI, even after adjusting for multiple comparisons via the Bonferroni correction. However, there was no significant difference between those with and without c-AKI in the lowest eGFR, preoperative eGFR, or preoperative body weight (Fig. 2). Despite no significant difference in demographics and eGFR trajectories before surgery, the length of hospital stay for surgery was significantly longer for patients with c-AKI than for those without c-AKI [mean difference ($95\%$CI) 16.2 (4.4–28.1) days] (Fig. 3). The duration of operation and intraoperative bleeding between patients with and without c-AKI were not significantly different [operation duration; 561 (527–683) min, 573 (519–632) min, $$P \leq 0.99$$, and intraoperative bleeding; 101 (74–207) mL, 121 (70–200) mL, $$P \leq 0.86$$, respectively].Fig. 2Changes in eGFR, urinary volume, and body weight for those with and without c-AKI. eGFR estimated glomerular filtration rate, c-AKI cisplatin-induced acute kidney injury. P-values were calculated by Mann–Whitney U test or Wilcoxon signed-rank test as appropriate. *** denotes statistical significance after Bonferroni correctionFig. 3Cumulative incidence of hospital discharge for those with and without c-AKI. c-AKI cisplatin-induced acute kidney injury Compared with those without c-AKI, patients with c-AKI had significantly higher CRP levels and body weight ratio (BW ratio) over time after surgery, whereas time-dependent eGFR levels did not differ between the two groups (Fig. 4).Fig. 4The trajectories of postoperative CRP, body weight ratio (postoperative body weight/preoperative body weight), and eGFR. The data were analyzed by the mixed-effects models with time-dependent CRP levels, body weight ratio, or eGFR as a dependent variable and including an interaction term between the cubic term of time and c-AKI as an independent variable. CRP C-reactive protein, eGFR estimated glomerular filtration rate, c-AKI cisplatin-induced acute kidney injury Compared with those without c-AKI, the incidence of anastomotic leakage and postoperative pneumonia was significantly higher among those with c-AKI, whereas the incidence of SSI was not significantly different (Table 2). c-AKI was significantly associated with anastomotic leakage and postoperative pneumonia (Table 3). The association between c-AKI and anastomotic leakage did not change substantially after adjustment for the logit of the PS, or when analyzed by IPW. Postoperative pneumonia was no longer significantly associated with c-AKI after adjustment for the logit of the PS. However, the odds ratio remained greater than 2, suggesting a potentially important relationship. c-AKI was not associated with SSI in any of the analyses. The association between c-AKI and anastomotic leakage was attenuated by adjustment for time-averaged eGFR, albumin, body weight change, and CRP levels after surgery and before the event of interest, suggesting that these variables were potential mediators of the association. The association between c-AKI and postoperative pneumonia was also attenuated by adjustment for time-averaged albumin and CRP levels. The association between c-AKI and SSI was not affected by adjustment for these variables (Table 3).Table 2The incidence of postoperative complications within 20 days after surgeryWithout c-AKIn = 79With c-AKIn = 22P-valueAnastomotic leakage8 (10.1)8 (36.4)0.01Postoperative pneumonia12 (15.2)11 (50.0)0.003Surgical site infection13 (16.5)4 (18.2)1.00Data were shown as numbers (%). P-value was calculated by Fisher’s exact testc-AKI cisplatin-induced acute kidney injuryTable 3Association between c-AKI and postoperative complicationsAnastomotic leakageOR ($95\%$ CI)Postoperative pneumoniaOR ($95\%$ CI)Surgical site infectionOR ($95\%$ CI)Univariate analysis4.14 (1.30–13.18)3.87 (1.35–11.0)0.80 (0.21–3.11)PS adjustment5.78 (1.50–22.12)3.87 (1.35–11.03)1.71 (0.38–7.70)Inverse probability weighting#5.77 (1.42–23.51)2.23 (0.67–7.33)2.00 (0.02–1.72)PS adjustment with stepwise adjustment for potential mediators Model 1; PS adjustment5.78 (1.50–22.12)3.87 (1.35–11.03)1.71 (0.38–7.70) Model 25.00 (1.27–19.66)2.27 (0.67–7.72)1.90 (0.41–8.71) Model 34.28 (1.06–17.31)1.77 (0.50–6.32)1.77 (0.38–8.33) Model 43.86 (0.92–16.11)2.17 (0.57–8.26)1.61 (0.34–7.77) Model 52.45 (0.50–11.95)1.84 (0.48–7.12)1.80 (0.36–8.99)PS derived from models including age, sex, body mass index, estimated glomerular filtration rate, hemoglobin, C-reactive protein, albumin, urinary protein, Brinkman Index, chronic obstructive pulmonary disease, height-adjusted total kidney volume, kinds of preoperative chemotherapy, pharyngo-laryngoesophagectomy, comorbidities, and medications listed in Table 1Model 1: The data were adjusted for the logit of propensity scoreModel 2: adjusted for variables in Model 1 and time-averaged estimated glomerular filtration rate before the event of interest or within 20 days postoperativelyModel 3: adjusted for variables in Model 2 and time-averaged albumin levels before the event of interest or within 20 days postoperativelyModel 4: adjusted for variables in Model 3 and time-averaged body weight ratio (postoperative body weight/preoperative body weight) before the event of interest or within 20 days postoperativelyModel 5: adjusted for variables in Model 4 and time-averaged C-reactive protein (log-transformed) levels before the event of interest or within 20 days postoperativelyc-AKI cisplatin-induced acute kidney injury, PS Propensity scoreData were adjusted for the logit of the propensity score#Data with inverse probability weighting values less than 5 percentile or more than 95 percentiles were excluded, which left 89 cases in the analyses Mediation analysis* showed that body weight changes and CRP levels mediated the association between c-AKI and anastomotic leakage by $17\%$ and $48\%$, respectively. The mediation percent did not change when the model include both CRP and body weight change. Scatter plots for an average of CRP and BW ratio show a significant association between CRP and BW ratio (Fig. 5).Fig. 5Mediation by body weight ratio (postoperative body weight/preoperative body weight), C-reactive protein, or eGFR for the association between c-AKI and anastomotic leakage. Scatter plots for the average C-reactive protein (natural log-transformed) level and the average body weight ratio. The regression line was shown in the scatter plot. Mediation percentage was defined as [100(OR1–OR2)]/(OR–1). OR1: odds ratio of c-AKI for anastomotic leakage (Data adjusted for the logit of propensity score). OR2: odds ratio of c-AKI for anastomotic leakage (Data adjusted for the logit of propensity score and covariates indicated in the graph). c-AKI cisplatin-induced acute kidney injury, lnCRP C-reactive protein (natural log-transformed), CRP C-reactive protein, eGFR estimated glomerular filtration rate ## Discussion This study showed that c-AKI diagnosed only by urine output criteria after chemotherapy was associated with postoperative complications, especially anastomotic leakage, leading to a significantly longer hospital stay. The association was mainly mediated by higher CRP after surgery. This is the first study to show that c-AKI during preoperative chemotherapy was associated with postoperative complications. As shown in Table 1, there were no significant differences in known risk factors for postoperative complications between patients with and without c-AKI. After adjusting for these known risk factors, c-AKI remained independently associated with anastomotic leakage. From these two viewpoints, c-AKI is a novel risk factor for postoperative complications in patients with esophageal cancer. Numerous observational studies have shown that AKI was independently associated with worse prognoses including all-cause mortality, cardiovascular morbidity, and end-stage kidney disease [12, 21, 22]. However, baseline characteristics in these studies were significantly different between those with and without AKI. As a result, the association between AKI and worse prognosis could be due to residual confounders. Our study was peculiar because the background of those with and without c-AKI was not significantly different. Therefore, our results strengthened the argument for an independent association between c-AKI and postoperative complications. Our study was also unique since most patients with c-AKI were diagnosed only by urine output criteria, which could be overlooked in real clinical practice. An evaluation of kidney function using serum creatinine level may be insufficient. Patients with c-AKI may have undergone surgery before the complete recovery of their tubular function. It would have been beneficial to measure clinical biomarkers for tubular damage, which might allow us to infer an appropriate surgical timing. Otherwise, preoperative chemotherapy with cisplatin might have acted as a stress test for the kidneys to evaluate the poor functional reserve that cannot be determined by existing renal evaluations. A stricter in–out balance may be required during perioperative management for patients with c-AKI. Therefore, the clinical application of biomarkers as indicators of perioperative fluid delivery and balance is also required [23, 24]. The underlying mechanism of the association between c-AKI and postoperative complications could be prolonged inflammation after AKI. We also demonstrated that the time-averaged BW ratio was associated with time-averaged CRP level after surgery, suggesting that those with prolonged elevated CRP levels had prolonged fluid retention. In recent years, kidney tubules have been reported to play a role in the suppression of inflammation [25, 26]. For example, kidney injury molecule 1 expression is anti-inflammatory due to its mediation of the phagocytotic process in tubules [27]. Additionally, in a clinical study, prolonged inflammation was suggested to be a mediator of worse outcomes after AKI [28]. The results of the current study suggest that the kidneys after c-AKI did not suppress the postoperative inflammatory response and that increased vascular permeability due to proinflammatory cytokines caused prolonged weight gain with systemic or local edema, leading to anastomosis leakage and postoperative pneumonia. Niebauer et al. reported that in patients with edema due to congestion, intestinal edema causes increased intestinal permeability, leading to bacterial translocation, which, in turn, leads to a prolonged inflammatory response and more severe edema, resulting in a vicious cycle. If a similar mechanism is observed in patients with c-AKI, diuretic use with body weight as an indication may help reduce the occurrence of postoperative complications [29]. This study has several limitations including its retrospective design and the small number of patients at a single center. Although the sample size was relatively small, our results showed that the incidence of anastomotic leakage among patients with and without cAKI and the incidence of cAKI were $10.1\%$, $36.4\%$, and $21.8\%$, respectively, which were not substantially deviated from our assumptions for power calculation. Due to the small number of patients, we were unable to examine the relationship between AKI severity (AKI stage) after preoperative chemotherapy and postoperative complications. ## Conclusion Cisplatin-induced AKI after preoperative chemotherapy in esophageal cancer patients was significantly associated with the development of postoperative complications and led to a resultant longer hospital stay. This association was mainly mediated by higher CRP level in those with c-AKI. Increased vascular permeability and tissue edema might explain the mechanisms for the higher incidence of postoperative complications. Conservative fluid management and liberal use of diuretics or albumin might be warranted for those undergoing surgery for esophageal cancer with a history of c-AKI. ## References 1. Kuwano H, Nishimura Y, Oyama T, Kato H, Kitagawa Y, Kusano M. Guidelines for diagnosis and treatment of carcinoma of the esophagus April 2012 edited by the Japan Esophageal Society. 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--- title: The Extracellular Matrix Vitalizer RATM Increased Skin Elasticity by Modulating Mitochondrial Function in Aged Animal Skin authors: - Kyung-A Byun - Seyeon Oh - Sosorburam Batsukh - Min Jeong Kim - Je Hyuk Lee - Hyun Jun Park - Moon Suk Chung - Kuk Hui Son - Kyunghee Byun journal: Antioxidants year: 2023 pmcid: PMC10044720 doi: 10.3390/antiox12030694 license: CC BY 4.0 --- # The Extracellular Matrix Vitalizer RATM Increased Skin Elasticity by Modulating Mitochondrial Function in Aged Animal Skin ## Abstract Oxidative stress-induced cellular senescence and mitochondrial dysfunction result in skin aging by increasing ECM levels-degrading proteins such as MMPs, and decreasing collagen synthesis. MMPs also destroy the basement membrane, which is involved in skin elasticity. The extracellular matrix vitalizer RATM (RA) contains various antioxidants and sodium hyaluronate, which lead to skin rejuvenation. We evaluated whether RA decreases oxidative stress and mitochondrial dysfunction, eventually increasing skin elasticity in aged animals. Oxidative stress was assessed by assaying NADPH oxidase activity, which is involved in ROS generation, and the expression of SOD, which removes ROS. NADPH oxidase activity was increased in aged skin and decreased by RA injection. SOD expression was decreased in aged skin and increased by RA injection. Damage to mitochondrial DNA and mitochondrial fusion markers was increased in aged skin and decreased by RA. The levels of mitochondrial biogenesis markers and fission markers were decreased in aged skin and increased by RA. The levels of NF-κB/AP-1 and MMP$\frac{1}{2}$/$\frac{3}{9}$ were increased in aged skin and decreased by RA. The levels of TGF-β, CTGF, and collagen I/III were decreased in aged skin and increased by RA. The expression of laminin and nidogen and basement membrane density were decreased in aged skin and increased by RA. RA increased collagen fiber accumulation and elasticity in aged skin. In conclusion, RA improves skin rejuvenation by decreasing oxidative stress and mitochondrial dysfunction in aged skin. ## 1. Introduction The primary triggering factor of skin aging is oxidative cellular damage caused by increased oxidative stress [1,2]. Oxidative stress results from an imbalance between reactive oxygen species (ROS) synthesis and defense mechanisms that remove ROS [1,2]. Enzymes that remove ROS, such as glutathione (GSH), superoxide dismutase (SOD), and catalase, are representative of defense mechanisms against oxidative stress [3]. During the skin aging process, oxidative stress leads to the upregulation of nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB) and its downstream signal pathway of activator protein 1 (AP-1), which eventually increases the levels of extracellular matrix (ECM)-degrading proteins such as collagenase and matrix metalloproteinases (MMPs) [4,5,6]. ROS also decreases collagen synthesis by downregulating the transforming growth factor-β (TGF-β) pathway [7]. In addition, ROS downregulates type II TGF-β receptor and its downstream signaling pathways of mothers against decapentaplegic homolog 3 and connective tissue growth factor (CTGF), eventually leading to decreased synthesis of type I collagen [7]. Increased ROS levels damage mitochondrial DNA (mtDNA), which leads to mitochondrial dysfunction [8,9,10]. Increased oxidative stress induced by H2O2 treatment in dermal fibroblasts leads to decreased levels of peroxisome proliferator-activated receptor γ coactivator 1 α (PGC-1α), which is a significant modulator of mitochondrial biogenesis [11]. Moreover, the PGC-1α level decreases with aging in various tissues [12]. Damage to mtDNA in human keratinocytes caused by UV radiation was accompanied by increased levels of collagen degradation enzymes and ROS synthesis, which aggravated mitochondrial dysfunction [13,14,15]. Mitochondrial dynamics alter mitochondrial morphology via fusion and fission and are essential for maintaining mitochondrial number and shape [16]. Mitofusin (MFN) 1 and 2 and optic atrophy protein 1 (OPA1) are required for mitochondrial fusion, which provides a trade-off between damaged mtDNA and healthy mtDNA [16]. Mitochondrial fission, performed by dynamin-related protein 1 (DRP1) or mitochondrial fission 1 protein (FIS1), leads to the formation of new mitochondria [16]. During cellular senescence, mitochondrial fission is decreased, which leads to mitochondrial elongation [17,18]. Increased oxidative stress decreases the levels of fission proteins such as FIS1 [17]. Oxidative stress induced by UV radiation resulted in increased levels of fusion proteins such as OPA1 and MFN2 and decreased levels of DRP1 [19]. Mitochondrial dysfunction leads to dermal fibroblast and keratinocyte senescence, which causes skin wrinkle formation via ECM changes [20,21,22,23,24,25]. The epidermal basement membrane (BM) in the dermal-epidermal junction has a sheet-like structure and acts as a binder between the dermis and the epidermis [26]. The major proteins of the BM are type IV and type VII collagens, nidogen, laminins, and perlecan [26]. Since the MMPs destroy the BM, radiation causes BM degradation [27,28]. Moreover, the BM is thinned, accompanied by decreased BM protein gene expression during aging [29,30]. The expression of a structural protein, collagen IV, is reduced in aged skin or senescent fibroblasts [31]. In contrast, laminin or nidogen-stimulating peptide complexes increased dermal collagen XVII and laminin levels in excised human skin and decreased wrinkles in Asian females [32]. Various effective compounds, such as vitamins, amino acids, hyaluronic acid, and minerals, have been used to rejuvenate aged skin. Since ascorbic acid (AA, vitamin C), a well-known antioxidant, is an essential factor for the synthesis of DNA and collagen, it has been frequently used for skin rejuvenation [33,34]. Niacinamide (NA, vitamin B3) is a precursor of nicotinamide adenine dinucleotide [35], which is a redox cofactor [12,36]. Since NA shows antioxidative and anti-inflammatory effects, it has also been used for skin rejuvenation [37]. Glutathione is a powerful antioxidant and decreases melasma when used as a mesotherapy [38]. Since hyaluronic acid can hold 1000 times its own weight of water, it is beneficial for skin hydration [39]. Moreover, hyaluronic acid showed anti-inflammatory and antioxidant effects [40,41]. In aged skin, dermal fibroblasts show decreased synthesis of hyaluronic acid [42]. Hyaluronic acid complex with various vitamins increased dermal fibroblast proliferation [42]. The extracellular matrix vitalizer RATM (RA, illglobal, Seoul, Republic of Korea) contains various antioxidants, such as AA, NA, coenzymes, glutathione, and sodium hyaluronate. Thus, we hypothesized that RA injection could decrease oxidative stress and cellular senescence, which eventually resulted in a reduction in NF-κB/AP-1 and mitochondrial dysfunction in the skin. These reductions led to decreased levels of MMPs, which eventually decreased ECM fiber destruction and BM destruction. RA also increased the levels of TGF-β and CTGF, which eventually increased collagen fiber synthesis. We evaluated the RA-mediated increase in collagen fiber accumulation and decrease BM destruction via decreased oxidative stress in aged animals. The effects of RA were compared with those of AA or NA injected alone into aged animal’s skin. ## 2.1. Preparation of RA RA was formulated as a liquid before application. First, AA, NA, coenzymes, glutathione, and sodium hyaluronate were dissolved in distilled water with mixing at 3000 rpm using a high-speed mixer (T.K. Homo Disper, Model 2.5, PRIMIX, Hyogo, Japan). Then, the RA solution was filtered through a 0.2 μm filter (S2GPU11RE, Merck, Darmstadt, Land Hessen, Germany) to remove bacteria. The RA liquid contained $0.25\%$ AA and $0.25\%$ NA (Table S1). ## 2.2.1. Cell Culture Human primary epidermal keratinocytes (HEKn; ATCC, Manassas, VA, USA) were cultivated with dermal cell basal medium (ATCC) with a keratinocyte growth kit (ATCC) and maintained at 37 °C under $5\%$ CO2. ## 2.2.2. NA, AA, and RA Treatment To determine whether keratinocytes were affected by NA, AA, or RA treatment, HEKn cells were treated with 50 μM H2O2 for 2 h, treated with NA (0.4 mM), AA (0.4 mM), or RA (80 μL), and cultured for 48 h (Figure S1A). In addition, control cells were treated with phosphate-buffered saline (PBS). ## 2.3.1. Mouse Conditions Eight-week-old male C57BL/6 mice were obtained from Orient Bio (Seongnam, Korea). The young group contained 9-week-old mice after one week of acclimatization, and the aging group was bred until 12 months old. This study was approved by the ethical board of the Center for Animal Care and Use. It was conducted by the guidelines of the Institutional Animal Care and Use Committee of Gachon University (approval number: LCDI-2022-0095). The mice used in this study were domesticated in an area with controlled temperature (22 ± 5 °C), relative humidity (50 ± $10\%$), and a 12-h light-dark cycle. In addition, they had free access to standard laboratory diets and water. ## 2.3.2. RA Treatment To determine whether aged animal skin was affected by RA treatment, 12-month-old aging mice were injected intradermally with RA (100 μL/cm2/day) twice every two weeks using a microneedle therapy system (MTS; Derma-Q Gold 0.5 mm, DONGBANG medicare, Seongnam, Korea). The control was injected with distilled water under the same conditions (Figure S1B). ## 2.3.3. Skin Elasticity To confirm whether the skin elasticity of aged animal skin was changed by RA treatment, the skin elasticity of the animal before RA treatment and after 4 weeks was measured. Skin elasticity was evaluated with API-100® (Aram Huvis Co., Ltd., Seongnam, Republic of Korea), and the average was used after measuring 5 times for each animal. ## 2.4.1. Protein Isolation Proteins were isolated from the cells and skin tissues by using the EzRIPA lysis kit (ATTO Corporation, Tokyo, Japan). First, the cells and skin tissues were lysed with EzRIPA buffer containing protease and phosphatase inhibitors. Then, the lysed samples were sonicated and centrifuged at 14,000× g for 20 min at 4 °C. Then, the supernatants were transferred to a new tube, and the protein was quantified by using a bicinchoninic acid assay kit (Thermo Fisher Scientific, Waltham, MA, USA). ## 2.4.2. RNA Extraction and cDNA Synthesis The total RNA from cells and frozen skin tissues was extracted using RNAiso Plus (Takara Bio, Kusatsu, Japan) according to the manufacturer’s instructions. The quality and concentration of the extracted RNA were confirmed by a Nanodrop 2000 spectrophotometer (Thermo Fisher Scientific), and cDNA was synthesized by using a PrimeScript First Strand cDNA Synthesis Kit (Takara Bio) according to the manufacturer’s instructions. ## 2.4.3. Paraffin-Embedded Tissue The harvested skin tissues were fixed with cold $4\%$ paraformaldehyde (Sigma-Aldrich, St. Louis, MO, USA) in PBS at 4 °C for 24 h. The fixed skin tissues were washed for 30 min, and a paraffin block was made using a tissue processor (Thermo Fisher Scientific). The paraffin blocks were sectioned to 7 µm in thickness using a microtome (Leica Biosystems, Nussloch, Germany) and dried at 60 °C overnight to allow them to attach to the slides. ## 2.5. Nicotinamide Adenine Dinucleotide Phosphate (NADPH) Oxidase and SOD Activity NADPH oxidase (Abcam, Cambridge, UK) and SOD (Abcam) activities in the H2O2-treated HEKn cells and skin tissue of each group were determined by using appropriate kits, following the manufacturers’ instructions. ## 2.6. Enzyme-Linked Immunosorbent Assay (ELISA) To measure the levels of the 8-hydroxy-2′-deoxyguanosine (8-OHdG), Collagen type I alpha 1 (COL1A1), and Collagen type III alpha 1 (COL3A1), 96-well microplates were coated in 100 nM carbonate and bicarbonate-mixed buffer, adjusted to pH 9.6 and incubated overnight at 4 °C. The microplates were then washed with PBS containing $0.1\%$ Tween 20 (TPBS). The remaining protein-binding sites were blocked using $5\%$ skim milk for 6 h at room temperature. After washing with PBS, 30 μg of protein samples were distributed into each well and incubated overnight at 4 °C. Each well was rinsed with TPBS and then incubated with primary antibodies diluted in PBS overnight at 4 °C (Table S2). After washing, peroxidase-conjugated secondary antibodies (Vector Laboratories, Newark, CA, USA) was loaded for 4 h at room temperature. Tetramethylbenzidine solution was added, followed by incubation for 15–20 min at room temperature. The stop solution that was used was 2 N sulfuric acid. The optical density was measured at a wavelength of 450 nm using a microplate reader (Molecular Devices, San Jose, CA, USA). ## 2.7. Western Blotting Equal amounts of proteins were separated on 8–$12\%$ polyacrylamide gels and transferred to polyvinylidene fluoride membranes (Millipore, Burlington, Massachusetts, USA) by a power station (ATTO). After blocking using $5\%$ skim milk and washing with Tris-buffered saline containing $0.1\%$ Tween 20 (TTBS), we incubated the membranes with primary antibodies (Table S2) for 15 h at 4 °C and then washed them with TTBS. Next, the membranes were incubated with peroxidase-conjugated secondary antibodies (Vector Laboratories) at room temperature for 1 h and rinsed with TTBS. Subsequently, an enhanced chemiluminescence detection reagent (CytivaTM, Seoul, Korea) and imaging system (ChemiDoc; Bio-rad, Hercules, CA, USA) were used to visualize the immunoreactive proteins on the membrane. ## 2.8. Quantitative Real-Time Polymerase Chain Reaction (qRT–PCR) The qRT–PCR reagent mixture was prepared by mixing 1 µg of synthesized cDNA, SYBR Green reagent (Takara), and 10 pmol of primer (Table S3). This mixture was added to a 384-well multi-plate and analyzed with a CFX386 Touch Real-Time PCR System (Bio-Rad). ## 2.9. Immunohistochemistry The sectioned slides were passed through a series of xylene and ethanol solutions ($100\%$, $90\%$, $80\%$, $70\%$) to remove the paraffin and then hydrated with distilled water and PBS. In order to reduce nonspecific binding, the slides were incubated with normal serum. The blocked slides were incubated with primary antibody (Table S2) for 15 h at 4 °C and 1 h at room temperature. The slides were then rinsed with PBS and incubated with biotinylated secondary antibodies (Vector Laboratories) for 1 h at room temperature. The slides were again rinsed with PBS and then incubated with ABC reagent (Vector Laboratories) according to the manufacturer’s instructions. After washing with PBS, the slides were developed for 5 min using a 3,3′-diaminobenzidine tetrahydrochloride hydrate (DAB; Sigma-Aldrich). Then, the slides were washed with PBS, followed by distilled water, and counterstained with hematoxylin solution (DAKO, Glostrup Kommune, Denmark). After the slides were washed, they were dehydrated with absolute alcohol and mounted using xylene and dibutyl phthalate in xylene (DPX; Sigma-Aldrich). Images of the stained slides were acquired with an optical microscope (Olympus Optical Co., Tokyo, Japan), and the intensity was analyzed using ImageJ software (NIH, Bethesda, MD, USA). ## 2.10.1. Periodic Acid-Schiff (PAS) Staining After deparaffinization, skin tissues were incubated in $0.5\%$ periodic acid (BBC Biochemical, McKinney, TX, USA) for 5 min and rinsed with distilled water. Then, they were incubated in Schiff’s reagent for 15 min and rinsed with running tap water. After they were incubated with hematoxylin for 2 min, the samples were rinsed with distilled water, dehydrated, mounted with DPX mount solution (Sigma-Aldrich), and observed under an optical microscope (Olympus Optical Co.) equipped with a slide scanner (Motic, Vancouver, British Columbia, Canada). All images were analyzed for collagen fiber density using ImageJ software (NIH). ## 2.10.2. Masson’s Trichrome Staining After deparaffinization, skin tissues were incubated in Bouin solution (Scytek Laboratories, West Logan, UT, USA) at 60 °C for 1 h and rinsed with distilled water. The sections were then placed in a working solution of iron hematoxylin (Scytek Laboratories) for 5 min, Biebrich scarlet acid fuchsin solution (Scytek Laboratories) for 5 min, phosphomolybdic-phosphotungstic acid solution (Scytek Laboratories) for 12 min, and aniline blue solution (Scytek Laboratories) for 3 min. The stained slides were mounted with DPX mount solution (Sigma-Aldrich) and observed under an optical microscope (Olympus Optical Co.) equipped with a slide scanner (Motic). All images were analyzed for collagen fiber density using ImageJ software (NIH). ## 2.11. Transmission Electron Microscopy (TEM) Specimens were fixed for 12 h in $2\%$ glutaraldehyde/$2\%$ paraformaldehyde in 0.1 M phosphate buffer (pH 7.4) and washed in 0.1 M phosphate buffer, postfixed with $1\%$ OsO4 in 0.1 M phosphate buffer for 2 h, dehydrated with an ascending ethanol series ($50\%$, $60\%$, $70\%$, $80\%$, $90\%$, $95\%$, $100\%$, and $100\%$) for 10 min each, and infiltrated with propylene oxide for 10 min. The fixed samples were embedded using a Poly/Bed 812 kit (Polysciences, Warrington, PA, USA) and polymerized in an electron microscope oven (DOSAKA, Katsumi, Japan) at 65 °C for 12 h. The block was equipped with a diamond knife in an ultramicrotome, cut into 200 nm sections, and stained with toluidine blue for optical microscopy. The region of interest was then cut into 80 nm sections using the ultramicrotome, placed on copper grids, double stained with $3\%$ uranyl acetate for 30 min and $3\%$ lead citrate for 7 min, and observed by TEM (JEOL, Tokyo, Japan) equipped with a Megaview III CCD camera (Olympus Optical Co.) at an acceleration voltage of 80 kV. ## 2.12. Statistical Analysis All results are presented as the means ± standard deviations, and all statistical analyses were performed using SPSS version 22 (IBM Corporation; Armonk, NY, USA). Statistical significance was determined by the Kruskal–Wallis test for comparisons of each group, followed by a post hoc Mann–Whitney U test. In this study, groups marked with different letters indicate significant intergroup differences. , PBS/PBS-treated keratinocyte vs. H2O2/PBS-treated keratinocyte or Aging/MTS vs. Young/MTS $, H2O2-treated keratinocytes vs. H2O2/PBS-treated keratinocyte or Aging/RA+MTS vs. Aging/MTS #, vs. H2O2/RA-treated keratinocyte ## 3.1. RA Decreased Oxidative Stress in H2O2-Treated Keratinocytes and Aged Animal Skin First, we evaluated whether RA decreased oxidative stress in the cellular senescence model and aged animal skin. Since the H2O2-induced cellular senescence model is the most widely used in vitro aging model [43], we treated human keratinocytes with H2O2 (Figure S1A). After treating keratinocytes with H2O2, PBS, AA, NA, and RA were administered (Figure S1A). The oxidative stress alleviation effect of RA was compared with that of PBS, AA, and NA. NADPH oxidases are one of the primary ROS-generating enzymes [44]. NADPH oxidase activity was increased by treatment with H2O2. It was decreased by the administration of AA, NA, and RA. The most prominent decrease was observed in the RA-treated keratinocytes (Figure 1A). SOD activity was decreased by treatment with H2O2. It was increased by the administration of AA, NA, and RA. The most prominent increase was observed in the RA-treated keratinocytes (Figure 1B). 8-OHdG is a widely used marker of oxidative damage to DNA [45]. The expression of 8-OHdG was increased by treatment with H2O2. It was decreased by the administration of AA, NA, and RA. The most prominent decrease was observed in the RA-treated keratinocytes (Figure 1C). mtDNA damage was significantly increased by H2O2 treatment. It was decreased by the administration of AA, NA, and RA. The most prominent decrease was observed in the RA-treated keratinocytes (Figure 1D). RA was injected two times into aged animal skin with an MTS every two weeks (Figure S1B). The NADPH oxidase activity in aged animal skin was higher than that in young animal skin. It was decreased by RA injection (Figure 1E). The SOD level, which was evaluated with western blotting, decreased in aged skin compared with young skin. It was increased by RA injection (Figure 1F,G). The level of mtDNA damage in aged skin was higher than that in young skin, and it was decreased by RA injection. The 8-OHdG level in aged skin was higher than that in young skin, and it was decreased by RA injection (Figure 1H,I). The findings indicated that RA decreased oxidative stress and oxidative stress-induced DNA damage. ## 3.2. RA Decreased Mitochondrial Dysfunction and Cellular Senescence in H2O2-Treated Keratinocytes and Aged Skin PGC-1α, which is an essential controller of mitochondrial biogenesis [11], was decreased by treatment with H2O2. It was increased by the administration of AA, NA, and RA. The most prominent increase was observed in the RA-treated keratinocytes (Figure 2A). Cytochrome c oxidase (COX)1 and succinate dehydrogenase complex, and flavoprotein subunit A (SDHA) are also mitochondrial biogenesis markers [46]. COX1 and SDHA levels were decreased by treatment with H2O2 and increased by administration of AA, NA, and RA. The most prominent increase was observed in the RA-treated keratinocytes (Figure 2B,C). The levels of the mitochondrial fission markers DRP1 and FIS1 were decreased by treatment with H2O2 and increased by administration of AA, NA, and RA. The most prominent increase was observed in the RA-treated keratinocytes (Figure 2D,E). The levels of the mitochondrial fusion markers OPA1 and MFN2 were increased by treatment with H2O2 and decreased by administration of AA, NA, and RA. The most prominent decrease was observed in the RA-treated keratinocytes (Figure 2F,G). The levels of the cellular senescence markers P21 and P16 [47] were increased by treatment with H2O2 and decreased by administration of AA, NA, and RA. The most prominent decrease was observed in the RA-treated keratinocytes (Figure 2H,I). The levels of the markers of mitochondrial biogenesis, PGC-1α, COX1, and SDHA, were decreased in aged skin and were increased by RA injection (Figure 3A–D). The protein expression levels of DRP1 and FIS1 were decreased in aged skin and increased by RA injection (Figure 3E–G). The protein expression levels of OPA1 and MFN2 were increased in aged skin and decreased by RA injection (Figure 3E,H,I). The expression of P21 and P16 was increased in aged skin and decreased by RA injection (Figure 3J,K). ## 3.3. RA Decreased NF-Κb/AP-1 and MMP1/2/3/9 Expression in Aged Skin The expression of NF-κB and AP-1 in aged skin was increased compared with that in young skin. However, these levels were decreased by RA injection (Figure 4A,B). The expression of MMP$\frac{1}{2}$/$\frac{3}{9}$ in aged skin was increased compared with that in young skin. These levels were decreased by RA injection (Figure 4C–F). ## 3.4. RA Increased the Expression of Laminin and Nidogen and the BM Density The expression of laminin and nidogen was significantly lower in aged skin than in young skin. These levels were increased by RA injection (Figure 5A–C). The BM density was evaluated with PAS staining. The intensity of the pink color observed by PAS staining of the aged skin was lower than that of young skin; however, this intensity was increased by RA injection (Figure 5D,E). The BM consists of three layers: the lamina lucida, lamina densa, and lamina fibroreticularis [48]. The lamina densa has a sheet-like structure. The lamina lucida exists between the lamina densa and the epithelial layer and forms hemidesmosomes, which are electron-dense plaques [27]. It is known that photoaging induced disruption and duplication of the lamina densa [29]. The hemidesmosomes and lamina densa were observed by transmission electron microscopy. In aged skin, the lamina densa was more disrupted, and the number of hemidesmosomes was less than that in young skin. By RA injection, disruption of the lamina densa was improved, and the number of hemidesmosomes was increased (Figure 5F). ## 3.5. RA Upregulated the Expression of TGF-Β, CTGF, and α-Smooth Muscle Actin (A-SMA) and Collagen Fiber Accumulation in Aged Skin In aged skin, the expression of TGF-β and CTGF was decreased compared with that in young skin. These levels were increased by RA injection (Figure 6A–C). When fibroblasts are changed to activated myofibroblasts that express α-SMA, the expression of collagen type I and collagen type III increases [49,50]. The expressions of α-SMA, COL1A1, and COL3A1 were decreased in aged skin compared with young skin and increased by RA (Figure 6A,D,E,F). The collagen fiber density in the skin was evaluated with Masson’s trichrome staining. The collagen fiber density in aged skin was decreased compared with that in young skin, and this density was increased by RA injection (Figure 6G,H). Skin elasticity was evaluated with API-100® (Aram Huvis Co., Ltd.). Skin elasticity change was decreased in aged skin and was increased by RA injection (Figure 6I). ## 4. Discussion During intrinsic (chronological) aging, increasing levels of senescent epidermal or dermal cells lead to aggravation of DNA damage and mitochondrial dysfunction in neighboring cells [51]. Moreover, environmental factors such as UV radiation lead to increased deterioration of cellular senescence [52,53]. Collagen fibers play an essential role in maintaining skin elasticity by supporting the skin matrix [54,55]. Type I collagen is the main type of skin collagen and is mainly produced by fibroblasts in the dermis layer [56]. During aging, the ability of fibroblasts to synthesize collagen decreases by 1.0–$1.5\%$ each year, and decreasing collagen is accompanied by the formation of wrinkles [57,58]. During skin aging, changes in communication between keratinocytes and fibroblasts result in decreased levels of collagen fibers in the skin by both decreasing collagen synthesis and increasing collagen destruction [59,60,61]. Aged keratinocytes lead to more destruction of elastin fiber than young keratinocytes after UV exposure [62]. For skin rejuvenation, various effective formulas, including antioxidants, have been used via various delivery systems, such as topical creams, hypodermic needles, and microneedles [63]. Since topical creams only spread following the skin surface, the penetrating amount of drug is just 10–$20\%$ of the total amount of drug included in the cream [64]. Hypodermic needles can deliver almost 90–$100\%$ of the contained drug; however, they cause pain [65,66]. The delivery efficacy of microneedles is similar to that of hypodermic needles; however, microneedles do not cause pain [66]. Microneedles penetrate the stratum corneum and deliver drugs to the epidermis or upper dermal layer [66]. Since we delivered RA via MTS, we thought that RA could first affect keratinocytes in the epidermis and then keratinocyte-modulated fibroblasts in the dermis. Thus, we evaluated whether RA could decrease oxidative stress and mitochondrial dysfunction in H2O2-induced senescent keratinocytes. We evaluated oxidative stress by measuring NADPH oxidase activity and SOD activity in H2O2-treated keratinocytes. After the H2O2 treatment, NADPH oxidase activity was increased, and SOD activity was decreased. RA decreased NADPH oxidase activity and increased SOD activity. mtDNA damage and 8-OHdG levels, which are markers of oxidative damage to DNA, were increased by H2O2 treatment. However, they were decreased by RA. Similar to the result of the in vitro test, the NADPH oxidase activity of aged skin was increased compared with that of young skin. SOD expression was decreased in aged skin compared with young skin. After RA injection, NADPH oxidase activity decreased, and SOD expression was increased. Endogenous ROS are mainly generated in the mitochondria since ROS are a byproduct of energy production [67,68]. During aging, chronic accumulation of ROS in the mitochondria leads to mutations in mtDNA that cause mitochondrial dysfunction [69,70,71]. Since mitochondrial dysfunction causes senescence in both keratinocytes and fibroblasts, which eventually leads to skin wrinkle formation [20,21,22,23,24,25], we evaluated whether RA could modulate ECM destruction conditions by decreasing keratinocyte senescence and mitochondrial dysfunction. After H2O2 treatment, the levels of mitochondrial biogenesis markers (PGC-1α, COX1, and SDHA) were decreased, and these levels were increased by RA administration. Mitochondrial fission, which was evaluated by measuring the expression of DRP1 and FIS1, was decreased by H2O2 treatment. The levels of these markers were increased by RA. Mitochondrial fusion, which was evaluated by measuring the expression of OPA1 and MFN2, was increased by H2O2 treatment and decreased by RA. The levels of the cellular senescence markers P21 and P16 were increased by H2O2 and decreased by RA. It seemed that H2O2 induced mitochondrial dysfunction and cellular senescence in keratinocytes, and this effect was reduced by RA. These changes were also observed in aged animal skin. Mitochondrial biogenesis and fission were decreased in aged skin and increased by RA. In contrast, mitochondrial fusion was increased in aged skin and decreased by RA. Cellular senescence in aged skin, which was evaluated by measuring P21 and P16, also increased and was decreased by RA injection. NF-κB/AP-1/MMPs, which are involved in one of the main pathways involved in ECM destruction during aging [4,5,6], were evaluated in aged skin. The expression of NF-κB and AP-1 was increased in aged skin and decreased by RA injection. MMP$\frac{1}{2}$/$\frac{3}{9}$ levels were increased in aged skin and decreased by RA injection. MMPs destroy the BM as well as collagen fibers in the ECM of the dermis [72]. During aging, the levels of proteins that form the BM are decreased [73]. Proteins that form the structure of the BM, such as collagen XVII, are also involved in transmembrane signal transduction during keratinocyte differentiation [74]. Laminin is also involved in supporting structural stability as well as modulation of cellular proliferation, migration, and differentiation [74]. Since BM plays an essential role in skin homeostasis, controlling the levels of BM proteins that are decreased by aging has been considered a method for decreasing skin wrinkles [75]. Epidermal keratinocytes express laminin and nidogen and secrete those proteins into the dermal-epidermal junction [76]. Then, laminin and nidogen are assembled into the BM [76]. Thus, keratinocyte function for modulating the BM is also important, in addition to fibroblast function. We hypothesized that senescent keratinocytes might affect the expression of BM proteins such as laminin and nidogen by increasing oxidative stress. Thus, we evaluated the expression of nidogen and laminin in aged skin. These levels were decreased in the aged skin and increased by RA injection. The BM density, which was evaluated with PAS staining, was also decreased in aged skin and increased by RA injection. Since increased oxidative stress also leads to decreased collagen synthesis by decreasing TGF-β [77], we evaluated the levels of TGF-β and CTGF, which are involved in collagen synthesis. In aged skin, the levels of TGF-β and CTGF were decreased, and these levels were increased by RA injection. Moreover, the expression of COLI and COLIII and collagen density were decreased in aged skin and increased by RA injection. Skin elasticity, which was evaluated with API-100, was decreased in aged skin and increased by RA. ## 5. Conclusions Our study showed that RA decreased oxidative stress and mtDNA injury, which eventually decreased mitochondrial dysfunction in aged skin. RA also decreased ECM destruction by decreasing the levels of NF-κB/AP-1/MMPs and increasing the levels of BM proteins such as nidogen and laminin. RA enhanced the collagen synthesis-related signaling pathway of TGF-β and CTGF. These modulations associated with RA treatment led to increased collagen fiber accumulation and skin elasticity in aged skin. ## References 1. Kandola K., Bowman A., Birch-Machin M.A.. Oxidative stress—A key emerging impact factor in health, ageing, lifestyle and aesthetics. Int. J. Cosmet. Sci.* (2015) 37 1-8. DOI: 10.1111/ics.12287 2. Reilly D.M., Lozano J.. Skin collagen through the lifestages: Importance for skin health and beauty. Plast. Aesthet. Res. (2021) 8 2. DOI: 10.20517/2347-9264.2020.153 3. Ahsanuddin S., Lam M., Baron E.D.. Skin aging and oxidative stress. AIMS Mol. Sci. (2016) 3 187-195. DOI: 10.3934/molsci.2016.2.187 4. Wlaschek M., Briviba K., Stricklin G.P., Sies H., Scharffetter-Kochanek K.. 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--- title: Antioxidant, Anti-Inflammatory and Anti-Diabetic Activities of Tectona grandis Methanolic Extracts, Fractions, and Isolated Compounds authors: - Mei Han - Fengxian Yang - Kun Zhang - Jiyan Ni - Xia Zhao - Xuelin Chen - Zhennan Zhang - Hanlei Wang - Jing Lu - Yumei Zhang journal: Antioxidants year: 2023 pmcid: PMC10044725 doi: 10.3390/antiox12030664 license: CC BY 4.0 --- # Antioxidant, Anti-Inflammatory and Anti-Diabetic Activities of Tectona grandis Methanolic Extracts, Fractions, and Isolated Compounds ## Abstract Tectona grandis is a traditional Dai medicine plant belonging to the Lamiaceae family, which can be used to treat malaria, inflammation, diabetes, liver disease, bronchitis, tumors, cholelithiasis, jaundice, skin disease and as an anti-helminthic. To find more novel therapeutic agents contained in this medicinal plant, the antioxidant, anti-inflammatory and anti-diabetic activities of T. grandis methanolic extract, fractions and compounds were evaluated. In this study, 26 compounds were isolated from the leaves and branches of T. grandis. Their structures were identified based on extensive spectral experiments, including NMR, ESI-MS and comparison with published spectral data. Among them, compounds 1–2, 4–6, 9–14 and 16–22 were reported for the first time for this plant. The antioxidant activity screening results showed that compounds 5, 15 and 23 had potent antioxidant capacities, with SC50 values from 0.32 to 9.92 µmol/L, 0.92 to 1.10 mmol Trolox/L and 1.02 to 1.22 mmol Trolox/L for DPPH, ABTS and FRAP, respectively. In addition, their anti-inflammatory effects were investigated by releasing TNF-α, IL-1β and IL-6 through the use of mouse monocytic macrophages (RAW 264.7). Compounds 1, 13, 18 and 23 had the effects of reducing the expression of inflammatory factors. Compounds 13 and 18 were reported for the first time for their anti-inflammatory activities. Furthermore, the methanolic extract (ME), petroleum ether extract (PEE) and EtOAc extract (EAE) of T. grandis showed significant glucose uptake activities; compounds 21 and 23 significantly promoted glucose uptake of 3T3-L1 adipocytes at 40 µM. Meanwhile, compounds 4, 5 and 7 showed significant inhibitory activities against α-glucosidase, with IC50 values of 14.16 ± 0.34 µmol/L, 19.29 ± 0.26 µmol/L and 3.04 ± 0.08 µmol/L, respectively. Compounds 4 and 5 were reported for the first time for their α-glucosidase inhibitory activities. Our investigation explored the possible therapeutic material basis of T. grandis to prevent oxidative stress and related diseases, especially inflammation and diabetes. ## 1. Introduction Tectona grandis has been widely used in traditional Dai medicine. It mainly grows in tropical and subtropical southwestern China, India, Laos and northern Thailand. It is a large deciduous tree, measuring up to 40–50 m, with a deeply fluted trunk that can reach 2–2.5 m in diameter and a brown or gray bark [1]. Previous phytochemical research reported that T. grandis was not only rich in flavonoids and quinones, but also contained phenolic, steroids, phenylpropanoids, fatty esters and other compounds [2]. In ethnomedicine, T. grandis is commonly used to treat wounds, pain, fever, malaria, inflammation, diabetes, liver disease, helminthic infection, bronchitis, tumors, cholelithiasis, jaundice, skin disease and bacterial infection [3,4,5,6,7]. Pharmacological studies conducted on the methanolic extracts of T. grandis bark and flowers established its hypoglycemic activities [8,9]. The leaf extract of T. grandis has significant wound healing activity [10]. Hydrochloric acid extract of T. grandis leaves exhibited antitumor activity in the female Swiss mouse malaria model, thereby validating its traditional use in the treatment of tumors [11]. Traditional use of T. grandis as an antibacterial and anti-inflammatory medicine was validated by a study conducted by Bitchagno [12], who reported that the ethanolic extract from the fruit of T. grandis exhibited a remarkable inhibitory effect on four Gram-negative bacteria, and the methanolic extract of T. grandis woods demonstrated significant analgesic activity and inhibited edema action in writhing test and paw edema test rats [13]. In summary, T. grandis has a wide range of pharmacological properties. However, the previous modern pharmacological research on T. grandis mainly focused on antibacterial and analgesic aspects [14,15]; the components of antioxidative, anti-inflammatory and anti-diabetes effects of T. grandis have not yet been studied in detail. It is worthwhile to explore the therapeutic material basis and effects of T. grandis in treating oxidative stress and related diseases, such as inflammation and diabetes. Reactive oxygen species (ROS) have important roles in a wide range of physiological processes; however, oxidative stress and the resultant oxidative damage have been implicated in many human diseases, including cardiovascular disease, neurodegenerative diseases, inflammation, diabetes and cancer and also in the aging process [16,17,18]. Extensive or prolonged exposure to ROS results in oxidative stress, which is a deleterious process that damages lipids, proteins and DNA in the cell. Excessive ROS can cause damage to cell structure and function and induce somatic cell mutation and tumor transformation [19]. It is noteworthy that excessive ROS produced during oxidative metabolism can induce inflammatory processes leading to the production of many inflammatory mediators such as TNF-α, IL-1β and IL-6 [20], which can lead to a variety of chronic diseases. As such, antioxidants play an important role in anti-inflammation and protection against oxidative damage to proteins and DNA. Meanwhile, oxidative stress and inflammation play a key role in the development of diabetes and its complications [21,22]. When excessive amounts of ROS are produced in the body, the internal oxidation and antioxidant effects will be out of balance, and lead to oxidative stress and ultimately damage the macromolecules involved in insulin release [23]. According to the International Diabetes Federation (IDF), globally, the number of cases of diabetes are predicted to increase from 537 million in 2021 to 643 million by 2030 [24]. α-Glucosidase is an important catalytic enzyme involved in the hydrolysis of carbohydrates in the gastrointestinal tract, which enables monosaccharides to be absorbed into the blood, thus reducing postprandial glucose fluctuations in diabetic patients. It has been confirmed that α-glucosidase inhibitors possessing delayed α-glucose uptake can reduce postprandial blood glucose levels [25]. In view of this, one of the therapeutic strategies used to manage diabetes focuses on the inhibition of α-glucosidase. Herein, we reported the isolation and structure identification of compounds 1–26 from T. grandis, together with the exploration of their potential antioxidant, anti-inflammatory, α-glucosidase inhibition and glucose uptake activities. Firstly, DPPH, ABTS and FRAP methods were used to detect and analyze the antioxidant activities and the protective effects against oxidative damage to DNA and protein. Secondly, different types of compounds were screened for the inhibitory effects of TNF-α, IL-1β and IL-6 inflammatory factors. Finally, glucose uptake assays and α-glucosidase inhibition experiments were used to explore its anti-diabetes effect. The potent antioxidant capacities of compounds 15 and 23 suggested that they might be potential natural candidate drugs to inhibit oxidative stress and prevent DNA and protein oxidative damage. ## 2.1. Plant Material Branches and leaves of T. grandis were collected from Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences, in August 2020. The original plants were identified by Hua Shuai, an engineer at the Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. The voucher specimen [20200801] was given to the Innovative Drug Research Group, Xishuangbanna Tropical Botanical Garden, Chinese Academy of Sciences. ## 2.2. General Experimental Procedures ESI-MS was carried out using a Bruker Micro ToF-Q II mass spectrometer (Bruker Daltonics, Fremont, CA, USA). NMR spectra were recorded using a Bruker AV II-600 or 400 MHz spectrometer (Bruker, Fällanden, Switzerland). Column chromatography (CC) was run on silica gel (80–100 mesh or 200–300 mesh) (Qingdao Marine Chemical Co., Ltd., Qingdao, China), LiChroprep RP-C18 gel (Merck, 40–63 μm) and Sephadex LH-20 (GE Healthcare). Fractions were monitored using thin layer chromatography (TLC) and spots were visualized by heating silica gel plates sprayed with $10\%$ H2SO4/CH3CH2OH. Semipreparative HPLC was run on a Shimadzu system (Shimadzu Corporation, Nakagyo-ku, Kyoto, Japan) with a Shim-pack Scepter C18-120 (4.6 mm × 250 mm, 5 µm). RAW 264.7 mouse mononuclear macrophages and 3T3-L1 mouse preadipocytes were purchased from the American Type Culture Collection (ATCC, Manassas, VA, USA). High glucose DMEM, low glucose DMEM, Pen-Strep solution (P/S), insulin, certified fetal bovine serum (FBS), special newborn calf serum (NBCS) and phosphate-buffered saline (PBS) were purchased from Biological Industries (Shanghai, China). 3-Isobutyl-1-methylxanthine (IBMX) and dexamethasone (DEX) were obtained from Sigma-Aldrich (St. Louis, MO, USA). Rosiglitazone (ROSI) was purchased from Meilun Biotech Co., Ltd. (Dalian, Liaoning, China). Ascorbic acid, folin-phenol, 5 × protein loading buffer, the sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) gel kit and dimethyl sulfoxide (DMSO) was obtained from Solarbio (Beijing, China). Lipopolysaccharide (LPS) was obtained from ACMEC (Beijing, China). The cytokine kit was purchased from Kunming Zanna Biotech Co., Ltd. (Kunming, China), glucose test kit was purchased from Rongsheng Biotech Co., Ltd. (Shanghai, China). CellTiter 96® AQueous One Solution Cell Proliferation Assay was obtained from Promega Corporation (Madison, WI, USA). α-Glucosidase (33 U/mg), acarbose, 4-nitrophenyl-α-D-glucopyranoside (pNPG) and ascorbic acid were purchased from Yuanye Biotech Co., Ltd. (Shanghai, China). The absorbance was measured using a microplate reader (Molecular Devices, Palo Alto, Santa Clara, CA, USA). 2,2′-Azobis (2-methylpropionamidine) dihydrochloride (AAPH) was purchased from GlpBio (Montclair, America). pBR322 DNA was purchased from Takara (Beijing, China). The 5 × DNA loading buffer was purchased from Shanghai Generay Biotech Co., Ltd. (Shanghai, China). Agarose and rutin were obtained from Aladdin (Shanghai, China). Bovine serum albumin (BSA) was purchased from BioFroxx (Einhausen, Germany). 2,2′-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) (ABTS) and ferric-reducing antioxidant power (FRAP) detection reagents were purchased from Suzhou Comin Biotechnology Co., Ltd. (Jiangsu, China). Gallic acid, sodium nitrite, aluminum nitrate, sodium carbonate, 2,2-diphenyl-1-picrylhydrazyl (DPPH), brilliant blue R and sodium hydroxide were purchased from Macklin (Shanghai, China). The other chemicals and reagents were purchased from local suppliers. ## 2.3. Extraction, Isolation and Purification Dried branches and leaves of T. grandis (16 kg) were extracted using heating reflux for 3 h in $90\%$ methanolic (100 L × 3 times). The filtrate was combined and concentrated under vacuum to obtain a methanolic extract (ME, 1.55 kg), then sequentially partitioned with petroleum ether, EtOAc, n-BuOH and water, respectively, to obtain petroleum ether extract (PEE, 793 g), EtOAc extract (EAE, 141 g), n-BuOH extract (NBE, 247 g) and water extract (WE, 369 g). The EtOAc extract (141 g) was purified using silica gel CC and eluted with a gradient of petroleum ether: EtOAc (from 50:1 to 1:1, v/v) and CHCl3: MeOH (from 10:1 to 1:1, v/v) to provide six fractions (Fr. A–Fr. F). Fr. C (15.9 g) was further isolated using silica gel CC eluted with petroleum ether: EtOAc (from 100:1 to 1:1, v/v) to obtain three subfractions (C1–C3). Subfraction C3 (9.23 g) was further isolated using silica gel CC eluted with petroleum ether: EtOAc (from 50:1 to 1:1, v/v), Sephadex LH-20 eluted with CHCl3: MeOH (1:1, v/v), semi-preparative HPLC and eluted with methanol: H2O (45:55, v/v) to obtain compounds 9 (1.2 mg), 10 (2 mg), 11 (15.8 mg) and 12 (2.8 mg). Fr. D (95.4 g) was purified repeatedly using silica gel CC eluted with EtOAc: MeOH (from 80:0 to 0:1, v/v) to obtain four subfractions (D1–D4). Subfraction D2 (39.23 g) was purified repeatedly using silica gel CC eluted with EtOAc: MeOH (from 50:0 to 0:1, v/v), MCI (small pore resin gel column, polystyrene-based inverse resin filler) eluted with MeOH: H2O (from 10:90 to 100:0, v/v), Sephadex LH-20 eluted with CHCl3: MeOH (from 3:1 to 1:1, v/v), semi-preparative HPLC eluted with methanol: H2O (form 70:30 to 55:45, v/v) to obtain compounds 14 (2 mg), 16 (4 mg), 17 (10 mg), 18 (1.9 mg), 22 (0.8 mg) and 23 (4.9 mg). Fr. E (25 g) was chromatographed repeatedly with a gradient of petroleum ether: EtOAc (from 100:0 to 1:1, v/v), ODS (octadecylsilane) and eluted with petroleum ether: EtOAc (from 10:1 to 1:1, v/v), silica gel CC eluted with petroleum ether: EtOAc (from 50:1 to 1:1, v/v), Sephadex LH-20 eluted with CHCl3: MeOH (1:1, v/v) to obtain compounds 13 (4 mg), 19 (3.7 mg), 20 (2.4 mg) and 21 (5 mg). The n-BuOH extract (247.3 g) was purified using D101 macroporous resin and eluted with EtOH: H2O (from 20:80 to 100:0, v/v) to obtain three fractions (Fr. I–Fr. III). Fr. I (66.3 g) was purified repeatedly using silica gel CC eluted with CHCl3: MeOH (from 100:0 to 1:1, v/v) to obtain four subfractions (I1–I4). Subfraction I2 (15.7 g) was purified repeatedly using silica gel CC eluted with CHCl3: MeOH (from 20:0 to 1:1, v/v), MCI eluted with MeOH: H2O (from 10:90 to 100:0, v/v), Sephadex LH-20 eluted with CHCl3: MeOH (1:1, v/v), semi-preparative HPLC eluted with methanol: H2O (from 70:30 to 50:50, v/v) to obtain compounds 1 (2.1 mg), 2 (0.8 mg), 3 (1.2 mg), 4 (1.6 mg) and 5 (1 mg). Fr. II (25.9 g) was further isolated using silica gel CC eluted with petroleum ether: EtOAc (from 100:1 to 10:1, v/v) to obtain compounds 15 (236 mg), 24 (200.7 mg), 25 (209 mg) and 26 (50.7 mg). Fr. III (35.2 g) was purified repeatedly using silica gel CC eluted with CHCl3: MeOH (from 100:1 to 1:1, v/v) and petroleum ether: EtOAc (from 80:1 to 1:1, v/v), Sephadex LH-20 eluted with CHCl3: MeOH (1:1, v/v), semi-preparative HPLC eluted with methanol: H2O (80:20, v/v) to obtain compounds 6 (30.6 mg), 7 (8 mg) and 8 (0.6 mg). ## 2.4. DPPH Free Radical Scavenging Assay The DPPH free radical scavenging activities of T. grandis methanolic extract, different fractions and its compounds were conducted according to Dr. Yang et al. [ 26]. The concentration gradient of methanolic extract and different fractions was 160.0, 80.0, 40.0, 20.0, 10.0, 5.0 and 2.5 μg/mL, the concentration gradient of VC was 100.0, 50.0, 25.0, 12.5, 6.3, 3.1 and 1.6 μg/mL and the detection concentration of compounds (1–23) was 50 µM. Initially, the samples were dissolved in DMSO to different concentrations, and the DPPH was dissolved in PBS (0.1 M, pH 6.8) to 0.1 mM. Then, 190 µL DPPH solution and 10 µL sample were mixed in each well of a 96-well plate and incubated for 30 min at room temperature in the dark. The absorbance was measured using a microplate reader at 517 nm, and ascorbic acid was used as a positive control. The DPPH scavenging activity was calculated according to the following formula: Scavenging activity (%) = [1 − (A1 − A2)/(A3 − A4)] × $100\%$ [1] where A1 is the OD value of the tested samples, A2 is the OD value of the sample control, A3 is the OD value of the negative control and A4 is the OD value of the blank control. The analysis was performed in triplicates and the results were described as SC50 values. ## 2.5. ABTS Radical Cation Scavenging Assay The ABTS radical cation (ABTS•+) scavenging activities were conducted in accordance with the instructions for the ABTS kit. The total antioxidant activity values were estimated using the Trolox equivalent antioxidant capacity test. The detection concentration of methanolic extract and different fractions was 80 μg/mL, and the detection concentration of compounds (1–23) was 50 μM. The ABTS working solution was prepared by taking 1 bottle of reagent 2 and adding 11 mL reagent 1, shaking it for 20 min, letting it stand and taking the supernatant for use. Then, 190 μL of ABTS working reagent was mixed with 10 μL sample and incubated at room temperature for 5 min. The absorbance was then measured using a microplate reader at 734 nm, and the scavenging activity was calculated according to Equation [2]. The blank control group used DMSO to replace the sample. Scavenging activity (mmol Trolox/L) = 1.424 × (A1 − A2 + 0.0012)[2] where A1 is the OD value of the blank control and A2 is the OD value of the tested samples. The analysis was performed in triplicates, 1.424, 0.0012 was a constant. ## 2.6. Ferric Reducing Antioxidant Power Assay The FRAP assay was determined in accordance with the instructions for the FRAP kit. The total antioxidant activity values were estimated using the Trolox equivalent antioxidant capacity test. Initially, the detection concentration of methanolic extract and different fractions was 80 μg/mL, and the detection concentration of compounds (1–23) was 50 μM. Then, 190 μL of FRAP working reagent was mixed with 10 μL sample and incubated at room temperature for 20 min. The absorbance was then measured using a microplate reader at 593 nm, and the scavenging activity was calculated according to Equation [3]. The blank control group used DMSO to replace the sample. Scavenging activity (mmol Trolox/L) = 0.8054 × (A1 − A2 − 0.0134)[3] where A1 is the OD value of the blank control and A2 is the OD value of the tested samples. The analysis was performed in triplicates, 0.8054, 0.0134 was a constant. ## 2.7. DNA Oxidative Damage Assay The effects of T. grandis methanolic extract, different fractions and its compounds 15, 19 and 23 on the prevention of DNA oxidative damage were carried out using the method of Han et al. [ 27]. Initially, 8 μL pBR322 DNA (50 μg/mL) in PBS (0.1 M, pH 6.8) was mixed with different concentrations of T. grandis fractions and its compounds (12 μL) and incubated in water at 37 °C for 30 min. Then, 5 μL AAPH (20 mM) was added to the mixture and the reaction continued for 60 min. The control group used the same volume of PBS (0.1 M, pH 6.8) to replace AAPH. The pre-treated samples were mixed with 5 × DNA loading buffer, and the mixture (8 μL) was taken for $1.0\%$ agarose gel electrophoresis for 25 min. After electrophoresis, the gels were stained with ethidium bromide for 20 min. Finally, images were taken using a DNA gel imaging system (ChampChemi 610, Beijing sage creation Co., Ltd. Beijing, China). ## 2.8. Protein Oxidative Damage Assay The protective activities of T. grandis methanolic extract, different fractions and its compounds 15 and 23 on protein oxidative damage were carried out using the method of Yang et al. [ 28]. Initially, 20 μL BSA solutions (1 mg/mL) in PBS (0.1 M, pH 6.8) were mixed with different concentrations of fractions (40 μL) and compounds (40 μL) and incubated in water at 37 °C for 30 min. Then, 40 μL AAPH (160 mM) was added to the mixture and the reaction continued for 4 h. The control group used the same volume of PBS (0.1 M, pH 6.8) to replace AAPH. The pre-treated samples were added to 5 × SDS loading buffer and treated in water at 95 °C for 15 min. The mixture (16 μL) was taken for $10\%$ SDS-PAGE for 95 min. After electrophoresis, the gels were stained with Coomassie brilliant blue R-250 dye ($0.25\%$, w/v), and then the gels were decolorized with the decolorizing solution. Finally, images were taken using the SDS-PAGE gel imaging system (ChampChemi 610, China) and the grayscale of Western blot was analyzed using Image J software. ## 2.9. Anti-Inflammatory Activity and Cell Viability Assay In order to increase cytokine production, macrophages were treated with LPS at a final concentration of 1 µg/mL, and then with T. grandis compounds at 40 µM. The compounds were solubilized in DMEM medium containing $0.1\%$ DMSO. Dexamethasone was employed as a positive control (5 µg/mL). A centrifuge was used to separate the supernatant from the cells after 48 h of incubation, with three replicates per group. Quantification of TNF-α, IL-1β and IL-6 secretion was achieved by following the ELISA manufacturer's instructions. Then, absorbance was measured at 450 nm. Relative TNF-α, IL-1β and IL-6 expression was calculated according to Equation [4]. Following the inflammatory factor test, cell viability was detected by CellTiter96® Aqueous One Solution Cell Proliferation Assay. Then, 20 μL/well CellTiter96® Aqueous One Solution Cell Growth Assay reagent was added to the plate and incubated at 37 °C for 8 h, absorbance was measured at 570 nm and relative cell viability was calculated according to Equation [5]. Relative content of inflammatory cytokines (%) = (A1/A2)/A3 × $100\%$[4] where A1 is the concentrations value of inflammatory cytokines of each group, A2 is the relative cell viability of each group (model group is 100) and A3 is the concentration of inflammatory cytokines of model group. Cell viability rate (%) = B1/B2 × $100\%$[5] where B1 is the OD value of each fraction group or positive control group and B2 is the OD value of the model control. ## 2.10. α-Glucosidase Inhibition Assay The inhibitory effect on α-glucosidase was measured according to the previous report method with slight modifications [29]. The 20 mM sample dissolved in DMSO was diluted to 50 μM in PBS, 10 μL sample was mixed with 50 μL of 0.1 μ/mL α-glucosidase solution in 0.1 M phosphate buffer (pH 6.5) in a 96-well microplate and 10 μL PBS was used as a blank control. The mixture was incubated at 37 °C for 10 min before adding 40 µL of 5 mM 4-nitrophenyl-α-D-glucopyranoside to each well. After 30 min of incubation at 37 °C, the reaction was terminated by adding 50 μL of 0.1 M Na2CO3 to this mixture. The released 4-nitrophenol absorbance measurements were carried out using a microplate reader (SpectraMax190, USA) at 405 nm. Acarbose was used as the positive control. The enzyme inhibitory activity was expressed as % inhibition and was calculated via the equation:Inhibition rate (%) = (A1 − A2)/A1 × $100\%$[6] where A1 is the OD value of the negative control and A2 is the OD value of each fraction group or positive control group. ## 2.11. Glucose Uptake and Cell Viability Assay Mouse 3T3-L1 preadipocyte cells were maintained in DMEM supplemented with $10\%$ NBCS and $1\%$ P/S at 37 °C in a humidified environment with $5\%$ CO2, and then began starvation when the cells grew to $80\%$ confluence (day 0). To facilitate differentiation to adipocytes, 2-days post-confluent cells were placed in $10\%$ FBS DMEM supplemented with $1\%$ P/S, 0.5 mM IBMX, 1 μM Dex, 1 μM Rosi and 100 nM insulin (day 2). For another three days (day 5), the medium was changed to high glucose DMEM containing $10\%$ FBS, $1\%$ P/S and 100 nM insulin for one day (day 6), and the 3T3-L1 preadipocytes were fully differentiated into mature adipocytes. Next, differentiated 3T3-L1 adipocytes at a density of 5 × 104 cells/well were cultured with DMEM in 96-well plates and divided into the blank control group, model group, insulin group (100 ng/mL), berberine group (10 μg/mL) and compound groups (40 μM). Then, 10 μL samples were mixed with 190 μL medium and added to individual 3T3-L1 adipocytes. This was repeated 3 times. After 24 and 48 h of culture, glucose uptake was initiated by the addition of 10 μM medium and 190 μL glucose detection reagent to each well, reaction at 37 °C for 15 min, and the glucose uptake was measured according to the operating instructions of the glucose content determination kit. Following the glucose uptake test, cell viability was detected by CellTiter96® Aqueous One Solution Cell Proliferation Assay. Then, 20 μL/well CellTiter96® Aqueous One Solution Cell Growth Assay reagent was added to the plate and incubated at 37 °C for 8 h. Absorbance was measured at 490 nm and relative cell viability was calculated according to Equation [7]. Cell viability rate (%) = A1/A2 × $100\%$ [7] where A1 is the OD value of each fraction group or positive control group and A2 is the OD value of the blank control. ## 2.12. Statistical Analysis All data are presented as the means ± SD from 3 replicates. The differences between different samples were assessed using a one-way analysis of variance (ANOVA). It was considered a significant difference when the p value was less than 0.05. All analyses were performed using Graph Pad Prism 7.0 software (Graph Pad Software Inc., San Diego, CA, USA). ## 3.1. Structure Elucidation Twenty-six compounds (Figure 1) were isolated from the dried branches and leaves of T. grandis, including luteolin-7-O-β-D-glucoside [1] [30], acacetin-7-O-β-glucuronide [2] [31], apigenin [3] [32], apigenin-7-O-β-D-glucuronide methyl ester [4] [33], vitegnoside [5] [34], luteolin [6] [35], rhamnetin [7] [36], quercetin [8] [37], isozyganein [9] [38], 1-hydroxy-6-hydroxymethyl anthraquinone [10] [39], luteolin-3′-O-glucuronide [11] [40], 1-O-methylemodin [12] [41], 3-carbomethoxy-1-hydroxy-9,10-anthraquinone [13] [42], 3-hydroxy-2-methyl-9,10-anthraquinone [14] [43], verbascoside [15] [44], 1H-indole-3-carboxylic acid [16] [45], 3-hydroxy-4-methoxycinnamaladehyde [17] [46], 2β,3β,19α,23-tetrahydroxy-urs-12-en-28-O-[β-D-glucopyranosyl [1-2]-β-D-glucopyranosyl] ester [18] [47], rel-(2α,3β)-7-O-methylcedrusin [19] [48], 7S,8R-syringylglycerol-8-O-4′-(synapyl alcohol) ether [20] [49], rel-5-(3S,8S-dihydroxy-1R,5S-dimethyl-7-oxa-6-oxobicyclo [1–3]oct-8-yl)-3-methyl-2Z,4E-pentadienoic acid [21] [50], austrocortirubin [22] [51], gallic acid [23] [52], oleanolic acid [24] [53], β-sitosterol [25] [54] and β-sitosterol 3-O-β-D-glucopyranoside [26] [55]. Among them, compounds 1–2, 4–6, 9–14 and 16–22 were isolated from T. grandis for the first time. Compound 1. C21H20O11. ESI-MS m/z 447 [M-H]−; 1H NMR (CD3OD, 500 MHz,) δH 7.79 (1H, d, H-2′), 7.62 (1H, dd, $J = 8.5$, 2.0 Hz, H-6′), 6.99 (1H, d, $J = 8.5$ Hz, H-5′), 6.60 (1H, s, H-3), 6.46 (1H, d, H-8), 6.21 (1H, d, $J = 2.0$ Hz, H-6), 4.61 (1H, s, H-1″), 4.15 (1H, d, $J = 12.0$ Hz, H-6″a), 3.84 (1H, dd, $J = 12.0$, 6.6 Hz, H-6″b), 3.64 (1H, m, H-5″), 3.58 (2H, overlapped, H-2″,3″), 3.44 (1H, m, H-4″); 13C NMR (CD3OD, 125 MHz) δC 183.9 (C-4), 166.5 (C-7), 165.6 (C-2), 163.3 (C-9), 159.5 (C-5), 153.2 (C-4′), 147.0 (C-3′), 124.0 (C-1′), 123.6 (C-6′), 118.2 (C-5′), 116.9 (C-2′), 105.3 (C-10), 104.3 (C-3), 104.24 (C-1″), 100.3 (C-6), 95.3 (C-8), 76.9 (C-2″), 76.8 (C-3″), 74.6 (C-4″), 72.9 (C-5″), 64.4 (C-6″). Compound 2. C22H20O11. ESI-MS m/z 459 [M-H]−; 1H NMR (DMSO-d6, 500 MHz) δH 12.98 (1H, s, 5-OH), 7.96 (2H, d, $J = 8.8$ Hz, H-2′,6′), 6.94 (2H, d, $J = 8.8$ Hz, H-3′,5′), 6.87 (1H, s, H-3), 6.86 (1H, d, $J = 2.2$ Hz, H-8), 6.47 (1H, d, $J = 2.2$ Hz, H-6), 5.36 (1H, s, H-1″), 4.20 (1H, d, $J = 9.6$ Hz, H-5″), 3.66 (3H, s, 4′-OCH3), 3.41–3.34 (2H, overlapped, H-3″,4″), 3.31 (1H, m, H-2″); 13C NMR (DMSO-d6, 125 MHz,) δC 196.5 (C-6″), 182.0 (C-4), 164.3 (C-7), 162.4 (C-2), 161.4 (C-5), 161.2 (C-4′), 156.9 (C-9), 128.6 (C-2′), 128.6 (C-6′), 121.0 (C-1′), 116.0 (C-3′), 116.0 (C-5′), 105.5 (C-10), 103.1 (C-3), 99.3 (C-1″), 99.0 (C-6), 94.6 (C-8), 75.4 (C-3″), 75.1 (C-5″), 72.7 (C-2″), 71.3 (C-4″), 48.6 (4′-OCH3). Compound 3. C15H10O5. ESI-MS m/z 269 [M-H]−; 1H NMR (DMSO-d6, 600 MHz) δH 12.90 (1H, s, 5-OH), 7.82 (2H, d, $J = 8.7$ Hz, H-2′,6′), 6.90 (2H, d, $J = 8.7$ Hz, H-3′,5′), 6.52 (1H, d, $J = 2.2$ Hz, H-8), 6.51 (1H, s, H-3), 6.42 (1H, d, $J = 2.2$ Hz, H-6); 13C NMR (DMSO-d6, 150 MHz) δC 183.1 (C-4), 165.1 (C-7), 164.9 (C-2), 163.4 (C-5), 161.9 (C-4′), 158.8 (C-9), 129.3 (C-2′,6′), 123.3 (C-1′), 116.9 (C-3′,5′), 105.4 (C-10), 104.1 (C-3), 99.7 (C-6), 94.7 (C-8). Compound 4. C22H20O11. ESI-MS m/z 459 [M-H]−; 1H NMR (DMSO-d6, 600 MHz) δH 7.95 (2H, d, $J = 8.8$ Hz, H-2′,6′), 6.94 (2H, d, $J = 8.8$ Hz, H-3′,5′), 6.86 (2H, d, $J = 2.1$ Hz, H-3,8), 6.47 (1H, d, $J = 2.1$ Hz, H-6), 5.31 (1H, d, $J = 7.4$ Hz, H-1″), 3.66 (3H, s, H-5″); 13C NMR (DMSO-d6, 150 MHz) δC 182.0 (C-4), 169.3 (C-6″), 164.4 (C-2), 162.4 (C-7), 161.5 (C-5), 161.2 (C-4′), 157.0 (C-9), 128.7 (C-2′), 128.7 (C-6′), 120.9 (C-1′), 116.1 (C-3′,5′), 105.5 (C-10), 103.1 (C-3), 99.3 (C-6), 99.0 (C-1″), 94.7 (C-8), 75.4 (C-3″), 75.2 (C-5″), 72.7 (C-2″), 71.3 (C-4″), 52.1 (5″-OCH3). Compound 5. C22H20O12. ESI-MS m/z 475 [M-H]−; 1H NMR (DMSO-d6, 600 MHz) δH 12.93 (1H, s, 5-OH), 7.68–7.61 (2H, m, H-2′,6′), 6.96 (1H, d, $J = 8.4$ Hz, H-5′), 6.81 (1H, s, H-3), 6.46 (1H, d, $J = 1.8$ Hz, H-8), 6.19 (1H, d, $J = 1.8$ Hz, H-6), 5.22 (1H, d, $J = 7.3$ Hz, H-1″), 4.21 (1H, d, $J = 9.7$ Hz, H-5″), 3.30–3.38 (3H, overlapped, H-3″,2″,4″), 3.16 (3H, s, 6″-OCH3); 13C NMR (DMSO-d6, 150 MHz) δC 181.7 (C-4), 169.3 (C-6″), 164.5 (C-7), 163.3 (C-2), 161.4 (C-5), 157.3 (C-9), 151.3 (C-4′), 145.2 (C-5′), 122.0 (C-2′), 121.1 (C-1′), 116.8 (C-3′), 113.9 (C-6′), 103.6 (C-10), 103.1 (C-3), 100.8 (C-1″), 98.9 (C-6), 94.0 (C-8), 75.2 (C-5″), 75.1 (C-3″), 72.9 (C-2″), 71.4 (C-4″), 52.1 (5″-OCH3). Compound 6. C15H10O6. ESI-MS m/z 285 [M-H]−; 1H NMR (DMSO-d6, 500 MHz) δH 7.41 (1H, d, $J = 2.1$ Hz, H-6′), 7.39 (1H, d, $J = 2.1$ Hz, H-2′), 6.88 (1H, 1H, H-5′), 6.65 (1H, 1H, H-3), 6.42 (1H, s, $J = 1.4$ Hz, H-8), 6.17 (1H, s, $J = 1.4$ Hz, H-6); 13C NMR (DMSO-d6, 125 MHz) δC 181.6 (C-4), 164.2 (C-2), 163.8 (C-7), 161.4 (C-9), 157.3 (C-5), 149.7 (C-4′), 145.7 (C-3′), 121.4 (C-1′), 118.9 (C-6′), 116.0 (C-5′), 113.3 (C-2′), 103.6 (C-10), 102.8 (C-3), 98.8 (C-6), 93.8 (C-8). Compound 7. C16H12O7. ESI-MS m/z 315 [M-H]−; 1H NMR (DMSO-d6, 500 MHz) δH 12.48 (1H, s, 5-OH), 7.71 (1H, d, $J = 2.1$ Hz, H-2′), 7.56 (1H, dd, $J = 8.5$, 2.1 Hz, H-6′), 6.88 (1H, d, $J = 8.5$ Hz, H-5′), 6.69 (1H, d, $J = 2.1$ Hz, H-8), 6.34 (1H, d, $J = 2.1$ Hz, H-6), 3.85 (3H, s, 7-OCH3); 13C NMR (DMSO-d6, 125 MHz) δC 175.9 (C-4), 164.8 (C-7), 160.3 (C-5), 156.0 (C-9), 147.8 (C-2), 147.2 (C-4′), 145.0 (C-3′), 136.0 (C-3), 121.8 (C-6′), 120.0 (C-1′), 115.5 (C-2′), 115.2 (C-5′), 104.0 (C-10), 97.4 (C-6), 91.9 (C-8), 56.0 (7-OCH3). Compound 8. C15H10O7. ESI-MS m/z 301 [M-H]−; 1H NMR (DMSO-d6, 500 MHz) δH 7.74 (1H, s, H-2′), 7.64 (1H, d, $J = 8.4$, 2.0 Hz, H-6′), 6.88 (1H, d, $J = 8.4$ Hz, H-5′), 6.39 (1H, d, $J = 2.0$ Hz, H-8), 6.19–6.17 (1H, m, $J = 2.0$ Hz, H-6); 13C NMR (DMSO-d6, 125 MHz) δC 177.4 (C-4), 165.7 (C-7), 162.6 (C-5), 158.3 (C-9), 148.9 (C-2), 148.1 (C-4′), 146.3 (C-3′), 137.3 (C-3), 124.2(C-1′), 121.7 (C-6′), 116.3 (C-5′), 116.1 (C-2′), 104.6 (C-10), 99.3 (C-6), 94.5 (C-8). Compound 9. C15H10O4. ESI-MS m/z 253 [M-H]−; 1H NMR (CD3OD, 600 MHz) δH 8.04 (1H, s, $J = 8.4$ Hz, H-8), 7.72 (1H, d, $J = 8.4$ Hz, H-4), 7.67 (1H, t, $J = 7.8$ Hz, H-7), 7.52 (1H, s, H-3), 7.26 (1H, d, $J = 7.8$ Hz, H-6), 2.33 (3H, s, 2-CH3); 13C NMR (CD3OD, 150 MHz) δC 189.4 (C-9), 183.9 (C-10), 163.6 (C-5), 163.5 (C-1), 138.0 (C-3), 137.3 (C-7), 135.3 (C-9a), 133.8 (C-8a), 131.1 (C-4a), 125.1 (C-6), 120.0 (C-8), 120.0 (C-4), 117.3 (C-10a), 112.7 (C-2), 16.6 (2-CH3). Compound 10. C15H10O4. ESI-MS m/z 253 [M-H]−; 1H NMR (CD3OD, 600 MHz) δH 12.61 (1H, s, 1-OH), 8.31 (2H, d, $J = 8.0$ Hz, H-8,5), 7.85 (2H, t, $J = 8.0$ Hz, H-7,4), 7.69 (1H, t, $J = 8.0$ Hz, H-3), 7.33 (1H, d, $J = 8.0$ Hz, H-2), 4.92 (2H, s, 6-CH2OH); 13C NMR (CD3OD, 150 MHz) δC 188.8 (C-9), 182.4 (C-10), 162.8 (C-1), 147.9 (C-6), 137.0 (C-3), 133.7 (C-4a), 133.5 (C-10a), 133.0 (C-8a), 132.6 (C-7), 128.1 (C-8), 124.7 (C-5), 124.5 (C-2), 119.8 (C-4), 116.4 (C-9a), 64.5 (6-CH2OH). Compound 11. C21H18O12. ESI-MS m/z 461 [M-H]−; 1H NMR (DMSO-d6, 500 MHz) δH 7.76 (1H, s, $J = 2.2$ Hz, H-2′), 7.57 (1H, dd, $J = 8.5$, 2.2 Hz, H-6′), 6.80 (1H, d, $J = 8.5$ Hz, H-5′), 6.62 (1H, s, H-3), 6.46 (1H, s, $J = 2.2$ Hz, H-8), 6.12 (1H, s, $J = 2.2$ Hz, H-6), 4.75 (1H, d, $J = 6.5$ Hz, H-1″), 3.16 (1H, s, $J = 8.5$ Hz, H-5″), 2.52–2.48 (10H, m, sugar-H); 13C NMR (DMSO-d6, 125 MHz) δC 181.3 (C-4), 172.7 (C-6″), 165.0 (C-7), 163.7 (C-2), 161.3 (C-5), 157.2 (C-9), 157.2 (C-4′), 146.9 (C-3′), 123.0 (C-6′), 123.0 (C-1′), 117.8 (C-5′), 117.3 (C-2′), 103.7 (C-10), 103.7 (C-3), 103.1 (C-1″), 98.9 (C-6), 94.1 (C-8), 76.0 (C-3″), 73.9 (C-5″), 73.2 (C-2″), 72.0 (C-4″). Compound 12. C16H12O5. ESI-MS m/z 283 [M-H]−; 1H NMR (CD3OD, 600 MHz) δH 7.98 (1H, s, 8-OH), 7.91 (1H, s, 6-OH), 7.72 (1H, d, $J = 7.8$ Hz, H-4), 7.68 (1H, t, $J = 8.0$, 7.8 Hz, H-5), 7.25 (1H, d, $J = 8.0$ Hz, H-2), 6.80 (1H, overlapped, H-7), 3.88 (3H, s, 1-OCH3), 2.36 (3H, s, 3-CH3); 13C NMR (CD3OD, 150 MHz) δC 189.2 (C-9), 183.5 (C-10), 163.2 (C-8), 157.7 (C-6), 153.0 (C-1), 137.4 (C-3), 136.3 (C-4a), 133.7 (C-10a), 127.3 (C-4), 126.5 (C-2), 125.6 (C-9a), 124.4 (C-8a), 119.9 (C-7), 116.9 (C-5), 62.1 (1-OCH3), 16.8 (3-CH3). Compound 13. C16H10O5. ESI-MS m/z 281 [M-H]−; 1H NMR (CDCl3, 800 MHz) δH 12.55 (1H, s, 1-OH), 8.46 (1H, d, $J = 1.6$ Hz, H-4), 8.39 (2H, s, H-5,8), 7.87 (1H, dd, $J = 1.6$, 0.9 Hz, H-2), 7.72 (2H, s, H-6,7), 4.02 (3H, s, H-3); 13C NMR (CDCl3, 200 MHz) δC 187.7 (C-9), 181.8 (C-10), 165.3 (3-COOCH3), 162.7 (C-1), 137.0 (C-3), 135.2 (C-6), 135.0 (C-7), 133.3 (C-8a), 133.2 (C-10a), 133.1 (C-4a), 128.3 (C-5), 127.7 (C-8), 124.7 (C-2), 119.8 (C-4), 119.5 (C-9a), 52.8 (3-COOCH3). Compound 14. C15H10O3. ESI-MS m/z 237 [M-H]−; 1H NMR (CD3OD, 600 MHz) δH 8.23 (1H, s, 3-OH), 8.22 (1H, s, $J = 8.0$, 2.0 Hz, H-8), 8.21 (1H, d, $J = 8.0$, 2.0 Hz, H-5), 8.01 (1H, s, H-1), 7.90 (1H, s, $J = 8.0$, 2.0 Hz, H-7), 7.81 (1H, d, $J = 8.0$, 2.0 Hz, H-6), 7.54 (1H, s, H-4), 2.34 (3H, s, 2-CH3); 13C NMR (CD3OD, 150 MHz) δC 184.5 (C-10), 183.7 (C-9), 163.2 (C-3), 135.3 (C-4a), 135.2 (C-7), 134.9 (C-6), 133.8 (C-10a), 131.3 (C-8a), 133.1 (C-2), 130.8 (C-1), 127.9 (C-9a), 127.9 (C-5), 127.0 (C-8), 112.5 (C-4), 16.7 (2-CH3). Compound 15. C29H36O15. ESI-MS m/z 623 [M-H]−; 1H NMR (CD3OD, 500 MHz) δH 7.58 (1H, d, $J = 15.9$ Hz, H-β′), 7.05 (1H, d, $J = 2.0$ Hz, H-2′), 6.95 (1H, dd, $J = 8.2$, 2.0 Hz, H-6′), 6.77 (1H, d, $J = 8.2$ Hz, H-5′), 6.66 (1H, d, $J = 7.8$ Hz, H-5), 6.55 (1H, dd, $J = 7.8$ Hz, H-6), 6.28 (1H, s, $J = 15.9$ Hz, H-α′), 5.18 (1H, d, H-1″′), 4.37 (1H, d, $J = 7.5$ Hz, H-1″), 2.78 (1H, t, $J = 7.5$ Hz, H-β); 13C NMR (CD3OD, 125 MHz) δC 168.2 (C=O), 149.7 (C-3′), 148.0 (C-β′), 146.8 (C-4′), 146.1 (C-4), 144.6 (C-3), 131.4 (C-1), 127.6 (C-1′), 123.2 (C-6′), 121.2 (C-6), 117.0 (C-2), 116.4 (C-5′), 116.2 (C-5), 115.1 (C-2′), 114.6 (C-α′), 104.1 (C-1″), 103.0 (C-1″′), 81.6 (C-3″), 76.1 (C-2″), 76.0 (C-5″), 73.7 (C-4″′), 72.3 (C-2″′), 72.2 (C-α), 72.0 (C-3″′), 70.5 (C-5″′), 70.4 (C-4″), 62.3 (C-6″), 36.5 (C-β), 18.4 (C-6″′). Compound 16. C9H7NO2. ESI-MS m/z 160 [M-H]−; 1H NMR (CD3OD, 600 MHz) δH 8.08 (1H, s, $J = 7.$ 0 Hz, H-8), 7.95 (1H, s, H-2), 7.43 (1H, s, H-5), 7.21–7.15 (2H, m, H-6,7); 13C NMR (CD3OD, 150 MHz) δC 169.3 (C-8), 138.3 (C-7a), 133.5 (C-2), 127.7 (C-3a), 123.7 (C-6), 122.5 (C-5), 122.1 (C-4), 113.0 (C-7), 108.8 (C-3). Compound 17. C10H10O3. ESI-MS m/z 177 [M-H]−; 1H NMR (500 MHz, CD3OD) δH δ 9.55 (1H, d, $J = 7.9$ Hz, H-9), 7.58 (1H, d, $J = 15.6$ Hz, H-7), 7.23 (1H, d, $J = 1.9$ Hz, H-2), 7.16 (1H, dd, $J = 8.2$, 1.9 Hz, H-6), 6.85 (1H, d, $J = 8.2$ Hz, H-5), 6.63 (1H, dd, $J = 15.6$, 7.9 Hz, H-8), 3.89 (3H, s, 4-OCH3); 13C NMR (125 MHz, CD3OD) δC 196.5 (C-9), 156.5 (C-4), 149.4 (C-3), 127.6 (C-8), 126.5 (C-1), 125.2 (C-7), 117.8 (C-6), 116.6 (C-2), 112.1 (C-5), 56.5 (4-OCH3). Compound 18. C42H68O16. ESI-MS m/z 827 [M-H]−; 1H NMR (DMSO-d6, 600 MHz) δH 5.24 (1H, s, H-1′), 5.17 (1H, d, $J = 7.4$ Hz, H-12), 4.44 (1H, t, $J = 7.8$ Hz, H-1″), 3.91 (1H, t, $J = 11.4$ Hz, H-2), 3.61 (1H, d, $J = 3.2$ Hz, H-3), 3.49 (1H, s, $J = 10.4$ Hz, Ha-23), 3.43 (1H, t, $J = 10.4$ Hz, Hb-23), 2.25 (1H, m, H-18), 1.22 (3H, d, $J = 14.3$ Hz, H-27), 1.12 (3H, d, $J = 14.3$ Hz, H-25), 0.91 (3H, d, $J = 5.5$ Hz, H-29), 0.85–0.83 (3H, m, H-30), 0.64 (3H, s, H-24), 0.54 (3H, s, H-26); 13C NMR (DMSO-d6, 150 MHz) δC 175.6 (C-28), 138.2 (C-13), 127.0 (C-12), 103.0 (C-1″), 94.0 (C-1′), 76.8 (C-5′), 76.8 (C-2′), 76.7 (C-3′), 76.6 (C-3″), 76.5 (C-5″), 73.5 (C-2″), 72.1 (C-19), 71.6 (C-4″), 69.9 (C-3), 69.9 (C-2), 69.2 (C-4′), 63.8 (C-23), 60.9 (C-6″), 60.9 (C-6′), 53.1 (C-18), 47.4 (C-17), 47.3 (C-9), 46.7 (C-5), 43.1 (C-1), 42.5 (C-4), 42.5 (C-8), 41.2 (C-14), 41.1 (C-20), 37.3 (C-22), 36.6 (C-10), 32.0 (C-7), 28.1 (C-15), 26.4 (C-29), 25.8 (C-21), 24.5 (C-16), 24.2 (C-27), 23.9 (C-11), 16.8 (C-6), 16.7 (C-25), 16.5 (C-26), 16.2 (C-30), 13.7 (C-24). Compound 19. C20H24O6. ESI-MS m/z 383 [M+Na]+; 1H NMR (CD3OD, 600 MHz) δH 6.95 (1H, s, H-2′), 6.83 (1H, d, $J = 8.1$ Hz, H-6′), 6.76 (1H, d, $J = 8.1$ Hz, H-5′), 6.73 (1H, s, H-4), 6.72 (1H, m, H-6), 5.49 (1H, d, $J = 6.3$ Hz, H-2), 3.86 (3H, s, 7-OCH3), 3.82 (3H, s, 3′-OCH3), 3.78–3.73 (2H, m, H-3a), 3.57 (1H, s, H-5c), 3.47 (1H, q, $J = 6.3$ Hz, H-3), 2.63 (1H, s, $J = 7.6$ Hz, H-5a), 1.84–1.81 (1H, m, $J = 7.6$ Hz, H-5b); 13C NMR (CD3OD, 150 MHz) δC 149.2 (C-3′), 147.6 (C-7a), 147.6 (C-4′), 145.3 (C-7), 137.0 (C-5), 134.9 (C-1′), 130.0 (C-4a), 119.8 (C-6′), 118.0 (C-4), 116.2 (C-5′), 114.2 (C-6), 110.6 (C-2′), 89.1 (C-2), 65.1 (C-3a), 62.3 (C-5c), 56.8 (7-OCH3), 56.4 (3′-OCH3), 55.6 (C-3), 35.9 (C-5b), 33.0 (C-5a). Compound 20. C22H28O9. ESI-MS m/z 437 [M+H]+; 1H NMR (CD3OD, 500 MHz) δH 7.58 (1H, d, $J = 15.6$ Hz, H-6′), 7.23 (1H, d, $J = 1.9$ Hz, H-2′), 7.16 (1H, dd, $J = 8.2$, 1.9 Hz, H-6), 6.85 (1H, d, $J = 8.2$ Hz, H-7′), 6.63 (1H, dd, $J = 15.6$, 7.9 Hz, H-8′), 4.91 (1H, overlapped, H-7), 4.25 (1H, s, H-9′), 4.19 (1H, s, H-8), 3.91 (6H, s, 3′,5′-OCH3), 3.88 (1H, s, H-9b), 3.56 (1H, overlapped, H-9a); 13C NMR (CD3OD, 125 MHz) δC 155.6 (C-3′,5′), 149.2 (C-3,5), 139.0 (C-4′), 136.2 (C-1), 134.1 (C-4), 133.9 (C-1′), 131.8 (C-7′), 127.4 (C-8′), 105.5 (C-2′,6′), 105.5 (C-2,6), 85.8 (C-8), 73.1 (C-7), 64.1 (C-9′), 62.1 (C-9), 56.9 (3′,5′-OCH3), 56.4 (3,5-OCH3). Compound 21. C15H20O6. ESI-MS m/z 295 [M-H]−; 1H NMR (CD3OD, 500 MHz) δH 7.97 (1H, d, $J = 16.0$ Hz, H-4), 6.39 (1H, d, $J = 16.0$ Hz, H-5), 5.84 (1H, s, H-2), 3.84 (1H, m, $J = 14.3$, 6.4 Hz, H-3′ax), 2.26 (1H, dd, $J = 14.3$, 6.4 Hz, H-2′eq), 2.06 (3H, s, 3-CH3), 1.89 (1H, dd, $J = 13.5$, 6.4 Hz, H-4′eq), 1.83 (1H, dd, $J = 14.3$, 6.4 Hz, H-2′ax), 1.72 (1H, dd, $J = 13.5$, 11.1 Hz, H-4′ax), 1.34 (3H, s, 1′-CH3), 1.07 (3H, s, 5′-CH3); 13C NMR (CD3OD, 125 MHz) δC 181.0 (C-6′), 171.1 (C-1), 148.8 (C-3), 133.4 (C-4), 131.3 (C-5), 122.1 (C-2), 89.8 (C-1′), 82.7 (C-8′), 65.2 (C-3′), 53.4 (C-5′), 42.2 (C-2′), 40.9 (C-4′), 20.9 (3-CH3), 18.4 (1′-CH3), 14.5 (5′-CH3). Compound 22. C16H12O5. ESI-MS m/z 283 [M-H]−; 1H NMR (CDCl3, 800 MHz) δH 13.77 (1H, s, 5-OH), 13.35 (1H, s, 8-OH), 8.19 (1H, d, $J = 8.0$ Hz, H-4), 8.09 (1H, s, H-1), 7.57 (1H, dd, $J = 8.0$ Hz, H-3), 6.76 (1H, d, $J = 10.0$ Hz, H-6), 2.53 (3H, s, 7-OCH3), 1.59 (3H, s, 2-CH3); 13C NMR (CDCl3, 200 MHz) δC 186.6 (C-9), 185.0 (C-10), 156.0 (C-5), 156.0 (C-7), 150.1 (C-8), 145.7 (C-2), 134.6 (C-3), 133.8 (C-4a), 131.5 (C-1a), 131.0 (C-1), 127.0 (C-4), 112.5 (C-9a), 106.5 (C-6), 106.5 (C-10a), 65.5 (2-CH3), 22.0 (7-OCH3). Compound 23. C7H6O5. ESI-MS m/z 169 [M-H]−; 1H NMR (CD3OD, 500 MHz) δH 7.05 (2H, s, H-2,6); 13C NMR (CD3OD, 125 MHz) δC 170.4 (C-7), 146.3 (C-3,5), 139.5 (C-4), 122.0 (C-1), 110.3 (C-2,6). Compound 24. C30H48O3. ESI-MS m/z 455 [M-H]−; 1H NMR (CDCl3, 500 MHz) δH 5.27 (1H, t, $J = 3.4$ Hz, H-12), 3.21 (1H, dd, $J = 11.3$, 4.3 Hz, H-3), 1.13 (3H, s, H-27), 0.98 (3H, s, H-30), 0.92 (3H, s, H-29), 0.91 (3H, s, H-26), 0.90 (3H, s, H-25), 0.77 (3H, s, H-24), 0.74 (3H, s, H-23); 13C NMR (CDCl3, 125 MHz) δC 183.4 (C-28), 143.5 (C-13), 122.6 (C-12), 79.0 (C-3), 55.2 (C-5), 47.6 (C-9), 46.5 (C-17), 45.8 (C-19), 41.5 (C-14), 40.9 (C-18), 39.2 (C-8), 38.7 (C-1), 38.3 (C-4), 37.0 (C-10), 33.7 (C-21), 33.0 (C-29), 32.6 (C-7), 32.4 (C-22), 30.6 (C-20), 28.1 (C-23), 27.9 (C-2), 27.4 (C-15), 25.9 (C-27), 23.5 (C-16), 23.4 (C-11), 22.9 (C-30), 18.2 (C-6), 17.1 (C-26), 15.5 (C-25), 15.3 (C-24). Compound 25. C29H50O. ESI-MS m/z 437 [M+Na]+; 1H NMR (CDCl3, 500 MHz) δH 5.35 (1H, s, H-6), 3.54–3.51 (1H, m, H-3), 1.00 (3H, s, H-19), 0.92 (1H, d, $J = 6.6$ Hz, H-21), 0.84 (1H, d, $J = 7.8$ Hz, H-29), 0.82 (3H, s, H-27), 0.81 (1H, d, $J = 6.8$ Hz, H-26), 0.68 (3H, s, H-18); 13C NMR (CDCl3, 125 MHz) δC 140.7 (C-5), 121.7 (C-6), 71.8(C-3), 56.7 (C-14), 56.0 (C-17), 50.1 (C-9), 45.8 (C-24), 42.3 (C-4), 42.3 (C-13), 39.7 (C-12), 37.2 (C-1), 36.5 (C-10), 36.1 (C-20), 33.9 (C-22), 31.9 (C-2), 31.9 (C-8), 31.6 (C-7), 29.1 (C-25), 28.2 (C-16), 26.0 (C-23), 24.3 (C-15), 23.0 (C-28), 21.0 (C-11), 19.8 (C-27), 19.4 (C-21), 19.0 (C-19), 18.7 (C-26), 11.9 (C-18), 11.8 (C-29). Compound 26. C35H60O6. ESI-MS m/z 577 [M+H]+; 1H NMR (CDCl3, 500 MHz) δH 5.33–5.30 (1H, m, H-6), 4.88 (1H, s, H-1′), 4.20 (1H, m, H-6′), 3.63 (1H, m, H-5′), 3.10 (1H, m, H-4′), 3.04 (1H, m, H-3′), 3.00 (1H, m, H-2′), 0.94 (3H, s, H-19), 0.89 (1H, s, H-29), 0.88 (1H, s, H-27), 0.81(1H, s, H-26), 0.79 (1H, s, H-21), 0.64 (3H, s, H-18); 13C NMR (CDCl3, 125 MHz) δC 140.4 (C-5), 121.2 (C-6), 100.7 (C-1′), 76.9 (C-3′), 76.7 (C-3), 76.7 (C-5′), 73.4 (C-2′), 70.1 (C-4′), 61.1 (C-6′), 56.1 (C-14), 55.4 (C-17), 49.6 (C-9), 45.1 (C-24), 41.8 (C-13), 38.3 (C-12), 36.8 (C-1), 36.2 (C-10), 35.5 (C-20), 33.3 (C-22), 31.4 (C-7), 31.3 (C-8), 29.2 (C-2), 28.7 (C-25), 27.8 (C-16), 25.4 (C-23), 23.8 (C-15), 22.6 (C-28), 20.6 (C-11), 19.7 (C-26), 19.1 (C-19), 18.9 (C-27), 18.6 (C-21), 11.8 (C-29), 11.7 (C-18). ## 3.2. Antioxidant Activity Assays Oxidative stress (OS) refers to the imbalance between oxidation and antioxidation in the body. Studies have found that oxidative stress is related to a variety of diseases, such as cardiovascular diseases, diabetes and metabolic disorders [56]. Therefore, the ME, PEE, EAE, NBE, WE and isolated compounds 1–23 of T. grandis were evaluated for their antioxidant activities according to DPPH, ABTS, and FRAP assays and the half maximal scavenging concentration (SC50). As shown in Table 1, the ME had antioxidant activity. This result supported the benefits of using T. grandis in herbs and food. The NBE showed significant antioxidant activity, and the EAE showed moderate antioxidant activity. The PEE showed weak antioxidant activity. The WE exhibited no significant antioxidant activity. Furthermore, in the DPPH test, we found that methanolic extract and different fractions were capable of scavenging DPPH free radicals in a concentration-dependent manner (Figure 2). The results showed that the DPPH free radical scavenging ability was ME > NBE > EAE > PEE > WE (Figure 2), and the scavenging capacity of DPPH was related to the accumulative effect of each extract. The results suggested that the main antioxidant compounds of T. grandis might present in the NBE and EAE. The scavenging capacities of compounds 1–23 are shown in Table 2. Compounds 13, 15, 21 and 23 exhibited significant DPPH antioxidant activities (SC50 = 0.32–3.56 µmol/L), compounds 2, 5–8, 15, 17, 19 and 23 exhibited significant ABTS antioxidant activities (SC50 = 0.8–1.1 mmol Trolox/L) and compounds 5, 15 and 23 exhibited significant FRAP antioxidant activities (SC50 = 1.02–1.22 mmol Trolox/L). These results indicated that compounds 15 and 23 had a potent free radical scavenging ability and ferric reducing power, which might be developed into effective natural antioxidants. In addition, compared with quinones (9–10, 12–14, 22), flavonoids (2, 5–8) and phenolic acids [15, 17, 19, 23] exhibited stronger ABTS and FRAP scavenging activities. The antioxidant capacities of flavonoids might be related to the degree of methylation. Methylation has been documented to enhance the entry of flavonoids into cells and to prevent degradation, and this might help to enhance antioxidant capacities [57]. Compounds 15 and 23 in T. grandis had significant antioxidant capacities, which was consistent with the significant antioxidant capacities of phenolic acids reported in the literature [58]. Interestingly, the potent antioxidant properties of the NBE and EAE might be due to their flavonoid and phenolic ingredients [59], which reminded us that we should pay more attention to the antioxidant activities of polyphenols and flavonoids in T. grandis. ## 3.3. Prevention of AAPH-Induced DNA Oxidative Damage Assay As the most important genetic material in human beings, oxidative damage of DNA can accelerate cell aging and apoptosis, leading to neurodegenerative diseases, inflammation, cancer and other diseases [60,61]. Studies have confirmed that the level of DNA oxidation products in the brain tissues of Alzheimer's patients is significantly increased [62]. DNA is a target for excess oxidative stress, which attacks the bases and sugar moieties, creating strand breaks, altered gene expression and, ultimately, mutagenesis [63]. As shown in Figure 3, when AAPH was added to the model control group, only open circular form (ocDNA) and linear form (linDNA) occurred, resulting in the cleavage of supercoiled circular DNA (scDNA) to ocDNA and linDNA, which indicated that AAPH successfully induced DNA oxidative damage at 20 mM. DNA derived from pBR322 plasmid showed two bands on agarose gel electrophoresis. The faster moving band corresponded to the native form of scDNA and the slower moving band was the ocDNA. The addition of samples to the reaction mixture of AAPH suppressed the formation of linDNA and induced a partial recovery of scDNA. In this study, Figure 3 shows the electrophoretic pattern of the DNA oxidative damage protection effect induced by AAPH (20 mM) from methanolic extract, different fractions and compounds 15, 19 and 23 of T. grandis. Compared with the positive control vitamin C, the EAE showed a remarkable protective effect against DNA oxidative damage induced by AAPH. The EAE showed a protective effect at 12.5 μg/mL ($p \leq 0.0001$) and the NBE showed a protective effect at 25.0 μg/mL ($p \leq 0.0001$). The ME and PEE began to be protective at 50.0 μg/mL ($p \leq 0.0001$). The results showed that the protective ability against DNA oxidative damage was EAE > NBE > ME > PEE > WE. Compounds 15, 19 and 23 showed obvious protective effects against oxidative damage. Compounds 15 and 23 began to have protective effects at 0.6 mM ($p \leq 0.0001$) and compound 19 began to have a protective effect at 5.0 mM ($p \leq 0.0001$), which suggests that compounds 15, 19 and 23 could be developed into effective natural antioxidants. In addition, it is worth noting that the EAE, NBE and compounds 15, 19 and 23 with significant antioxidant capacities showed significant protective activities against oxidative damage of DNA induced by AAPH, which suggests that the protective effect against DNA oxidative damage might be related to the antioxidant capacities. ## 3.4. Prevention of AAPH-Induced Protein Oxidative Damage Assay The literature reports that AAPH is a free radical generator that can generate alkoxy and alkylperoxy radicals, thereby inducing oxidative damage to macromolecules [64]. Protein degradation and the formation of protein carbonyl groups are the main characteristics of protein oxidation [65,66]. Studies have found that many diseases, such as Alzheimer′s disease, diabetes, cardiovascular and cerebrovascular diseases, are associated with increased protein carbonylation levels [67]. As shown in Figure 4, compared with the control group without AAPH, BSA was induced by AAPH, the BSA bands were noticeably shallower and the carbonylation level of BSA was increased, indicating that hydroxyl free radicals produced by the AAPH induced system can significantly degrade and carbonize BSA. In this experiment, the content of residual protein was used to reflect the abilities of samples to prevent protein oxidative damage. The results showed that when the concentration of AAPH was 160 mM, the content of residual protein increased with the increase of the sample concentration. As shown in Figure 4d,e, compared with the positive control group (VC), the EAE and NBE showed significant protective effects against protein oxidative damage at 63 μg/mL. In an increased sample concentration, the protective effect became more significant; the EAE and NBE at 1000 μg/mL almost completely inhibited the oxidative damage of BSA induced by AAPH ($p \leq 0.0001$), the ME and PEE began to show protective effects against protein oxidative damage at 500 μg/mL ($p \leq 0.0001$) and the WE began to show protective effects against protein oxidative damage at 1000 μg/mL ($p \leq 0.0001$). The results showed that the protective ability against protein oxidative damage was EAE > NBE > ME > PEE > WE, and this might be related to their better antioxidant capacities. As shown in Figure 4g,h, compounds 15 and 23 have significant protective effects against protein oxidative damage induced by AAPH (160 mM) in a concentration-dependent manner. Compound 15 began to show a protective effect against protein oxidative damage at 40 μM, and compound 23 began to show a protective effect against protein oxidative damage at 320 μM. The results showed that compounds 15 and 23 might be natural antioxidants, which could inhibit the oxidative damage of protein by free radicals. ## 3.5. Anti-Inflammatory Activity Assay LPS is widely used as a stimulator to activate macrophages, inducing the release of pro-inflammatory mediators from macrophages, in particular TNF-α, interleukins (IL-1β, IL-6), NO and ROS [68]. It is involved in various pathological processes of acute and chronic inflammation. The levels of TNF-α, IL-1β and IL-6 in macrophage culture supernatants were measured using an ELISA kit, and then the anti-inflammatory effects of compounds 1, 12, 13, 15, 18 and 23 on LPS-stimulated macrophages were studied. Compared with blank control cells, pro-inflammatory cytokines were increased in the model and positive groups (Dexamethasone) stimulated by LPS (Figure 5). As seen in Figure 6a, compounds 1, 18 and 23 could decrease TNF-α inflammatory factors levels at 40 µM when compared to the model controls ($p \leq 0.01$). Meanwhile, in Figure 6b, compounds 1, 13, 18 and 23 produced a significant decrease in IL-1β inflammatory factors levels at 40 µM ($p \leq 0.0001$). As shown in Figure 6c, compounds 1, 13 and 18 showed significant activities in reducing the expression of IL-6 inflammatory factors at 40 µM ($p \leq 0.05$). The anti-inflammatory activity of compound 1 might be related to the fact that the flavonoid was connected by two benzene rings (A and B) through an oxygen-containing heterocycle (C), and with the glycosylation mode on the A ring [69]. Compound 13 has significant anti-inflammatory activity, which was consistent with the literature [4]. The activity of compound 18 might be related to its structure, which is composed of six isoprene and one pentacyclic [70]. The activity of compound 23 might be related to the phenolic hydroxyl groups contained in the structure [71]. The inhibitory effects of compounds 1, 13, 18 and 23 on the expression of inflammatory cytokines develop the therapeutic material basis of T. grandis in the treatment of inflammation in traditional medicine. ## 3.6. α-Glucosidase Inhibition Assay In order to search for active compounds with α-glucosidase inhibition, T. grandis methanolic extract and different fractions at concentrations of 100, 50, 25, 12.5, 6.25, 3.125 and 1.5625 μg/mL were incubated in 96-well plates for 30 min, and then the α-glucosidase inhibition rate was examined. The results are shown in Figure 7b–e. Compared to the positive control acarbose (Figure 7a), the ME and PEE showed the most potent α-glucosidase inhibition activities with IC50 values of 3.05 ± 0.18 µg/mL and 1.92 ± 0.06 µg/mL, respectively. The EAE and NBE showed moderate α-glucosidase inhibition activities with IC50 values of 7.84 ± 0.49 µg/mL and 8.90 ± 0.79 µg/mL, respectively. The results indicated that most α-glucosidase inhibitory substances might be small polar compounds present in the PEE and EAE. Compounds 1–23 were screened for their α-glucosidase inhibitory activities, and the results are shown in Table 3. The inhibitory rates of compounds 4, 5 and 7 on α-glucosidase were $68.54\%$, $69.67\%$ and $85.13\%$, respectively, whereas compounds 1–3, 6 and 8–23 exhibited no significant α-glucosidase inhibitory activities, with IC50 values greater than 50 µM. Next, the IC50 values for compounds 4, 5 and 7 were measured, and the results are shown in Figure 7f–h. Compound 7 showed the most potent α-glucosidase inhibition activity, with an IC50 value of 3.04 ± 0.08 µmol/L. Compounds 4 and 5 showed moderate α-glucosidase inhibitory activities, with IC50 values of 14.16 ± 0.34 µmol/L and 19.29 ± 0.26 µmol/L, respectively. Furthermore, compounds 4, 5 and 7 had significant α-glucosidase inhibitory activities, which might be due to the nucleus of 2-phenylchromone, and the double bond between C2 and C3 in the C ring [72]. Therefore, the α-glucosidase inhibitory activities of compounds 4, 5 and 7 suggested that flavonoids might have better α-glucosidase inhibitory activities than the other compounds in T. grandis. Our study further provides scientific evidence for the efficacy of T. grandis as a herbal medicine in the treatment of diabetes. ## 3.7. α-Glucosidase Enzyme Kinetic Assay Lineweaver–Burk plotting was used to evaluate the inhibition type of the methanolic extract, fractions and compounds 5 and 7 partitioned from T. grandis on α-glucosidase. The concentrations of 1/[pNPG] are displayed on the X-axis, and the 1/v values obtained from the Lineweaver–Burk plot are shown along the Y-axis. As shown in Figure 8a–d, all data lines of the ME, PEE, EAE, and NBE on the Lineweaver–Burk plot intersected at a point in the second and third quadrant; with the increasing of inhibitor concentrations, the kinetic parameters of Vmax were decreased, and the kinetic parameters of Km were unchanged. Therefore, all inhibitory effects of samples on the α-glucosidase enzyme belonged to the mix inhibition type [73], which suggested that the inhibitors presented in samples ME, PEE, EAE, and NBE might be bound to the enzyme–substrate complex to inhibit α-glucosidase. Figure 8e,f show the α-glucosidase inhibitory Lineweaver–Burk double count down diagrams for compounds 5 and 7. All the data lines of compounds 5 and 7 on the Lineweaver–Burk plot intersected in the second quadrant, with the increasing of inhibitor concentrations, the Vmax of the enzymatic reaction decreased, and the Km values were unchanged, indicating that compounds 5 and 7 were an uncompetitive type of α-glucosidase enzyme inhibition [74]. It is possible to find out the enzyme inhibition effects of the active ingredients through further study of the enzyme reaction, especially of the chemical components which showed significant α-glucosidase inhibitory activities in the preliminary screening, so as to provide a valuable active lead for the study of hypoglycemia using T. grandis. ## 3.8. Glucose Uptake Activity and Cell Viability Assay To examine the glucose uptake activities of methanolic extract, different fractions and the isolated compounds 1–23 of T. grandis, glucose uptake capacities were evaluated by 3T3-L1 adipocytes. Firstly, the 3T3-L1 preadipocytes were differentiated into mature adipocytes (Figure 9). Next, 10, 20, 40, 80 and 160 μg/mL concentrations of methanolic extract and different fractions were added respectively. After 24 h, the glucose uptake was examined. As shown in Figure 10A, compared with the blank control group, the sample groups of ME, PEE and EAE significantly promoted the glucose uptake rate of 3T3-L1 adipocytes ($p \leq 0.0001$) and the NBE group significantly inhibited the glucose uptake of 3T3-L1 adipocytes ($p \leq 0.0001$). The results showed that the main hypoglycemic effect of T. grandis might be achieved by improving glucose uptake in 3T3-L1 adipocytes. In addition, the results of the cell viability assay showed that the MEE, PEE, EAE and NBE had no significant effects on the cell viabilities of 3T3-L1 adipocytes (Figure 10B). The effects of compounds 1–23 on glucose uptake in 3T3-L1 adipocytes were as follows: As shown in Figure 11A(a,b), the results showed that the glucose uptake rate at 40 μM was positively correlated with time, and the glucose uptake rate at 48 h was higher than that at 24 h. As shown in Figure 11A(b), compounds 21 and 23 could significantly promote the glucose uptake of 3T3-L1 adipocytes at 40 µM ($p \leq 0.01$), and the other tested compounds 1–20 and 22 showed no significant promotion effects ($p \leq 0.05$). Additionally, compared with the blank control, except for compounds 5 and 9, the positive control group, and all the other tested compounds had no significant effects on the cell viabilities of 3T3-L1 adipocytes (Figure 11B). ## 4. Conclusions In summary, 26 compounds were isolated from T. grandis (EAE, NBE), including nine flavonoids (1–8, 11), six anthraquinones (9, 10, 12–14, 22), one alkaloid [16], two pentacyclic triterpenoids [18, 24], four phenylpropanoids [15, 17, 19, 20], one sesquiterpene [21], phenolic acid [23] and two sterols [25, 26]. Studies of the antioxidant, anti-inflammatory and anti-diabetic activities of T. grandis methanolic extract, fractions and compounds 1–23 showed that the EAE, NBE fractions, and compounds verbascoside [15], gallic acid [23] had remarkable antioxidant activities. Furthermore, compounds verbascoside [15] and gallic acid [23] showed significantly higher antioxidant activities than the positive control ascorbic acid. Additionally, verbascoside [15] and gallic acid [23] showed significant protective effects against the oxidative damage of DNA and protein ($p \leq 0.001$). Simultaneously, the results of inflammatory factor detection showed that luteolin-7-O-β-D-glucoside [1], 2β,3β,19α,23-tetrahydroxy-urs-12-en-28-O-[β-D-glucopyranosyl [1-2]-β-D-glucopyranosyl] ester [18] and gallic acid [23] could degrade the expression of inflammatory factors (TNF-α, IL-1β, IL-6), and 2β,3β,19α,23-tetrahydroxy-urs-12-en-28-O-[β-D-glucopyranosyl [1-2]-β-D-glucopyranosyl] ester [18] and gallic acid [23] were similar to the positive control Dex inhibitory effects on TNF-α and IL-1β, which showed significant inhibitory effects on inflammatory cytokines ($p \leq 0.001$). Meanwhile, ME, PEE fractions, and compounds apigenin-7-O-β-D-glucuronide methyl ester [4], vitegnoside [5] and rhamnetin [7] had significant α-glucosidase inhibition capacities. The enzyme kinetic experiments showed that the inhibitory effects of T. grandis methanolic extract, fractions, and compounds vitegnoside [5], rhamnetin [7] on α-glucosidase belonged to mixed and uncompetitive inhibition types. 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--- title: Assessment of Oxidant and Antioxidant Status in Prepubertal Children following Vegetarian and Omnivorous Diets authors: - Grażyna Rowicka - Witold Klemarczyk - Jadwiga Ambroszkiewicz - Małgorzata Strucińska - Ewa Kawiak-Jawor - Halina Weker - Magdalena Chełchowska journal: Antioxidants year: 2023 pmcid: PMC10044729 doi: 10.3390/antiox12030682 license: CC BY 4.0 --- # Assessment of Oxidant and Antioxidant Status in Prepubertal Children following Vegetarian and Omnivorous Diets ## Abstract Oxidant-antioxidant balance is crucial for maintaining one’s health, and the diet is possibly one of the most important factors affecting this balance. Therefore, the aim of this study was to determine the oxidant-antioxidant balance in children on a lacto-ovo-vegetarian diet. The study was conducted between January 2020 and December 2021. The concentrations of total oxidant capacity (TOC), total antioxidant capacity (TAC), reduced (GSH), and oxidized (GSSG) glutathione, as well as C-reactive protein (CRP) and calprotectin were measured in serum samples of 72 healthy prepubertal children (32 vegetarians and 40 omnivores). The oxidative stress index (OSI) and the GSH/GSSG ratio (R-index) were calculated. Children on a vegetarian diet had significantly lower median values of TOC, GSH, and GSSG, and higher TAC compared with the omnivores. OSI was significantly lower in vegetarians, while R-index, as well as median values of CRP and calprotectin did not differ between both groups of children. Significant negative correlations were observed between TOC and TAC levels in the whole group of children and in vegetarians. GSH and GSSG levels correlated positively in the groups of vegetarians, omnivores, and in all the children. There were significant positive correlations between TOC and GSH, as well as GSSG levels in all the studied groups of children. Our study results suggest that the vegetarian model of nutrition allows to maintain the oxidant-antioxidant balance in the serum of prepubertal children. ## 1. Introduction In recent years, there has been growing interest in unconventional diets, including the vegetarian model of nutrition. According to the available data (consumer research), the number of adults in Poland who prefer a vegetarian diet amounts to approximately $8\%$, while the percentage of children eating unconventionally (i.e., whose parents consciously eliminate certain foods from their diet not for health reasons) is estimated at 0.5–$4.5\%$. The variation of the vegetarian diet considered to be one of the least restrictive is lacto-ovo-vegetarianism. Lacto-ovo-vegetarians do not consume meat and fish, as well as foods that contain them, but their diets include eggs, milk, and dairy products. According to the current knowledge, children can be on a vegetarian diet, provided that it is properly balanced [1]. However, some authors emphasize that it is difficult to properly balance a vegetarian diet, especially in infants during the period of expanding their diet, which may adversely affect their physical and neurocognitive development [2]. Therefore, some scientific societies recommend that in infants and young children whose parents decide to feed them a vegetarian diet, it should be a lacto-ovo-vegetarian diet [3,4,5]. Despite the documented health benefits of a vegetarian diet, which is most extensively studied in adults, attention is drawn to the fact that long-term use of any type of unbalanced diet, including a vegetarian diet, may lead to nutritional deficiencies [6,7,8,9,10]. In vegetarian diets, this applies primarily to selected exogenous amino acids, long-chain polyunsaturated fatty acids (PUFAs), minerals such as iron, calcium, and zinc, and vitamins, mainly B12 and D [10,11,12,13,14]. Possible consequences of deficiencies—particularly of those ingredients that have anti-inflammatory and antioxidant properties—include not only the induction of low-grade inflammation, but also excessive reactive oxygen species (ROS) activity. ROS are natural products of aerobic cellular metabolism and in physiological concentrations play an important role in the appropriate functioning of complex mechanisms, which control cell division and participate in many important cellular processes. Such processes include, inter alia, the activation of transcription factors, such as Nrf2 (nuclear factor-erythroid 2-related factor 2) or NF-kB (kappa-light-chain-enhancer of activated B cells), the regulation of protein phosphorylation processes, or the level of calcium in cells, etc. They are also defense agents of the body, participating in the elimination of microorganisms in the process of phagocytosis. Activation of the NF-κB pathway triggers the transcription of many genes involved in further stages of the response, including in inducing inflammation and stimulating cell proliferation. It is also a key regulator of inflammatory processes, immune response, and apoptosis. Apart from these important biological functions, ROS may also be agents damaging the cellular components [15,16]. In in vitro conditions, free radicals cause modifications and damage lipids, proteins, carbohydrates, and nucleotides, inducing changes in the DNA structure leading to mutations or cytotoxic effects. Free radicals formed in the body are eliminated through antioxidant mechanisms, including enzymes, e.g., superoxide dismutase (Cu/ZnSOD), catalase (CAT), glutathione peroxidase (GPx), glutathione reductase (GR), and glutathione transferases (GST), and small molecule antioxidants of both endogenous and exogenous origins [17]. Among endogenous low-molecular antioxidants, glutathione plays a particularly important role. It is an intracellular peptide belonging to the thiol group, i.e., chemical compounds containing sulfur, composed of three amino acids—glutamic acid, cysteine, and glycine. Key determinants of GSH synthesis, apart from the availability of the sulfur amino acid precursor, cysteine, include the activity of the enzyme, glutamate cysteine ligase (GCL), which is composed of a catalytic (GCLC) and a modifier (GCLM) subunit, and of the GSH synthetase (GS) enzyme [18]. Its reducing properties rely on an interaction with hydrogen peroxide and organic peroxides. Simultaneously, it enables the regeneration of antioxidant vitamins, e.g., ascorbic acid, α-tocopherol, and maintaining the thiol groups of proteins in a reduced state. It is one of the key factors affecting the oxidoreductive status in cells. Exogenous low-molecular antioxidants include vitamin A, β-carotene, vitamins C, E, B12, and D, the serum concentrations of which, except for vitamin D, depend mainly on the amount of intake of these compounds with food [17,19,20,21,22,23]. There are few studies assessing the oxidant and antioxidant status in children on a lacto-ovo-vegetarian diet, but they seem to be particularly important due to the fact that imbalances between pro-oxidants and antioxidants leading to oxidative stress during metabolic programming, apart from consequences in the present, may also have long-term adverse health effects [24]. The aim of the study was to evaluate the potential severity of oxidative processes and antioxidant defense capacity in children on a lacto-ovo-vegetarian diet in relation to omnivorous children. Therefore, the serum concentrations of total oxidative capacity (TOC), total antioxidant capacity (TAC), reduced (GSH), and oxidized (GSSG) forms of glutathione in vegetarian and omnivorous children were assessed. ## 2. Materials and Methods The protocol of this study was in accordance with the Helsinki Declaration of Principles and approved by the Ethics Committee of the Institute of Mother and Child, Warsaw, Poland (decision number $\frac{12}{2019}$, 12 March 2019). All children’s parents were informed about the study procedures and all signed a written consent prior to the start of the study. ## 2.1. Subjects The study was conducted at the Institute of Mother and Child in Warsaw among patients of the Gastroenterology Outpatient Clinic between January 2020 and December 2021. We examined 72 healthy prepubertal Caucasian children aged 2–10 years. Among them, there were 32 children ($47\%$ male, $53\%$ female) on a lacto-ovo-vegetarian diet and 40 children on a traditional omnivorous diet ($45\%$ male, $55\%$ female) as controls. The lacto-ovo-vegetarian children did not consume meat, poultry, and fish, but ate eggs and dairy products and followed this diet since the introduction of complementary foods. The children remained under regular medical and nutritional supervision. The exclusion criteria for the study were not being in the prepubertal period, infections of various etiologies and localizations, as well as intake of prescription medications and food supplements with anti-inflammatory and antioxidant properties, except for standard vitamin D3 supplementation, which amounted to 600–1000 IU/day [25]. ## 2.2. Anthropometric Measurements and Dietary Assessment The children’s height and weight were assessed using a standard stadiometer and electronic scale, respectively. Anthropometric measurements were taken using calibrated instruments. The same team examined all the study participants. Weight (kg) and height (m) were used to calculate BMI (body mass index). Body mass index was calculated as body weight (kg) divided by height squared (m2). BMI values were compared with BMI norms for age and sex according to the WHO criteria, thus obtaining a BMI z-score, which is a normalized relative weight indicator independent of age and sex [26,27]. Pubertal stage was assessed according to the Tanner’s criteria [28]. Dietary intakes were assessed using diet record methods. The parents of the studied children were advised by a nutritionist and asked to prepare a food diary for their children. Three dietary recalls (two weekdays and one weekend day) were performed to evaluate dietary habits. The average daily energy intake, i.e., percentage of energy from dietary protein, fat, and carbohydrates, as well as fiber and vitamin dietary intakes were assessed using the nutritional software program Dieta 5® (National Food and Nutrition Institute, Warsaw), as described in more detail in the previous article [29]. ## 2.3. Blood Sampling and Biochemical Analysis For biochemical measurements, peripheral blood (3 mL) was taken in the morning after an overnight fast. Serum samples were obtained after centrifugation (2500× g at 4 °C for 10 min) and were used for C-reactive protein (CRP) determination. Serum levels of CRP were measured using immunoturbidimetric assay on the Cobas Integra auto-analyzer (Roche Diagnostics, Basel, Switzerland). Residual serum was stored in small portions at −70 °C for a maximum of four weeks until analyses of TOC, TAC, GSH, GSSG, and calprotectin were performed. Concentrations of serum calprotectin were measured by enzyme-linked immunosorbent assay (ELISA) using specific antibodies with a high affinity to these proteins (CALPROLABTM Calprotectin (ALP) ELISA kit, CALPRO AS; Lysaker, Norway). The detection limit of this method was below 5.0 ng/mL and the intra- and inter-assay CVs were less than $5.0\%$. Serum TOC and TAC values were evaluated by colorimetric assay based on the enzymatic reaction of peroxides and peroxidases according to Tatzber et al. [ 30] (Omnignostica Forschungs GmbH, Hoflein/Danube, Austria). The analytical sensitivity of TOC was 0.06 mmol/L, and the intra- and inter-assay CVs were $4.90\%$ and $7.33\%$, respectively. The sensitivity of TAC was 0.08 mmol/L, and the intra- and inter-assay CVs were $5.00\%$ and $6.92\%$, respectively. The oxidative stress index (OSI) was calculated from the ratio of TOC to TAC levels [31]. GSH and GSSG concentrations in serum were assessed using the human (GSH) ELISA kit and human (GSSG) ELISA kit (SunRed Biotechnology Company, Shanghai, China) based on sandwich enzyme-linked immunosorbent assay using two specific and high-affinity monoclonal antibodies. The intra- and inter-assay coefficients of variations (CVs) were less than $10\%$ and $12\%$ for GSH, and $8.0\%$ and $11.0\%$ for GSSG, respectively. The analytical sensitivity of the tests was 0.118 μmol/L for GSH and 0.045 μmol/L for GSSG, respectively. The GSH/GSSG ratio (R-index), which is regarded as an indicator of the redox state of the cell, was estimated. ## 2.4. Statistical Analysis Statistical analysis was performed using SPSS software version 21. The Shapiro–Wilk test was used to check the normality of the data distribution. Parametric data were described as means and standard deviations (SDs) and were analyzed using Student’s t test. Non-parametric data were expressed as medians and interquartile ranges and were analyzed using the Mann–Whitney U test. The associations between biochemical parameters were calculated using Spearman’s rho. Differences were regarded as statistically significant at $p \leq 0.05.$ ## 3. Results Both groups of children were comparable in terms of age and anthropometric parameters (Table 1). An analysis of the children’s diets showed a similar daily energy intake in both groups of children (Table 2). Vegetarians had a significantly higher percentage of energy from carbohydrates, a lower percentage of energy from protein, and a similar percentage of energy from fat. Fiber intake was higher in vegetarians compared with omnivores. The intake of vitamin B12 was significantly lower in children on a vegetarian diet, while intakes of vitamins A, E, C, and D were similar in both groups. The differences between serum concentrations of biochemical parameters in both groups of children included in the study are presented in Table 3. Children on a vegetarian diet had significantly lower median values of serum TOC, GSH, GSSG ($p \leq 0.01$) and significantly higher median values of TAC ($p \leq 0.001$) compared with the omnivores. OSI was significantly lower ($p \leq 0.001$) in vegetarian subjects than in controls, while R-index did not differ between both groups of children. CRP (within the reference value) and calprotectin median values did not significantly differ between both groups. Relations between antioxidant biochemical parameters were shown in Table 4 and Figure 1A–C. As expected, we observed a negative correlation ($p \leq 0.05$) between the levels of TOC and TAC in vegetarians and in the whole group of children. Moreover, in the whole group of children, TOC positively correlated with GSH ($p \leq 0.05$), GSSG ($$p \leq 0.05$$) and R-index ($p \leq 0.05$) (Table 4). We also documented a strong significant positive correlation between levels of GSH and GSSG in the whole group (rho = 0.896, $p \leq 0.001$), as well as in vegetarian (rho = 0.933, $p \leq 0.001$) and omnivore (rho = 0.763, $p \leq 0.001$) groups (Figure 1A–C). We revealed no correlation between the components of the diet and markers of inflammation. Similarly, the components of the diet did not correlate with TOC, TAC, OSI and GSH, GSSG, and R-index. ## 4. Discussion Research results evaluating the concentrations of selected markers of both oxidation and reduction processes in adults on a vegetarian diet are ambiguous, and there are few studies of this type among children [32,33,34,35]. Thus, we were interested to know whether the oxidant-antioxidant balance (for the assessment of which we used the concentrations of selected oxidative stress markers such as TOC, TAC, GSH, GSSG and R-ratio, and OSI) is maintained in children on a vegetarian diet remaining under the constant medical and dietary care [36]. Such research seems to be important considering that vegetarian diets are an increasingly popular alternative to the traditional diet. Parents eating according to the vegetarian model of nutrition also feed their children similarly [37,38,39]. Among the 32 children on a vegetarian diet, 19 children had parents who were both vegetarians, while the remaining children had one vegetarian (the mother) and one non-vegetarian parent. Research results indicate that a factor predisposing to the development of many diseases is oxidative stress, which may be caused by, among others, low-grade inflammation generating reactive oxygen species [40,41,42]. Some studies show that a vegetarian diet may affect inflammatory biomarker concentrations, thus reducing the risk of chronic diseases. An inflammatory marker that has not yet been evaluated in vegetarians is the complex S100A8 and S100A9 proteins called calprotectin. Calprotectin is contained mainly in the cytosol of neutrophils and is secreted during inflammation. The determination of calprotectin concentration in the stool is used for diagnosing intestinal inflammation, including inflammatory bowel disease. It is also useful to monitor the course of these diseases, i.e., response to treatment and the occurrence of exacerbations. The determination of its concentration in both serum and plasma is used in the diagnosis of many inflammatory diseases, including autoimmune diseases [43,44]. In our study, we found no differences in calprotectin concentrations between both groups of children. Among the biomarkers of inflammation, C-reactive protein appears to be associated with the vegetarian diet, and its levels were found to be lower in vegetarians compared with omnivores in some studies of adults [32,45,46]. In two studies conducted on children, one which concerned teenagers proved no significant differences in CRP concentrations among vegetarians compared with omnivores, which is similar to our study, while in the second study, which concerned prepubertal vegetarian children, lower concentrations of this marker were found. [ 29,47]. Body mass index, length of time on a vegetarian diet, and how balanced it is in terms of ingredients with anti-inflammatory and antioxidant properties have been considered as factors that may impact inflammatory marker concentrations in vegetarians. The lower body weight observed by some authors in vegetarians compared with omnivores may be potentially associated with a lower percentage of body fat, which, as a hormonally active tissue, is responsible for the release of pro-inflammatory factors, e.g., some hormones, adhesion molecules, growth factors, and cytokines [48,49,50,51]. The nutritional status of the examined children was normal and the period of being on a vegetarian diet had been approximately six years. According to a meta-analysis by Haghighatdoost et al. [ 45], the period of being on such a diet for CRP to respond to the beneficial effects of a vegetarian lifestyle is two years. With regard to the relation between individual components of the diet and the concentration of inflammatory markers, including CRP, the research results are ambiguous. They point to both a beneficial effect of eating whole grain cereals, nuts, or legumes on CRP concentration, as well as to the lack of such a relationship. In the above analysis, either an inverse or a lack of a relationship between the consumption of fruit and vegetables and CRP concentration was found [45,52,53,54,55,56,57,58,59]. A vegetarian diet may be associated with a higher intake of vitamins, e.g., A, C, and E, and their higher concentrations in the serum [60,61,62,63]. However, not all studies confirm this. Some indicated no differences in the concentrations of such vitamins as A or E, and even found their lower concentrations in the serum of vegetarians, which has been explained by the poor bioavailability of those vitamins from plant foods, among others, and may be of particular concern for vegans [13,64,65,66,67,68]. In the studied groups of children on vegetarian and traditional diets, no significant differences were found in the content of vitamins A and E in the diet, but differences in the intake of vitamins C and B12 were observed [29]. In our study, only the intake of vitamin B12 was significantly lower in the vegetarian group. Furthermore, in this group, approximately $50\%$ of the children did not achieve the level recommended by nutrition standards for this vitamin, whereas dietary intake of vitamin B12 for omnivorous children was sufficient. A lower intake of vitamin D and a higher intake than the recommendations of vitamins A, C, and E were found in the diets of children from both groups [69]. Some authors argue that vegetarian diets may adversely affect vitamin B12 status, causing vegetarians to be predisposed to diseases in which oxidative stress and inflammation are involved [70,71,72]. Misra et al. [ 73] showed that vitamin B12 deficiency was associated with increased Malondialdehyde (MDA, a product of lipid peroxidation) levels and decreased TAC and GSH concentrations in adults. Glutathione, which plays an important role in antioxidant defense, also has a role in nutrient metabolism and the regulation of cellular events, including gene expression, protein synthesis, cell proliferation and apoptosis, signal transduction, cytokine production, and immune response [74]. Disturbances in glutathione homeostasis expressed by an imbalance in the GSH/GSSG ratio are increasingly recognized as a factor predisposing to oxidative stress, and in consequence, to the development of many diseases. The best documented observations of this kind concern neurodegenerative diseases (Parkinson’s disease, Alzheimer’s disease), diabetes and its related complications, cardiovascular diseases, and cancers [75,76]. During the reduction processes, GSH is oxidized to GSSG by glutathione peroxidase, while the regeneration of the active form of GSH occurs as a result of GSSG reduction by glutathione reductase [77]. Glutathione status in children on a vegetarian diet has not been systematically investigated so far. In a group of adult vegetarians, both lower and the same glutathione concentrations were found compared with omnivores. These studies most often concerned total glutathione or its reduced form and glutathione peroxidase activity [42,60,78,79]. In the present study, we measured the concentrations of both forms of this compound and determined the GSH/GSSG ratio, which is crucial for cell activity and survival. The positive relationship between the reduced and oxidized forms of glutathione that we found, both in the vegetarians and omnivores, may prove the efficient enzymatic regeneration process of this compound in the studied groups of children. The lower GSH and GSSG concentrations in vegetarians may indicate lower GSH/GSSG ratio activity to neutralize emerging free radicals in response to lower total oxidative activity levels in this group compared with omnivores. The reduced concentrations of both forms of glutathione in vegetarians may also potentially be a result of a lower supply of sulfur amino acids, which are precursors of glutathione, in their diet. This is a possibility considering that, in the diet of vegetarians, the percentage of energy from protein was significantly lower than in omnivores, and sulfur amino acids, such as cysteine or methionine, are contained mainly in protein-rich foods such as meat and dairy. However, it should be borne in mind that cereal products, as well as legumes and nuts, included in the vegetarian diet are also a good source of sulfur amino acids. Therefore, in order to assess the status of these amino acids in the body, it would be necessary to determine their serum concentrations. It should also be remembered that the glutathione concentration could have also been affected by polymorphisms of GCLC and GCLM genes encoding the enzyme responsible for its synthesis, i.e., glutamate cysteine ligase (GCL), which we had not analyzed. It is worth noting that, despite significant differences in the concentrations of both forms of glutathione in the groups of children on vegetarian and omnivorous diets, the R-index did not differ significantly. This may indicate a balance of redox processes in both groups. The balance of oxidation and reduction processes in both groups of children may also be supported by the fact that, despite significantly lower TOC and higher TAC in the vegetarian group, the concentrations of these markers in both groups of children were within the normal range. According to the manufacturer’s reference data (instructions of LDN Labor Diagnostika NordGmbH & Co. KG, Nordhorn, Germany), the expected TOC value is <0.35 mmol/L, while TAC—indicating sufficient antioxidant capacity—is >1.3 mmol/L. Similarly, in the study of Drabko and Kowalczyk [80], the average total antioxidant status (TAS) was 1.3 mmol/L in healthy children. Our previous observations showed lower TAS levels in vegetarian children compared with omnivores (1.21 vs. 1.30 mmol/L; $p \leq 0.001$, respectively), but it was also within the physiological range in both groups of children [34]. This difference may be due to the fact that we originally studied a much smaller group of older children, not all of whom were on a vegetarian diet from birth. In a group of adult vegetarians, the total plasma antioxidant activity assessed by FRAP (Ferric-reducing ability of plasma) and TAC tests was not significantly different from that which was observed in the group of omnivores. At the same time, the authors showed both higher and comparable concentrations of lipid and protein peroxidation products in the studied groups [32,79,81]. Vegetarian diets, if unbalanced, can lead to nutritional deficiencies, including anti-inflammatory and antioxidant properties, predisposing vegetarians to oxidative stress. Although in our study we did not find a relationship between the individual components of the diet and the assessed markers, the results indicate the important role played by doctors and dieticians in the care of children fed unconventionally in the context of maintaining the oxidant-antioxidant balance. ## 5. Strengths and Limitations There are few studies assessing the oxidant-antioxidant balance in children following unconventional diets. To the best of our knowledge, we are the first to attempt to simultaneously assess the severity of both oxidation and reduction processes in children on a lacto-ovo-vegetarian diet. We are also the first to use markers such as TOC, TAC, GSH, and GSSG for this purpose and calculate the oxidative stress index (OSI) and the R-index from these. The presented study has several potential limitations. We analyzed relatively small samples, which reduced the power of our results. However, our data are from two groups of prepubertal children who were similar in terms of age, weight, height, and BMI. Assessment of a broader panel of oxidative stress markers could be considered; however, the markers we assessed seem to be particularly useful for evaluating the efficiency of the oxidant-antioxidant mechanisms, and thus the predisposition to free radical complications. The factors that may influence the severity of oxidative stress, including the development of potential oxidative stress-related complications, include not only the type of diet and its balance, but also its duration—as shown in studies on adults. Cho and Park [45] showed that a long-term vegetarian diet, i.e., for at least 15 years, had a positive effect on the total antioxidant status assessed on the basis of biological antioxidant potential (BAP) and endogenous antioxidant enzyme levels, such as superoxide dismutase, catalase, and glutathione peroxidase. In the studied group of children, the average duration of the vegetarian diet was only about six years, which could be considered another limitation. Nevertheless, this is the only study assessing the severity of oxidative stress in children on a lacto-ovo-vegetarian diet for so long. Another limitation of our work may be the fact that we assessed the content of vitamins with antioxidant properties in the children’s diets, but we did not determine the concentrations of these vitamins in serum. We consider this to be our pilot study. We plan to conduct this kind of observation while taking into account a broader panel of oxidative stress markers with a determination of serum antioxidant vitamin levels, not only in a larger group of children, but also in those following a vegetarian model of nutrition for a longer period of time. Taking into account that such children remain under the observation of our outpatient gastroenterology clinic from birth until adulthood, i.e., until the age of 18, it will be possible to conduct this type of study. ## 6. Conclusions Our study results suggest that the vegetarian model of nutrition in children under regular medical and dietary care allows to maintain the oxidant-antioxidant balance in the serum of these children. Further studies with a larger number of subjects and with a longer follow-up period are needed to confirm our observations and to answer the question of whether a vegetarian diet will maintain the oxidant-antioxidant balance in these children in the future and whether it will influence the parameters of their somatic development and the risk of developing selected metabolic and cardiovascular diseases. ## References 1. Melina V., Craig W., Levin S.. Position of the Academy of Nutrition and Dietetics: Vegetarian Diets. J. Acad. Nutr. Diet. 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--- title: Choroidal structure investigated by choroidal vascularity index in patients with inherited retinal diseases authors: - Kia Bayat - Kiana Hassanpour - Hamideh Sabbaghi - Sahba Fekri - Narsis Daftarian - Tahmineh Motevasseli - Fatemeh Suri - Bahareh Kheiri - Mehdi Yaseri - Hamid Ahmadieh journal: International Journal of Retina and Vitreous year: 2023 pmcid: PMC10044756 doi: 10.1186/s40942-023-00457-w license: CC BY 4.0 --- # Choroidal structure investigated by choroidal vascularity index in patients with inherited retinal diseases ## Abstract ### Purpose To evaluate the choroidal structure in patients with inherited retinal diseases (IRDs) by investigating the choroidal vascularity index (CVI). ### Methods The present study was conducted on 113 IRD patients and 113 sex- and age-matched healthy individuals. Patients’ data was extracted from the Iranian National Registry for IRDs (IRDReg®). Total choroidal area (TCA) was determined between retinal pigment epithelium and choroid-scleral junction,1500 microns on either side of the fovea. Luminal area (LA) was considered as the black area corresponding to the choroidal vascular spaces, following Niblack binarization. CVI was calculated as the ratio of the LA to the TCA. CVI and other parameters were compared among different types of IRD and the control group. ### Results The IRD diagnosis included retinitis pigmentosa ($$n = 69$$), cone-rod dystrophy ($$n = 15$$), Usher syndrome ($$n = 15$$), Leber congenital amaurosis ($$n = 9$$), and Stargardt disease ($$n = 5$$). Sixty-one ($54.0\%$) individuals of each of the study and control groups were male. The average CVI was 0.65 ± 0.06 in the IRD patients and 0.70 ± 0.06 in the control group ($P \leq 0.001$). Accordingly, the average of TCA and LA were 2.32 ± 0.63 and 1.52 ± 0.44 mm [1] in patients with IRDs, respectively. The measurements for the TCA and the LA were significantly lower in all subtypes of IRD (P-values < 0.05). ### Conclusion CVI is significantly lower in patients with IRD than in healthy age-matched individuals. Choroidal changes in IRDs may be related to the changes in the lumen of the choroidal vessels rather than the stromal changes. ### Supplementary Information The online version contains supplementary material available at 10.1186/s40942-023-00457-w. ## Introduction Inherited retinal diseases (IRDs) are a heterogeneous group of retinal disorders associated with progressive deterioration of the photoreceptors’ function. 1With the prevalence of 1 in every 3,000 individuals, IRDs are the most prevalent hereditary cause of severe visual impairment in children and the working-age population, which may progress eventually to irreversible complete sight loss [1–3]. Findings in clinical settings have been proposed for the diagnosis of IRDs. However, there is an overlap of common signs and symptoms such as severely decreased central vision, visual field defect, nyctalopia, nystagmus, and even the presence of normal-looking fundus among multiple subtypes of IRDs. Therefore, clinical findings are not a reliable method for the precise identification of different subtypes [2, 4]. Electrophysiological testing and multiple retinal imaging modalities such as optical coherence tomography (OCT), color fundus photography, and autofluorescence imaging (AF) have been acknowledged as proper techniques for detecting IRDs [2, 5–7]. Even though IRDs basically involve the dysfunction of photoreceptors and retinal pigment epithelium (RPE), recent studies have reported alterations of choroidal structure in these diseases [8–11]. Hence, quantitative assessment of both vascular and stromal components of the choroidal structure can be a notable tool for diagnosing IRDs and distinguishing different subtypes of these retinal disorders. Choroidal thickness has been considered a substantial marker for detecting changes in choroidal structure on OCT images of IRD patients. Recent studies have discovered thinning of choroid in IRD cases [12–14]. EDI-OCT however, in reporting choroidal consistency, is incapable of discriminating between stromal versus vascular changes of choroidal structure [15–19]. Choroidal vascularity index (CVI) is a novel OCT-based index which is defined as the proportion of vascular areas to that of the total choroidal area. It has already been employed to investigate choroidal changes in retinal vein occlusion [20], diabetic retinopathy [21] and age-related macular degeneration (AMD) [22]. In this study, we aimed to employ CVI for detecting choroidal changes in retinal dystrophies and also for distinguishing different subtypes of IRDs. ## Methods In this cross-sectional study, the clinical and imaging data of the right eyes of 113 patients with IRDs were extracted from the Iranian National Registry for IRDs (IRDReg®) [22]. In addition, 113 sex- and age-matched healthy individuals were enrolled in the study as the control group. Written informed consent was obtained from all subjects. All study procedures adhered to the tenets of the Declaration of Helsinki. The study was approved by the Ethics Committee at the Ophthalmic Research Center, Research Institute for Ophthalmology and Vision Science with the code number: IR.SBMU.ORC.REC.1396.15. Patients were examined at Labbafinejad Medical Center, a tertiary referral center in Tehran, Iran. Diagnosis of IRD variations was made by a board-certified retina specialist after evaluation of clinical findings and electroretinogram (ERGs) results. Patients underwent genetic testing to confirm the diagnosis [23]. Demographic data and baseline clinical information including the present age and the age at disease onset, gender, past medical history and visual symptoms such as visual field defects and decreased central vision were recorded. All individuals underwent a complete ophthalmologic examination including best-corrected visual acuity (BCVA) measured by the Snellen chart, color vision evaluated by Ishihara pseudoisochromatic 38-plates, slit-lamp examination of the anterior segment, intraocular pressure (IOP) measured by a Goldmann applanation tonometer, dilated fundus examination performed by + 78 diopter (D)/+90 D lenses and indirect ophthalmoscopy. Exclusion criteria for this study were severe cystoid macular edema, history of intraocular surgery, presence of other retinal diseases such as diabetic retinopathy and AMD, mature cataract, glaucoma and myopic refractive errors greater than − 6.0 diopters. Patients with a confirmed diagnosis of systemic hypertension or diabetes were also excluded. Images with poor quality and images with unidentifiable center of the macula were excluded as well. Choroidal images were provided by the EDI-OCT (Heidelberg Spectralis System, Heidelberg Engineering, Heidelberg, Germany). To minimize the effect of diurnal variations of choroidal structure, EDI-OCT images were obtained from 15:00 to 17:00. EDI-OCT images were analyzed by ImageJ software (version 1.53; National Institutes of Health, USA; http://imagej.nih.gov/ij/). Processing of images was carried out by adopting the protocol reported by Sonoda et al [24]. The total choroidal area (TCA) was determined by the “Polygon Selection” tool as the area between the basal margin of the RPE and the choroid-scleral junction with a width of 1500 μm toward the temporal and nasal sides each, where the fovea was at the center. For measurement of the choroidal luminal area (LA), three choroidal vessels with lumens larger than 100 μm were selected by the “Oval Selection” tool. To keep the image noise at the least possible level, the average brightness of the luminal areas chosen in the previous step was calculated by the “Measure” tool and subsequently was set as the minimum value of the image reflectivity in the “Brightness/Contrast” tool. To make the binarization process possible, the type of image was downgraded to an 8-bit image. Binarization was then performed using the Niblack method of the “Auto Local Threshold” tool. The binarized image was converted back to a red green blue (RGB) image; a “color threshold tool” was applied to specify luminal areas or dark pixels from the stromal areas or light pixels. LA was measured by the “analyze-measure tool”. CVI was calculated as the ratio of LA to TCA, and the stromal area (SA) was computed by the subtraction of LA from TCA (Fig. 1). Fig. 1Enhanced depth imaging (EDI) optical coherence tomography images of a patient with RP. The light areas in choroid are the stromal areas and the dark areas are the luminal areas A. the total choroidal area (TCA) was determined as the area between RPE and the choroid-sclera junction in the subfoveal choroid. The examined area was set to be 3000 μm wide B, C. the average brightness of the three choroidal vessels with lumens larger than 100 μm was set as the minimum value of the image reflectivity in brightness/contrast tool D. the image was converted to a binary image using Niblack auto local threshold tool E, F. color threshold tool was applied to specify luminal areas from stromal areas ## Statistical analysis To describe the data, mean, frequency and standard deviation, median and interquartile range were used. Choroidal parameters were compared between cases and controls by Mann-Whitney test. The differences among IRDs groups and controls were investigated by Kruskal-Wallis test, then comparisons between levels of IRDs groups and controls were performed. In this evaluation, multiple comparisons were considered by Bonferroni method. Although the groups were matched for age and sex, we adjusted the effect of age and sex for possible residual confounding effects by general linear model. Correlation analysis was performed using Spearman correlation coefficient. SPSS (IBM Corp. Released 2019. IBM SPSS Statistics for Windows, Version 26.0. Armonk, NY: IBM Corp) was used to analyze the data. P-value less than 0 < 05 was considered statistically significant. ## Results A total of 113 patients with IRD, including 69 with retinitis pigmentosa (RP), 15 with cone-rod dystrophy (CRD), 15 with Usher syndrome, 9 with Leber congenital amaurosis (LCA) and 5 with Stargardt disease (STGD) were recruited. In addition, 113 healthy individuals were enrolled. Sixty-one ($54.0\%$) individuals were male in each of the IRD and control groups (Table 1). The patients were very genetically heterogenous and we did not find any association between the causative genes and the choroidal structure (data not shown). Table 1Demographic characteristics of study groupsGroupsControlIRDsP-ValueNumber of subjects113113Age in years≤ 204921–40475441–60334361–8047Overall (Mean ± SD)36.89 ± 11.8738.55 ± 13.620.72GenderFemale [No. (%)] 52 (46.0)52 (46.0)1Male [No. (%)] 61 (54.0)61 (54.0)IRD: Inherited retinal disease; Based on Mann-whitney; ** Based on chi-square The average CVI was 0.65 ± 0.06 in the IRD patients and 0.70 ± 0.06 in the control group ($P \leq 0.001$). Accordingly, the average of TCA and LA was 2.32 ± 0.63 mm2 and 1.52 ± 0.44 mm [1] in patients with IRDs, respectively. The corresponding values for the control group were 2.68 ± 0.6 mm2 and 1.85 ± 0.36 mm [1]. In the univariate analysis, the TCA and LA measurements were significantly lower in the IRD patients as compared with the control group (all Ps < 0.001); however, there was no statistically significant difference in terms of the stromal area (SA) between cases and controls (0.8 ± 0.24 mm [1] vs. 0.83 ± 0.3 mm [1], $$P \leq 0.47$$). Multivariable analysis revealed statistically significant difference between cases and controls in terms of all of the indexes, including TCA, LA, CVI (P-values < 0.001, < 0.001, and < 0.001, respectively) after adjusting for age and sex (Table 2). Table 2Comparison of choroidal parameters among IRD groups and healthy controlsRPSTGDCone- Rod DystrophyLCAUsher SyndromeControlP-valueTotal Choroidal Area2.24 ± 0.532.23 ± 0.352.29 ± 0.742.53 ± 0.972.62 ± 0.612.49 ± 0.5< 0.001Statistically significant different groups (P-value)Control (< 0.001)RP (< 0.001)Luminal Area1.46 ± 0.381.44 ± 0.241.43 ± 0.51.57 ± 0.621.82 ± 0.461.78 ± 0.35< 0.001Statistically significant different groups (P-value)Control (< 0.001)Usher Syndrome($$P \leq 0.045$$)Control (0.015)Usher Syndrome($$P \leq 0.045$$)RP (< 0.001)CRD (0.015)CVI0.65 ± 0.060.65 ± 0.060.62 ± 0.040.62 ± 0.040.69 ± 0.040.72 ± 0.04< 0.001Statistically significant different groups (P-value)Control (< 0.001)Control (< 0.001)Usher (0.005)Control (0.003)Usher (0.045)CRD (0.005)LCA (0.045)RP (< 0.001)CRD (< 0.001)LCA (0.003)Stromal Area0.77 ± 0.220.79 ± 0.210.86 ± 0.250.96 ± 0.390.8 ± 0.20.7 ± 0.20.77 IRD: Inherited Retinal Disease; RP: Retinitis Pigmentosa; STGD: Stargardt Disease; LCA: Leber Congenital Amaurosis; CVI: Choroidal Vascularity Index; CRD: Cone-rod dystrophy; Based on general linear model; ** multiple comparisons considered by Bonferroni method. Adjusted P values have been reported ## Correlation analysis Regarding the correlation of age and indexes, TCA, LA, and SA correlated with age ($$P \leq 0.003$$, 0.004, and 0.018, respectively; Spearman correlation coefficients were − 0.215, -0.232 and − 0.138). ( Table 3) However, CVI and age were not correlated (Spearman correlation coefficient and P-value were − 0.058 and 0.531, respectively). Table 3Correlation between choroidal parameters and ageTCALACVISATotal ThicknessSpearman Correlation-0.215-0.232-0.045-0.138-0.433P-value0.0020.0010.530.0510.001TCA: Total choroidal area, LA: Luminal area; CVI: choroidal vascularity index; SA: Stromal area ## Comparisons among different IRDs There were statistically significant differences between RP vs. controls, CRD vs. Usher syndrome, CRD vs. controls, LCA vs. Usher syndrome, and LCA vs. controls in terms of the CVI. ( Adjusted P-values: <0.001, 0.005, < 0.001, 0.045, and 0.003, respectively). There was a significant difference between RP vs. controls regarding TCA ($P \leq 0.001$). There was also a significant difference between RP vs. controls ($P \leq 0.001$), and CRD vs. controls in terms of LA ($$P \leq 0.015$$). There was no significant difference between subgroups of IRDs and healthy individuals regarding SA (Table 2; Fig. 2). CVI was comparable between RP and STGD (P: >0.99). Fig. 2Total choroidal stroma (TCA), Choroidal Vascularity Index (CVI), Luminal Area (LA), and Stromal Area (SA) are demonstrated in different subtypes of inherited retinal dystrophies. ( A-D) ## Discussion In this study, univariate and multivariable analysis indicated lower amounts of CVI, TCA, and LA in patients with IRDs than in the age- and sex-matched controls. The univariate analysis did not reveal a statistically significant difference between cases and controls regarding SA. However, patients with IRDs had a significantly lower SA than healthy individuals after adjusting for age and sex. We also found a correlation between age and three studied indices, including TCA, LA, and SA. Reduced amounts of all three indices were seen with increasing age. Our analysis did not reveal a significant correlation between age and CVI. To the best of our knowledge, the present study is the largest one investigating CVI in patients with genetically confirmed diagnosis of IRD. Comparing CVI between IRD subgroups and controls, RP, LCA, and CRD had significantly lower values compared with the control group. There was also a significant difference between Usher and LCA and also between Usher and CRD. CVI in Usher was significantly greater than LCA and CRD. Our results correlate with previous studies investigating CVI in patients with IRDs. RP followed by STGD are the most studied dystrophies in which CVI consistently shows reduction [8, 25, 26]. Wei and associates [8] investigated CVI in 17 patients with RP, four patients with STGD, and three patients with CRD as compared to healthy controls. The authors noticed lower mean CVI in IRDs than in controls. Our results are consistent with Wei et al’ s study, while there is a larger sample size in each subgroup in the current study. Ratra and colleagues investigated CVI in 39 patients with STGD as compared to the healthy controls. CVI was significantly decreased in patients with STGD. The authors concluded that CVI is a more robust tool than the subfoveal choroidal thickness (SFCT) measurement to evaluate the choroidal structure [25]. In the present study, CVI was lower in patients with STGD as compared with controls; however, this difference did not reach statistical significance possibly due to the small sample size of the STGD group. There are several mechanisms explaining the reduced CVI in patients with IRD. First, the choroidal vessels are responsible for the blood supply of RPE and outer retina. In most IRDs, the primary pathology comprises atrophy of the RPE and the outer retina. The reduction in choroidal vasculature could be viewed as a primary pathology or an autoregulatory consequence of RPE and outer retina attenuation. This needs to be evaluated in future studies. The luminal changes were more prominent than the stromal changes in IRD patients. This finding correlates with previous studies investigating choroidal vasculature in these patients. Despite stability of the stromal area, the changes in the lumen were prominent. This finding supports the possible changes that occur in choroidal vessels in patients with IRDs. Changes in choroid are well-documented in patients with RP. Choroidal thinning has been shown in histopathological studies and OCT [14]. Additionally, magnetic resonance imaging, Doppler, and laser speckle flowgraphy have demonstrated lower choroidal blood flow in RP patients [27]. Comparing the subgroups of the IRDs, CVI was reduced in RP, CRD, LCA, and Usher when also compared to the healthy controls. Of note, CVI was comparable between RP and STGD. Similarly, Hanumunthada et al. found no significant difference in CVI when compared between PR and STGD [28]. This finding may confirm that choroidal changes are secondary to RPE changes in patients with IRD rather than a primary alteration. The more prominent changes in LCA and CRD patients when compared to Usher syndrome and healthy controls could be attributed to the earlier onset of the disease in LCA and CRD and also the severe involvement of photoreceptors. The current study has some limitations including the small sample size in some groups such as LCA and STGD subtypes. However, this study has definite strengths such as comparison of IRD patients with age- and sex-matched control groups and also evaluation of different subgroups of IRD. In conclusion, patients with IRD show changes in choroidal structure. These changes cause reduced choroidal vascularity index as a novel marker to investigate the choroid. In general, changes in choroidal luminal areas are more prominent than the stromal areas. More severe diseases with an earlier onset such as LCA and CRD may result in more prominent changes in the CVI. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. Gill JS, Georgiou M, Kalitzeos A. Progressive cone and cone-rod dystrophies: clinical features, molecular genetics and prospects for therapy. Br J Ophthalmol (2019.0) 103 711-20. DOI: 10.1136/bjophthalmol-2018-313278 2. Bessant DA, Ali RR, Bhattacharya SS. 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--- title: Molecular hydrogen promotes wound healing by inducing early epidermal stem cell proliferation and extracellular matrix deposition authors: - Pengxiang Zhao - Zheng Dang - Mengyu Liu - Dazhi Guo - Ruiliu Luo - Mingzi Zhang - Fei Xie - Xujuan Zhang - Youbin Wang - Shuyi Pan - Xuemei Ma journal: Inflammation and Regeneration year: 2023 pmcid: PMC10044764 doi: 10.1186/s41232-023-00271-9 license: CC BY 4.0 --- # Molecular hydrogen promotes wound healing by inducing early epidermal stem cell proliferation and extracellular matrix deposition ## Abstract ### Background Despite progress in developing wound care strategies, there is currently no treatment that promotes the self-tissue repair capabilities. H2 has been shown to effectively protect cells and tissues from oxidative and inflammatory damage. While comprehensive effects and how H2 functions in wound healing remains unknown, especially for the link between H2 and extracellular matrix (ECM) deposition and epidermal stem cells (EpSCs) activation. ### Methods Here, we established a cutaneous aseptic wound model and applied a high concentration of H2 ($66\%$ H2) in a treatment chamber. Molecular mechanisms and the effects of healing were evaluated by gene functional enrichment analysis, digital spatial profiler analysis, blood perfusion/oxygen detection assay, in vitro tube formation assay, enzyme-linked immunosorbent assay, immunofluorescent staining, non-targeted metabonomic analysis, flow cytometry, transmission electron microscope, and live-cell imaging. ### Results We revealed that a high concentration of H2 ($66\%$ H2) greatly increased the healing rate (3 times higher than the control group) on day 11 post-wounding. The effect was not dependent on O2 or anti-reactive oxygen species functions. Histological and cellular experiments proved the fast re-epithelialization in the H2 group. ECM components early (3 days post-wounding) deposition were found in the H2 group of the proximal wound, especially for the dermal col-I, epidermal col-III, and dermis-epidermis-junction col-XVII. H2 accelerated early autologous EpSCs proliferation (1–2 days in advance) and then differentiation into myoepithelial cells. These epidermal myoepithelial cells could further contribute to ECM deposition. Other beneficial outcomes include sustained moist healing, greater vascularization, less T-helper-1 and T-helper-17 cell-related systemic inflammation, and better tissue remodelling. ### Conclusion We have discovered a novel pattern of wound healing induced by molecular hydrogen treatment. This is the first time to reveal the direct link between H2 and ECM deposition and EpSCs activation. These H2-induced multiple advantages in healing may be related to the enhancement of cell viability in various cells and the maintenance of mitochondrial functions at a basic level in the biological processes of life. ### Supplementary Information The online version contains supplementary material available at 10.1186/s41232-023-00271-9. ## Introduction Wound healing in adult mammals generally involves four major processes [1–4]: haemostasis, inflammation, proliferation, and remodelling—which may leave a scar [5, 6]. Failure at any of these stages could lead to acute or chronic wound repair disorders [7]. In evaluating wound healing, the acronym “TIME” (Tissue, Infection/Inflammation, Moisture balance, and Edge of wound) has been used to describe a favourable wound-bed microenvironment [8, 9]. Throughout the dynamic healing process, rapid re-epithelialization is mediated by epidermal stem cells (EpSCs) and the extracellular matrix (ECM) to restore the skin barrier [10–12]. EPSCs can be activated and recruited from various tissues [13], such as hair follicles (HFs), interfollicular epidermis (IFE), and bone marrow [14–17], thus replenishing the keratinocytes at the wound site and facilitating wound closure [18, 19]. Furthermore, ECM molecules can stimulate keratinocyte migration from the edges of the wound and nearby tissues to the wound bed [20, 21]. In addition to serving as a scaffold [22], the ECM is instrumental in creating a favourable environment for skin progenitor cells [23]. This is largely due to collagen in the ECM [24], which is synthesized by fibroblasts, epithelial cells [25], and keratinocytes [12], and constitutes a basis for translational wound-repair medications and therapies [26–29]. However, the process of healing is multifaceted, and improvements in wound healing, including safer and more efficient wound-healing methods, are needed. Wound healing processes involve the spatial and temporal synchronization of a variety of cell types with distinct roles in all phases [30]. During these, the reactive oxygen species (ROS) gradient is considered as one of the first signals that activate the cellular response and also crucial to regulate several other phases of healing processes [31]. While key factors that promote wound healing also include avoiding the tissue damage caused by the overproduction of ROS and activating the functions of tissue repair. Hydrogen gas has been reported by Ohsawa et al. [ 32] in 2007 to be an efficient antioxidant in protecting rats brain from ischemia-reperfusion injury. Other studies added evidence of H2 therapy in massive oxidative damage and inflammatory diseases [33–36]. Latest researches suggest that hydrogen can also be beneficial on skin injury, for example, H2 has been shown to effectively improve the damage repair of cutaneous wound [37], burn wound [38], pressure ulcer [39, 40], diabetic wounds [41, 42], radiation injury [43], psoriasis damage [44], and oral-wound [45]. Current explanations for hydrogen promoting wound healing have been mostly focused on its ability of anti-inflammatory, anti-oxidative stress and reacting with cytotoxic ROS. Few studies indicated that H2 may function in stem cell viability and collagen synthesis. Kawasaki et al. [ 46] suggested hydrogen gas prolonged replicative lifespan of bone marrow cells in vitro, and Zhang et al. [ 47] pointed out that hydrogen protected hematopoietic stem cell from radiation injury by reducing hydroxyl radical. In a study of pressure ulcer, masson staining was used to prove hydrogen inhalation induced collagen synthesis [40], another study of diabetic wound, topical application of H2 was revealed to induce Col-1 [42]. More comprehensive and deep research of hydrogen on wound healing are still needed, and many questions remains to be answered, such as how H2 functions on different types of cells especially on stem cells activities? What types of collagens and other ECM are synthesized under H2 treatment? Is this hydrogen-promoted wound healing dose-dependent? In the present study, by using a full-thickness dorsal-skin defect mouse model, we revealed that daily treatment in an high concentration H2 ($66\%$ H2 + $33\%$ O2) chamber induced a novel pattern of wound healing with less inflammation and better angiogenesis, faster cell migration, mitochondrial function maintenance, and less scab formation. H2 treatment facilitated early ECM deposition and EpSCs activation, thus providing a simple and effective approach to improve wound healing (mainly due to the high concentration of H2). H2-releasing dressing received a relevant effect in cutaneous wound healing. Therefore, H2 treatment may potentially provide a novel strategy different from the traditional pattern of wound care, with a vast range of applications in clinical postoperative treatment. ## Cell culture Human embryonic skin fibroblasts CCC-ESF-1 (ESF) (generation 10, G10), human embryonic lung fibroblast CCC-HPF-1 (HPF) (G10), human immortal keratinocyte line (HaCaT) (G8), and human Umbilical Vein Endothelial Cells (HUVECs) (G3) were obtained from the National Infrastructure of Cell Line Resource Center, Beijing, China. Primary mesenchymal stem cells derived from newborn umbilical cords (Human Umbilical Cord Primary Mesenchymal Stem Cells (HUCP-MSCs)) were donated by Beijing Obstetrics and Gynecology Hospital. Cells were routinely cultured at $5\%$ CO2 and 37 °C in DMEM medium (for ESF, HPF, HaCaT, and HUVEC) (Gibco, NY, USA) supplemented with $10\%$ fetal calf serum (Gibco, NY, USA) and $1\%$ penicillin and streptomycin (Gibco, NY, USA). ## Animal experiments All animal studies were carried out according to protocols approved by the Committee on Ethics of Biomedicine Research, the Sixth Medical Center, PLAGH, China, and all procedures were conducted in accordance with the Regulations for the Administration of Affairs Concerning Experimental Animals (China). Male C57BL/6J mice (7 weeks old, 20–24 g) were purchased from Vital River Laboratory Animal Technology Co., Ltd. (Beijing, China). Animals were maintained under standard conditions at 22 °C to 25 °C with a 12-h light/dark cycle and were fed a normal diet. Oral enrofloxacin (0.17 mg/mL) intake was administered in daily drinking water, and the antibiotic was prepared with chlorinate-free and acid-free water. For the NAC treatment group, 3.3 mg NAC (the ROS scavenger acetylcysteine; #S1623, Selleckchem, China) in 260 μL distilled water was injected in each mouse with 22 (± 2) g of body weight. ## Full-thickness cutaneous wound healing model The full-thickness cutaneous wound healing model was established according to the Murine Model of Wound Healing [48] with some modifications (Fig. 1A). Briefly, mice were anesthetized with tribromoethyl alcohol (20 mg/mL, 10 μL/g injection), and the hair was shaved and cleaned with $70\%$ ethanol. We split the back skin with a ring of medical silica gel membrane (to prevent the wound closure due to loose skin of mice), and then seamed it well with medical thread and removed the center skin to create 10 mm full-thickness excisional dorsal skin wounds using a sterile scissor. Wound areas were calculated using ImageJ software, and the wound closure percentage was obtained as follows:Fig. 1Persistent high concentration of H2 significantly accelerated cutaneous wound healing, blood perfusion and vessel formation. A Left: connection between each module in the H2 chamber system. Middle: Model of the full-thickness cutaneous wound in mouse backs; inside circle = original wound, outside circle = sampling boundary. Right: Scheme of a wound section after injury. B Timeline of animal experiments and daily H2 treatment (or under other conditions). Mice underwent surgery for 30 min and then immediately put in H2 chamber for 1 h. Thereafter, they were treated with H2 (or other conditions) every 24 h for 1 h until sacrifice. C Capture of wound area every two days after modeling in different persistent conditions ($$n = 5$$ for each group). D Comparison of wound closure percentage after modeling (persistent daily treatments). E H&E staining and Masson staining showing the tissue remodeling 11 days post-wounding (persistent daily treatments). F Treatment was applied for the first 3 days only, and the images of wound areas were captured every 2 days after modeling (days 0–9). G Comparison of wound closure percentage after modeling among the five groups (treatment for the first 3 days). H Blood flow perfusion 0–11 days post-wounding in the $66\%$ H2, $5\%$ H2, and Control groups. I Quantification of blood perfusion, SO2, tHb, and HHb in wound area among the three groups. J, K Representative immunofluorescence images and quantification for CD31+ (green) tube formation in dermal wounds of at day 11 post-wounding. L, M Panoramic scanning and the quantification of wound edge tube formation at the leading edge (L, 0–1 mm from wound site), mid-end (M, 1–2 mm from wound site) and distal (D, 2–3 mm from wound site) areas of the wound at day 3 post-wounding. Yellow dotted line indicates the boundary between the epithelium and dermis. N, O Representative microscopic images and the quantification of in vitro blood vessel formation of Human Umbilical Vein Endothelial Cells (HUVEC) at 12 h after different treatments. Red hatched line outlines the newly formed tubes. P GSEA Top-5 tube formation-related GO-bp enrichment plots showed the H2 accelerated tube formation (2 days earlier than the controls). Data in D, G, and I were analyzed by two-way ANOVA test, and data in K, M, and O were processed unpaired t test. All of the data are plotted as Mean ± SEM. * P value < 0.05; P value < 0.01; P value < 0.001; no stars for P value > 0.05; in D indicates a significant difference between the $66\%$ H2 and control group, the $5\%$ H2 and control group, separately; * in G indicates a significant difference between the $66\%$ H2 and control group. Scale bar = 100 μm. Black dotted line in E indicates the boundary between the epithelium and dermis. Arrow in A (right) indicates the epithelial tongue; black arrowheads in E indicate blood capillaries in the dermal layer of the wound. b, basal layer; d, dermis; he, hypertrophic epidermal wound edge; hf, hair follicles; ife, interfollicular epithelium; s, scab; sm, smooth muscle; wb, wound bed \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\textrm{Wound}\ \textrm{closure}\ \textrm{percentage}\ \left(\%\right)=\frac{\textrm{wound}\ \textrm{area}\ \textrm{on}\ \textrm{day}\ 0-\textrm{wound}\ \textrm{area}\ \textrm{on}\ \textrm{day}\ \textrm{n}}{\textrm{wound}\ \textrm{area}\ \textrm{on}\ \textrm{day}\ 0}\times 100\%$$\end{document}Woundclosurepercentage%=woundareaonday0-woundareaondaynwoundareaonday0×$100\%$ ## Muscle contusion model We developed a muscle contusion model as stated in the reference [49] to explore the role of hydrogen in muscle injury repair. Male C57/BJ mice aged 6–8 weeks were used. The detailed method is to drop a 25-g weight from a height of 60 cm to the inner surface of the gastrocnemius muscle (Figure S9A). This modeling method has moderate intensity and will not cause skeletal injury or gait abnormality. ## H2 chamber treatment A transparent closed box (20 cm × 18 cm × 15 cm) was connected to a hydrogen generator (KLE-H7, Kelieng Biomedical Co. Ltd., Shenzhen, China) (Fig. 1A), which produces $66\%$ H2 and $33\%$ O2 (V/V), $5\%$ H2 (V/V) mixed with air, or $33\%$ O2 (V/V) mixed with air. Hydrogen treatment was given immediately after establishment of the mouse cutaneous wound healing establishment, and then daily administration was given until the end of the experiment. Animals were placed in the box with the mixed air for 1 h each day. During this inhalation period, mice were awake and freely moving. Thermal trace GC ultra-gas chromatography (Thermo Fisher, MA, USA) was used to monitor the concentration of hydrogen gas in the closed box. ## H2 rich medium preparation H2 rich medium was produced by injecting gas using the same hydrogen generator used for H2 Chamber treatment (KLE-H7, Kelieng Biomedical Co. Ltd., Shenzhen, China) into DMEM medium (Gibco) for 30 min hydrogen dissolved in reaction solution was detected with a needle-type H2 sensor (Unisense, Aarhus N, Denmark). To measure the H2 concentration in the reaction system, the sensor was inserted below the liquid surface. ## Blood perfusion and blood oxygen detection Blood perfusion at the wound site during the healing processes was detected by using a moorFLPI-2 (Moor Instruments Limited, UK) according to manufacturer instructions. Images were further selected from videos and analyzed by moorFLPIReview V50 software (Moor Instruments Limited, UK). Blood oxygen levels were tested by the moorVMS-OXY Tissue Oxygen Monitor (Moor Instruments Limited, UK) according to the instructions. Five sites (top, bottom, left, right, and middle) of the wound area were chosen, and an average was calculated to create the oxygen kinetic curve. ## In vitro tube formation assay Tube formation assay was performed as described [50]. Firstly, Matrigel was thawed at 4 °C overnight to avoid bubble formation. Briefly, 15-well μ-Slides (ibidi, Germany) were coated with 10 μl of Matrigel, which was allowed to solidify at 37 °C. HUVECs before generation 10 were harvested and the cell number and viability were determined by Trypan blue staining before seeding. A cell suspension was prepared in DMEM and 15,000 cells/well were seeded in 50 μl medium on top of the matrigel. HUVECs were seeded in conditioned medium for 6 h, and the enclosed networks of complete tubes were counted and photographed under an inverted microscope. The tubular loops of the cells were measured and calculated for each well. ## Digital Spatial Profiler and bulk RNA-seq analysis Bulk RNA-seq was processed by Anoroad Inc. (Beijing, China) and the Digital Spatial Profiler assay was performed by Capital Bio Technology Inc. (Beijing, China). ## Tissue immunostaining Skin tissue at the wound site was harvested and fixed with formalin and embedded in paraffin blocks, and then 4 μm thick paraffin sections were mounted on glass slides for histological staining. IHC and IF of the paraffin-embedded tissue sections were performed as previously indicated [51]. Briefly, sections were dewaxed and rehydrated by subsequent immersion in xylene, ethanol ($100\%$, $95\%$, $70\%$, and $50\%$) and deionized H2O. Antigen was then retrieved in citrate buffer, and non-specific staining between the primary antibodies (Table S1A) and the tissue was blocked by incubation in $1\%$ goat serum in PBS for 60 min at RT. The sections were incubated with the primary antibodies listed in the Table S8A at 37 °C for 60 min for IHC or at 4 °C overnight for IF. Further labeling with specific secondary antibodies for IHC or IF (Table S1B) was performed according to the manufacturer’s instructions. For IF, nuclei were stained with NucBlue Live cell stain (R37605, Invitrogen). DAB, hematoxylin and neutral balsam mounting reagent used in IHC processes were obtained from ZSGB-BIO (Beijing, China). ## Cells immunofluorescence staining Cells suspensions in a 96-well plates at 10,000 cells/well were allowed to adhere for 24 h and then switched to full medium. Wells containing the seeded cells were washed with 1× PBS immediately at the end of the incubation times and fixed in $4\%$ paraformaldehyde for 30 min. Subsequent to the fixation, the cells were permeabilized with $0.3\%$ PBS-Tween for 15 min, washed, and then blocked with normal sheep serum blocking buffer (ZSGB-BIO, China) for at least 1 h at room temperature. Primary antibodies (Table S1A) were added at the dilution recommended by the manufacturers and were incubated overnight at 4 °C. Appropriate fluorescent dye-labeled secondary antibodies (Table S1B) were used, and cell nuclei were stained with NucBlue Live cell stain (R37605, Invitrogen). ## Hematoxylin and eosin and Masson trichrome staining The protocol described by Fischer et al was used for the Hematoxylin and eosin (H&E) staining [52], while Masson trichrome staining was performed in strict accordance with the manufacturer’s protocol (Masson Stain Kit (60532ES58), Yeasen, China). ## Flow cytometric staining For analysis of Th1, Th2, Treg, and Th17 cells, single-cell suspensions were prepared from the spleens of mice 24 h after wounding. The cells were labeled with CD8-APC, CD4-FITC, and CD3-PECy5. For T-bet and GATA-3, protein amounts were normalized, and cell surface staining was performed using APC-conjugated anti-CD4 followed by fixation with 1× Fixation/Permeabilization buffer and intracellular staining with PE-conjugated anti–T-bet and APC–conjugated anti–GATA-3 in 1× permeabilization buffer. Cells were washed in 1× permeabilization buffer and analyzed by flow cytometry (B53009, CytoFLEX Flow Cytometer, Biotek). For the analysis of Treg cells, cell surface staining was performed using FITC-conjugated anti-CD4, PE-conjugated anti-CD25, PB450-conjugated anti-CD127 and appropriate isotype controls. Cells were incubated with antibodies for 20 min at room temperature in the dark, followed by washing in phosphate buffered solution (PBS) and analysis by flow cytometry. For the analysis of Th17 cells, cells were stained with FITC anti-CD4 for surface expression of CD4, and intracellular cytokine IL-17 was detected by staining with APC-conjugated Anti-IL-17. Finally, cells were analyzed by flow cytometry. All antibodies and reagents were purchased from Biolegend Inc., USA (Table S1). For the identification of MSCs, 8 conjugated antibodies (CD73-PE, CD90-FITC, CD105-Cy5, CD34-FITC, CD45-Cy5, CD79a-PE, CD14-APC, and HLA-DR-APC-Cy7) targeting at the cell surface markers [53] were applied, and the staining and detection methods were consistent with the methods described previously. ## Enzyme-linked immunosorbent assay Murine serum was carefully collected from whole blood and stored in −80°C. Wound edge tissue was harvested after the mice were sacrificed, washed twice in distilled PBS, and cut into small pieces in lysis buffer containing protease inhibitor and 0.1 mM PMSF (Sangon Biotech, Shanghai, China), followed by homogenization and centrifugation (12,000 rpm, 20 min). The supernatant was then collected for further assessment of EGF (MM-0043M), bFGF (MM-0050M1), PDGF (MM-0070M1), and TGF-β1 (MM-0921M) levels using mouse ELISA kits (Jiangsu Meimian industrial, China). Tissue and serum samples underwent multiple cytokines detection by using Bio-plex proTM Mouse Cytokine Th17 Panel A 6-Plex kit (#M6000007NY, Bio Rad, USA). For tissue detection, amounts of target growth factors were normalized to the total amount of whole protein. ## Non-targeted metabolomics analysis The non-targeted metabolomics analysis was performed by IGENECODE Company, Beijing, China. Thermo Scientific™ Dionex™ UltiMate™ 3000 Rapid Separation LC (RSLC) system. UHPLC separation was achieved with reverse phase C18 or hydrophilic interaction liquid chromatography columns. For C18 separation, the mobile phase A was acetonitrile/water ($\frac{60}{40}$) and mobile phase B was isopropanol/ acetonitrile ($\frac{90}{10}$); both A and B contained $0.1\%$ formic acid and 10 mmol/L ammonium acetate. The gradient conditions for reverse phase C18 separation are shown in Table S2. The HSS T3 column (2.1 × 100 mm, 1.8 μm, waters) operated at 45 °C. The flow rate was 300 μL/min, and the injection volume was 1 μL. For HILIC separation, the mobile phase A was acetonitrile, and the mobile phase B was water; both A and B contained $0.1\%$ formic acid and 10 mmol/L ammonium acetate. A BEH Amide column (2.1 × 100 mm, 1.7 μm, waters) was operated at 40 °C. The flow rate was 300 μL/min, and the injection volume was 1 μL (see Table S3). A Thermo Scientific™ Q Exactive™ hybrid quadrupole Orbitrap mass spectrometer equipped with a HESI-II probe was employed. The pos HESI-II spray voltage was 3.7 kV, the heated capillary temperature was 320 °C, the sheath gas pressure was 30 psi, the auxiliary gas setting was 10 psi, and the heated vaporizer temperature was 300 °C. Both the sheath gas and auxiliary gas were nitrogen. The collision gas was also nitrogen at a pressure of 1.5 mTorr. The parameters of the full mass scans were as follows: a resolution of 70,000, an auto gain control target under 1 × 106, a maximum isolation time of 50 ms, and an m/z range 50–1500. The LC-MS system was controlled using Xcalibur 2.2 SP1.48 software (Thermo Fisher Scientific), and data were collected and processed with the same software. All data obtained from the four assays in the two systems in both pos and neg ion modes were processed using Progenesis QI data analysis software (Nonlinear Dynamics, Newcastle, UK) for imputing raw data, peak alignment, picking, and normalization to produce peak intensities for retention time (tR) and m/z data pairs. The ranges of automatic peak picking for the C18 were between 1 and 16 min and between 1 and 12 min, respectively; adduct ions of each “feature” (m/z, tR) were deconvoluted, and these features were identified in the human metabolome database (HMDB) and Lipidmaps. ## Fibroblast movement and migration and keratinocyte cell epithelialization ability test by a live cell imaging system HPF cells were seeded at 5000 cells/well in a 96-well plate with a clear bottom, stained with Actin-GFP (C10506, CellLingt, Invitrogen, USA) and cultured for another 24 h to observe movement. ESF cells were seeded at 20,000 cells/well, scratched by the high throughput scratcher of Cytation 5 (Biotek), and then observed for another 24 h to determine the conditioned medium in fibroblast migration function. Cell movement and migration were scanned using a Cytation 5 Cell Imaging Multi-mode reader (Biotek), and migration was quantified automatically according to the manufacturer’s instructions. HaCaT cells were seeded at 30,000 cells/well and then cultured for another 24 h to observed and calculate the area of colony formation (epithelialization). The images in each small field were captured every hour in 3 × 3 montage frames at × 10 magnification. For cell movement video footage, both bright field and fluorescent channel were used. ## Preparation and evaluation of magnesium based hydrogen storage material dressing Microparticles of magnesium (Mg) balls (average diameter 20 μm) are spread evenly on medical gauze soaked with physiological saline solution. Medical waterproof and breathable polyurethane film was covered on both sides of the medical gauze. Then the “sandwich dressing” was sealed around the edge, and sewed onto the silicone membrane at the back of the wounded animal. For the control group, normal medical gauze soaked with normal saline solution was used without Mg. All the dressings were changed every day. The release of H2 was measured by headspace gas chromatography (GC) (Shimadzu, GCMS-QP2010S). ## Statistical analysis For comparison between two groups, two-tailed paired and unpaired Student’s t tests were performed to calculate P values and determine statistically significant differences (significance was set at $P \leq 0.05$, as detailed in the figure legends). For comparison among more than two groups, ordinary one- or two-way analysis of variance (ANOVA) tests were followed by the appropriate multiple comparison tests (as detailed in the figure legends). All experiments were repeated twice with the same results. All statistical analyses were performed with GraphPad Prism 8 software. ## Hydrogen greatly accelerates cutaneous aseptic wound closure in full-thickness dorsal-skin defect mice To determine whether H2 improves wound healing, we established a cutaneous aseptic wound model using full-thickness dorsal-skin defect mice that were subjected to daily application of H2 in a treatment chamber (Fig. 1A). Sampling region and the scheme of wound were also shown in Fig. 1A. We evaluated the effect of high ($66\%$ H2, $66\%$ H2 + $33\%$ O2) and low ($5\%$ H2; $5\%$ H2 + $21\%$ O2 air) concentrations of H2 on wound healing over an 11-day time course (Fig. 1B), and included control (air) and $33\%$ O2 ($33\%$ O2 in air) mouse groups for comparison. H2 has long been recognized as an antioxidant targeting ROS; therefore, we also added an N-acetyl-L-cysteine (NAC) treatment group to evaluate a possible role of ROS cleavage in wound healing. Our results revealed that the $66\%$ H2 group ($66\%$ H2 + $33\%$ O2) healed faster than any of the other groups. Macroscopic differences appeared on the first day after treatment and became more visible on day 3 (Fig. 1C). Eleven days after wounding, the $66\%$ H2 group displayed a significantly increased wound-closure rate (approximately $90\%$ wound closure for the $66\%$ H2 group; as compared to $70\%$ closure for the $5\%$ H2 group and about $30\%$ closure for the control group; Fig. 1D). Furthermore, the healing rates of the NAC and $33\%$ O2 groups were similar to those of the control group. These results suggest that H2 promotes healing in a dose-dependent manner, and that the healing effect is not dependent on O2 or ROS (Fig. 1C, D). To further visualize the effect of H2 on wound healing, we performed histological analysis of haematoxylin and eosin (H&E) and Masson-stained sections on day 11 (Fig. 1E). The localization of the wounded skin tissue is shown in the schematic diagram (Fig. 1A, right part). The epidermis was more fully formed, and the dermal and epidermal junction (DEJ) appeared intact with more collagen deposition in the $66\%$ H2 group, while a weaker DEJ and partial re-epithelialization were observed in both the NAC and $33\%$ O2 groups. This is despite the effect of NAC in reducing EGF and TGF-β1 growth factor levels at day 3 after wounding (pbFGF < 0.01, pTGF-β1 < 0.05) (Figure S1). Thus, these results verify that neither ROS cleavage nor $33\%$ O2 administration alone were able to promote the wound healing process. Notably, the wound healing pattern in the $66\%$ H2 group appears similar to moist healing, with less blood scab formation and better in tissue remodeling. As the benefits of $66\%$ H2 application on wound healing were visible on day 3, we evaluated whether abbreviated treatment might have therapeutic value. For these experiments, we treated mice with $66\%$ H2 for 3 days only, and then stopped administration while continuing to monitor the mice for another 6 days. As shown in Fig. 1F, G, the $66\%$ H2 group healed faster than the control group; however, all healing speeds were much slower, with final wound closer rates of < $50\%$ and blood clots visible after days 3–5. Therefore, these results suggest that persistent daily treatment is required for the full benefits of H2. ## Hydrogen increases blood flow and blood oxygen levels in the wound area and promotes early blood vessel formation As important indicators of wound healing, the wound blood flow and blood oxygen [54, 55] content are tested to evaluated the effects of $66\%$ H2 on perfusion. From day 5 after wounding until the final day of imaging, blood perfusion in the wound bed was significantly higher in the $66\%$ H2 group that in the other groups (P [$66\%$ H2 vs. control] < 0.0001) (Fig. 1H, I), indicating better tissue survival and tube formation [56]. Furthermore, the oxygen saturation (SaO2), total haemoglobin mass (tHb), and deoxyhaemoglobin (HHb) values were all higher starting at day 3 in the $66\%$ H2 group than in the $5\%$ H2 and control groups (Fig. 2I). The initiation of vessel formation by H2 may happen earlier than day 5 post-wounding. Fig. 2H2 treatment promoted different ECM components early deposit in the epidermis, dermis and dermis-epidermis-junction of proximal wound. A Representative H&E staining of the truncated region of epidermis at the wound edge at 3 days post-wounding between two groups; graph indicated the widest basal layer of hypertrophic epidermal wound edge. B Masson staining of different regions of wound edge at 3 days post-wounding between two groups. C–H IHC staining showing the expression of ECM components of Col-I, Col-III, Fibronectin, Integrin, Col-XVII, and Laminin, respectively in the wound edge between D3H and D3C groups. I Scheme of a proximal wound section indicating the histological localization of three kinds of collagens: Col-I (dermis), Col-III (epidermis), and Col-XVII (DEJ) expression after H2 treatment during the re-epithelialization process. J Representative image of day 5 post-wounding indicating the moist haling mode possibly triggered by the early ECM deposition induced by H2 treatment. K Overview of mapping of metabolic differences between the $66\%$ H2 and control groups as well as PCA analysis and pathway enrichment of differential metabolomes; R2X = fraction of variance for the model; Q2 = predictive ability of the model. L IHC staining showing the expression of Col-I and Col-III between D11H and D11C groups. Data in A processed unpaired t test, and were plotted as Mean ± SEM. * P value < 0.05; P value < 0.01; P value < 0.001; no stars for P value > 0.05; scale bar = 100 μm. Red line indicates the boundary between the epithelium and dermis. Black arrowhead indicates positive-expressing cell. d, dermis; he, hypertrophic epidermal wound edge; ife, interfollicular epithelium; sm, smooth muscle CD31 staining 11 days after wounding suggested the $66\%$ H2 group had the most pronounced tube formation (more than six times compared with that in the control group) (Fig. 1J, K). Whole-mounting scans targeting CD31 suggested that, from day 3 onward, $66\%$ H2 already induces visible vessel formation, especially in the proximal part of the wound site under the epidermis (Fig. 1L, M, Figure S2). In vitro tube formation test showed that the $66\%$ H2 group had a similar effect as the positive control group (bFGF) (Fig. 1N, O), and better than the $5\%$ H2 (with air), N2 (no air), and $33\%$ O2 (No H2) (Figure S3 A, B). Tube formation-related genes were upregulated in D1H but downregulated in D3H (Fig. 2P), indicating an early activation of tube formation by $66\%$ H2. Further whole-mount tissue scanning suggested H2 accelerated the visible vessel formation of proximal wound since day 2 post-wounding (Figure S3A, B). These results are consistent with a role for H2 in early vascularization, which could contribute to wound healing. ## Hydrogen induced transcriptomic signatures associated with early ECM deposit, tube formation, cell migration and differentiation, and mitochondrial repair Given the remarkable healing by $66\%$ H2 at day 3 after wounding (the epithelialization phase), we performed functional annotation and enrichment analysis of RNA-seq data from skin samples to identify underlying processes. Based on differences in expression between the $66\%$ H2 and control group (see the total DEGs in Table S4), 68 differentially expressed genes (DEGs) from D1H (day 1 H2) vs D1C (day 1 control) ($\frac{58}{10}$, up/down) (Table S5), 18 DEGs from D2H vs D2C ($\frac{12}{6}$, up/down) (Table S6), and 24 DEGs from D3H vs D3C ($\frac{18}{6}$, up/down) (Table S7) were identified (adjusted p value ≤ 0.01 and │FC│ ≥ 1.5), with no overlap between the sets of DEGs (Figure S4A–C). Heatmap cluster analysis of all DEGs identified 4 clusters (Figure S4D). Among the top-10 Gene Ontology Biological Processes (Figure S4E) in cluster I, the DEGs were mainly enriched in hormone response and signal transduction processes, which were upregulated mainly in D2H; genes in cluster II were enriched mostly in biological functions related to tube formation, cell migration and extracellular matrix (ECM) organization and were upregulated first in D1H, then gradually decreased to an equivalent expression level between D2H and D2C, and upregulated again in D3C; genes enriched in cluster III were mostly related to muscle cell differentiation and organization and were upregulated in the D3H group; and genes in cluster IV were involved in processes related to metabolism and were upregulated in D2C. Gene set enrichment analysis (GSEA) confirmed the pattern of DEG enrichment in ECM deposition-, cell adhesion-, and tube formation-related function in D1H (Figure S4F); muscle filament sliding and myofibril assembly related functions in D2H (Figure S4G); and myocyte migration and differentiation and mitochondria activity-related functions in D3H (Figure S4H). ## Hydrogen induces early ECM deposition during the epithelialization process Considering of the important role of ECM in epithelialization [12], the effect of $66\%$ H2 on early ECM deposition at 1–3 days after wounding was evaluated, which is when the proliferative phase normally starts and keratinocytes migrate into the wound bed. H&E staining showed a thicker epidermal layer on the hypertrophic epidermal wound edge in the D3H group as compared to the D3C group ($$p \leq 0.0132$$) (Fig. 2A). Furthermore, Masson staining revealed more dermal collagen deposition in the D3H group at both proximal and distal wounds (Fig. 2B). Consistently, immunohistochemical staining (IHC) showed that col-I (Fig. 2C) and col-III (Fig. 2D) were more highly expressed in the D3H group, especially around the proximal area of the wound. Fibronectin (Fig. 2E), integrin (Fig. 2F), col-XVII (Fig. 2G), and laminin (Fig. 2H) were also elevated in the D3H proximal epidermis, indicating better DEJ formation after H2 treatment. Scheme of the col-I, col-III, and col-XVII expression in the proximal wound edge was shown in Fig. 2I. ECM deposition may contribute to maintain the moist healing (Fig. 2J). As additional evidence for the importance of the ECM, we examined the differential expression of metabolites. Twelve metabolites (Table S8, S9), all of which were amino acids, were differentially expressed on day 3 (Fig. 2K). Among them, L-proline, and especially 4-hydroxyproline, are known to provide raw materials for collagen synthesis [57]. Pathway categorization supported an increase in metabolites thought to contribute to an environment that supports ECM deposition in the wound bed after H2 treatment (Table S10). Furthermore, correlation analysis of D3 non-target metabolism and RNA-seq showed similarly enriched genes in tight junctions and focal adhesion pathways (Figure S5). We also examined ECM deposition at the wound site on day 2 by IHC, which demonstrated that col-I, fibronectin and laminin were more highly expressed in the D2H group than in the D2C group, at the proximal wound (Figure S6A–C), though no difference was observed in integrin expression (Figure S6D). This deposition of collagens in the $66\%$ H2 group lead all the way to day 11 post-wounding, especially for dermal Col-1 and epidermal Col-III (Fig. 2L). ## Hydrogen induces α-SMA+/K14+ keratinocytes and α-SMA+ myofibroblast migration to the wound edge Collagen and other ECM components can induce keratinocyte migration [58, 59]. To evaluate the effect of H2 on epithelialization at the proximal wound edge (leading edge), we performed immunofluorescent staining of markers for myoepithelial cells/myofibroblasts (α-SMA), keratinocytes (K14), and IFE cells (K5). Day 3 whole-mount staining demonstrated more than 3 times the amount of α-SMA+/K14+ keratinocytes in the D3H group as compared to the control group (especially in the migration tongue region of hypertrophic epidermal leading edge) (Fig. 3A, B), indicating transformation from keratinocytes to a myoepithelial-like cell type. However, the IFE keratinocytes displayed slower proliferation rates in the D3H group than in the D3C group (Fig. 3C, D, $p \leq 0.05$), which may be due to a more advanced cell proliferation induced by H2. Although not highly expressed, the epidermal keratinocytes at the leading edge produced more col-1 (α-SMA+/Col-1+) in the D3H group than in the D3C group (Fig. 3E, F). The transformation direction from keratinocytes to myoepithelial cells, were then proved by digital spatial profiler transcriptome analysis (D3H Leading edge vs D3H ife of the proximal wound). Seeing from Fig. 3G, key genes selected after up-PPI analysis include Acta2 (α-SMA), Vim (Vimintin), Col1a1 (col-I), and Col17a1 (col-XVII). D3H L up-GO BP were mostly enriched in myoepithelial cell-related functions (Fig. 3H). Electron microscopy demonstrated a higher density of tonofilaments arranged around the plasma membrane of keratinocytes at the leading edge of the D3H group (Fig. 4I); the correlation analysis of transcriptomics and metabolomics also revealed that, the focal adhesion-related function was enriched in D3H group (Figure S5). These results could support better migration ability for myoepithelial-transformed keratinocytes [60]. Furthermore, both and Vimentin+ fibroblasts (Fig. 3J, K, $p \leq 0.001$) and α-SMA+ myofibroblasts (Fig. 3L, M, $p \leq 0.001$), which are two of the main sources of collagen, were more abundant in the proximal wound of the D3H group. In vitro IF staining also proved that fibroblasts (HPF, (Figure S7E)) and keratinocytes (HaCat, Figure S7B) were producing more col-1 after H2 treatment. Besides, the primary cell HUCP-MSCs have been identified to maintain the MSCs phenotypes (Figure S7A, B). H2 promoted the HUCP-MSCs transformation into a myofibroblast phenotype (Vemintin+, α-SMA+) (Supplemental Figure S7C) with better collagens producing (Supplemental Figure S7C) abilities. Consistent with all the staining results, transcriptome analysis of D3 identified top Gene Ontology biological process (GO-BP) enrichment categories involved muscle cell development, differentiation, and contraction (Fig. 3N).Fig. 3H2 induces myofibroblasts and fibroblasts migration and IFE towards α-SMA+ myoepithelial cell transformation in the proximal wound. A, C, E, J Detection of α-SMA/K14, K5/ki67, Col-1/α-SMA and Vimintin/K14 in the leading edge of the proximal wound at day 3 post-wounding. L Col-1/α-SMA co-expression in the dermal fibroblast of proximal wound at day 3 post-wounding. B, D, F, K and M quantification of double/single positive cells in A, C, E, J and M. G, H DSP transcriptome analysis of D3H proximal wound area (D3H leading edge vs D3H ife) showing the upregulated PPI network, key genes and up-GO biological processes in the keratinocytes of leading edge. White and yellow dotted circles indicate ROI of leading edge and ife, respectively. I Observation of keratinocyte tonofilament morphology in $66\%$ H2 and control groups by electron microscope. N GO-BP enrichment and enriched GO-BP clusters of D3H vs D3C DEGs by using metascape online analysis. White dotted line indicates the boundary between the epithelium and dermis. White arrows in A, C, and E indicate co-positive IFE cells, yellow arrows indicate Vimintin+ (J) fibroblasts or Col-1+/α-SMA+ co-positive myofibroblasts (L). Data in B, D, F, K, and M processed Unpaired T test, and were plotted as Mean ± SEM. P value < 0.05; P value < 0.01; P value < 0.001; no stars for P value > 0.05; Scale bar in A, C, E, J, and $L = 100$ μm; Scale bar in $I = 1$ μm. d, dermis; he, hypertrophic epidermal wound edge; ife, interfollicular epithelium. DSP, digital spatial profilerFig. 4H2 treatment promoted in vivo early epidermal stem cells proliferation at days 1–3 after wounding. A-C Panoramic scanning of wound edge and the representative immunofluorescence images of K14 (green) and ki67 (red) expression in the leading edge (L, 0–1 mm from wound edge), mid-end (M, 1−2 mm from wound edge) and distal end (D, 2-3 mm from wound edge) of the wound; D−F Statistics of K14+/ki67+ basal cells and hair follicles in the immunofluorescence. G-I. Representative immunofluorescence of p63 (green) and ki67 (red), K15 (green) and ki67 (red), and Lgr6 (green) and ki67 (red) on skin section showing their expression in the leading edge (L), mid-end (M) and distal (D) of the proximal wound in epidermal and hair follicle cells from 1 to 3 days after wound. J−L. Statistics of p63+/ki67+, K15+/ki67+, and Lgr6+/ki67+ basal cells and hair follicles in the immunofluorescence from 1 to 3 days after wounding. White arrow indicates co-positive basal cell; yellow arrow indicates co-positive follicle cell. Data in D–F, J–L processed two-way ANOVA test, and were plotted as Mean ± SEM. P value < 0.05; P value < 0.01; P value < 0.001; no stars for P value > 0.05; Scale bar = 100 μm ## Hydrogen induces early IFE and HF stem cells proliferation IFE stem cells (IFESCs) are thought to promote wound healing and replace irreversibly lost skin [61], while hair follicle stem cells (HFSCs) around the wound replenish the basal layer and reconstitute non-proliferative, transcriptionally active spinous and granular layers [17, 62]. To characterize the early proliferation and distribution of IFESCs and HFSCs in early wounding, we performed whole mount tissue scans. More k14+/Ki67+ basal IFESCs were identified in the D1H (Fig. 4A, D) and D2H groups (Fig. 4B, E), particularly at the leading edge (L, 0–1 mm from the wound site). Furthermore, the proportion of k14+/Ki67+ HFs was more abundant in the D1H group in the L, M (middle), and D (distal) edges of the wound site (Fig. 4D). However, 3 days after wounding, there were fewer k14+/Ki67+ basal cells and HFs in the D3H group, especially in the L and M parts of the wound (Fig. 4C, F), indicating an advanced activation for the IFESCs proliferation triggered by H2. We further characterized IFESCs and HFSCs by examining the expression of specific markers in proliferating cells after H2 treatment. The results demonstrate that p63+/Ki67+ basal IFE cells were more abundant in the D1H and D2H groups in the L, M, and D parts of the proximal wound after H2 treatment (Fig. 4G, J). Additionally, there were more k15+/Ki67+ hair follicles in the D1H group and k15+/Ki67+ basal IFE cells in the M and D wound in the D2H group (Fig. 4H, K). There were also more Lgr6+/Ki67+ basal IFE cells in the D1H and D2H groups in the L, M, and D parts of the wound site (Fig. 4I, L), while Lgr6+/Ki67+ HFs were more abundant in the M and D parts in the D1H and D2H groups (Fig. 4I, L). All p63+/Ki67+, k15+/Ki67+, and Lgr6+/Ki67+ basal IFE cells and HFs were decreased for the first 3 days after H2 treatment (Fig. 6J–L). Thus, H2 treatment induces early proliferation of basal IFE stem cells and HFSCs that contribute to epidermal thickening and wound closure. ## Hydrogen alleviates inflammatory responses by reducing especially the Th1 and Th17 population during aseptic wound healing Inflammation also influences wound healing and matrix deposition [63, 64]. Therefore, we sought to determine the effect of $66\%$ H2 treatment on Th cell subsets. Flow cytometry of mouse spleens 24 h after wounding showed the CD4+ and CD8+ T cells had no significant differences among the three groups (Figure S8A), while the population of Th1 and Th17 subgroups were reduced by H2 treatment (Figure S8B, D). No significant differences were observed in the other two subsets of Th2 and Treg cells (Figure S8C, E). A representative gating strategy was shown in Figure S8F. The splenic FCM detection revealed H2 alleviated Th1- and Th17-related systemic inflammation during the early stage of wound healing. Due to the aseptic wound (daily oral intake enrofloxacin for each group) [65], tissue cytokine profile for the H2 group has no big difference with the control and NAC group, though TNF-α, IL-1β were reduced and IL-10 was slightly increased 24 h after wounding in the $66\%$ H2 group (Figure S8G). ## H2 promoted keratinocytes in re-epithelialization and fibroblasts/MSCs in migration possibly due to maintaining the mitochondrial functions In the re-epithelialization phase of wound healing, epithelial cells migrate to the wound site, cover the granulation tissue, and then converge in the middle. In vitro live cell imaging revealed an enlarged HaCat cell colony area in the H2 group (Fig. 5A, B). Furthermore, we performed electron microscopy 3 days after wounding and observed the Mt of the keratinocytes in the leading edge (0–1 mm from wound edge). The D3H group showed increased total Mt numbers, with more intact and fewer damaged Mt (Fig. 5C, D).Fig. 5H2 promoted in vivo and in vitro keratinocytes and fibroblasts cells viability by accelerating the re-epithelialization, migration and mitochondrial functions. A. Representative images of HaCat cell epithelization. Images were captured every two hours using a live cell imaging system, and the 0, 12 and 24 hour images were presented. B. The increased rate of the total colony area in 12 and 24 hours. C. Comparison of mitochondrial morphology 3 days post-wound for the D3H and D3C groups observed by electron microscope. The right two images in each panel are enlargements of the white frames in the left images. Red arrows indicate intact mitochondria, while yellow arrows indicate damaged mitochondria. D. Comparison of total mitochondria, intact mitochondria and damaged mitochondria numbers between D3H and D3C groups. E and G. Representative images of HUCP-MSCs and HPF fibroblasts migration at 0-h, 12-h, and 24-h time points in H2 medium and control groups. F, H Graphic analysis* of HUCP-MSCs and HPF fibroblasts total moving distance in 24 h. I. Cell migration ability examined by wound-healing assay in H2 medium and control groups. J Wound-healing curve analysis under different conditions. K Heatmap and enrichment plot showing genes related to mitochondrial respiratory chain complex assembly, which is the top 1 GO enrichment function in D3H. L, M Top GO enrichment plots in D3H related to inner mitochondrial membrane organization and ATP synthesis-coupled proton transport, which indicates more intact and functional mitochondria in D3H. N GSEA GO enrichment plots enriched in Mt related functions for D3H (NES > 1.5). White and black dotted lines in E and G indicate the migration route of fibroblasts. Red dotted line in G. outlines the representative HUCP-MSCs. Data in B and D processed two-way ANOVA test, and data in F, H, and J processed unpaired t test. Data were plotted as mean ± SEM. * P value < 0.05; P value <0.01; P value < 0.001; no stars for P value > 0.05; Scale bar in A and $G = 200$ μm, scale bar in $C = 2$ μm, scale bar in $E = 100$ μm, scale bar in $I = 1000$ μm To confirm the cell migration ability enhanced by H2, HPF fibroblasts and HUCP-MSCs were plated and observed by live cell imaging system for 24 h after H2 medium treatment. For the HPF cells, the total moving distance in the H2 group was 2.5 times higher ($p \leq 0.01$) than that in the control group (Fig. 5E, F; videos provided in supplemental movies 1 and 2). Similarly, at 24 h, the total moving distance of HUCP-MSCs were significantly higher in the H2 group than in the control group ($p \leq 0.0001$) (Fig. 5G, H). In a live cell scratch assay, human embryonic skin fibroblast ESF cells healed faster in the H2 group than in the control group (Fig. 5I, J). The GSEA from D3H vs D3C up-Go* genes were enriched in mitochondria (Mt) structure and function (Figure S4H), which are important for cell migration. Detailed GO-plots related to Mt organization and energy generation were extracted for the D3H up-GO GSEA (Fig. 5K–N). All these mitochondrial functions promoted by H2 give promise to a better cell viability for keratinocytes, fibroblasts, and MSCs in the wound edge. ## H2 promote the early repair of muscle and skin nerve injuries Since hydrogen can promote early epithelization and tissue remodelling during wound healing processes, it is also likely to have a good repair effect on other tissue injuries such as muscle and nerve. In the muscle contusion model (Figure S9A), the swelling and blood perfusion of gastrocnemius muscle was significantly relieved in the $66\%$ H2 group since day 2 onward (Figure S9B-D). These results indicated early repair functions of H2 in soft tissue muscle damage. On the other hand, NFH (neurofilament, heavy polypeptide) was used as a marker to reveal the possible function of hydrogen in nerve injury repair and axons maturation. More NFH+ cells were observed in the $66\%$ H2 group of proximal wound edge on day 3 and day 12 post-wounding, suggesting the cutaneous nerve was repaired by H2 treatment at an early stage. Other kinds and more complicated tissue injury restoration will be further explored in our future research. ## Topical treatment of H2-releasing dressing promoted wound healing Considering the application in human wound care, topical H2-releasing dressing could be a candidate strategy which is more convenient and easier to perform. As it is shown in Fig. 6A (left), Mg-based H2-releasing dressing is prepared and topical treated on top of the wound. The Mg balls are all microparticles with the average dynamiter of 20 μm (Fig. 6A, middle). The H2 release lasted for more than 12 h (Fig. 6A, right). H2 dressing group healed faster than the control group (Fig. 6C, D), with more blood capillaries formation and collagen deposition on day 11 post-wounding (Fig. 6E). The day 20 HE staining in the control group (Fig. 6F) suggested that, even though given doubled time the wound closure in the control group reached a same level as the H2 group on day 11 post-wounding, the healing effects might be not as good as the H2 group, because of the incomplete epithelial layer, unclear basal layer boundary, and less capillaries formation. Additional research into longer-lasting and more thorough healing benefits for hydrogen dressing is still needed, such as in scar formation and tissue remodeling. Fig. 6Daily topical treatment of H2 dressing significantly promoted cutaneous wound healing. A Left: scheme images of Mg-based H2 dressing in cutaneous wound healing treatment; Middle: scanning electron microscope (SEM) image of Mg microparticles; right: H2 release curves of Mg-based dressing. B Timeline of animal experiments and daily H2 dressing treatment (or normal dressing condition). C Representative wound area images captured every 2 days after modeling. D Comparison of wound closure percentage after modeling between H2 dressing and control groups. E, F H&E staining and Masson staining showing the tissue remodeling 11 days and 20 days post-wounding. Data in D was analyzed by two-way ANOVA test. All of the data are plotted as mean ± SEM. * P value < 0.05; P value < 0.01; P value < 0.001; **P value < 0.0001; no stars for P value > 0.05; scale bar in $A = 2$ μm, scale bar in $E = 100$ μm. Red dotted line in E (H&E staining) indicates the boundary between the epithelium and dermis. Black arrowheads in E and F indicate blood capillaries in the dermal layer of the wound. b, basal layer; d, dermis; hf, hair follicles; ife, interfollicular epithelium; s, scab ## Evaluation of the H2 induced novel wound healing pattern The speed and effect of re-epithelialization during wound healing is largely attributable to ECM [12] and EpSCs [11]. Therefore, it is vital and uplifting to find new strategies that induce autologous ECM accumulation and EpSCs activation for wound healing. In addition to these two factors above, “TIME” is usually being applied in the evaluation of healing effect. Our current study discovered that molecular hydrogen induced a new healing pattern, particularly characterized by fast re-epithelialization, early ECM deposition and EpSCs activation. For the evaluation of the “TIME” indicators, H2 promoted the effect of wound healing in a comprehensive way. “ T” for tissue remodeling is improved by good DEJ, massive blood vessel formation and dermal collagens deposition. In the “I” of inflammation, we proved that H2 reduced the splenic Th1 and Th17 Th-cells subgroup distribution. This shift of Th-cells subgroup is reported to be important in facilitating wound healing [66]. For “M” of moisture balance, we revealed that H2 induced a natural moist-like healing mode, with less blood clots formation. “ E” is representing of edge of wound, and H2 greatly promoted the re-epithelialization progress, with fast keratinocytes migration and early EpSCs proliferation in the leading edge. However, it has to be further investigated, if our perspective can be applied to all wounds. The animal model of sterile wound model can be one of the limitations in our study. Although there are evidences showing the positive role of hydrogen in multiple wound care, the therapeutic impact and our concept in the mechanism of hydrogen in the treatment of complicated wounds, still need to be further established. Because wounds such as in diabetic wounds and burns, are frequent in clinical practice. ## Early deposition of ECM components promoted by H2 and contributed to fast re-epithelialization, tight DEJ and natural moist healing environment As the most abundant protein in the ECM [67], collagen (especially type I and type III collagen) synthesis, deposition, and release are favourable for EpSCs activities in the skin. In our study, we observed early ECM deposition (1–3 days after wounding) especially for collagens after H2 treatment (Fig. 2C–H). This was confirmed by RNA-seq (Figure S3E, F) and untargeted metabolome analysis (Fig. 2K). Though in some hard-to-heal wound studies, H2 was proved to promote collagen synthesis by masson staining [40] and IHC staining for Col-1 [42], our study is the first comprehensive and histological reveal of H2-induced early deposition of massive ECM components during re-epithelialization of wound healing. In order to re-form the basement membrane beneath the epidermal basal layer, both keratinocytes and fibroblasts will contribute to ECM components synthesizing [12]. Early dermal col-I and especially epidermal col-III in the proximal wound were found accumulated in the H2 group, which indicated better structural integrity of the ECM and refined tissue functions [68]. This is possibly related with the transformation from keratinocytes to myoepithelial-like cells (K14+/αSMA+, Fig. 3A, G). In skin tissue, the ratio of col-I/col-III normally increased with time from foetus to adult [69, 70]. The cross-linking pattern of collagen is always important to determine the scar-free wound [69–72]: foetal skin tissue deposit more col-III than adult skin, which contributes to scar-less wound healing; on the contrary, fibrillar col-I is dominant in adult skin, leading to scar formation. During wound healing, col-I/col-III ratio stayed dynamically, first decreased and then increased to normal level [72]. Therefore, the massive deposition of col-III in the proximal wound (Fig. 2D) after H2 treatment may indicate a youthful state of skin, leading to faster re-epithelialisation. In addition to col-I and col-III, H2 induced early fibronectin deposition would also provide a scaffolding for epithelial migration [73], which is similar to fetal scarless wound healing pattern [74]. Skin basal keratinocytes separate the epidermis and dermis, and maintain the two layers attached together. This function is dependent on DEJ-related proteins, of which Laminins [75] and col-XVII [76] are two of the most important members. The higher expression level of laminin (Fig. 2H) and especially col-XVII (Fig. 2G) in the proximal epidermal keratinocytes indicated better DEJ induced by H2. Provision and maintenance of a moist condition (Fig. 2J) is favourable for establishing an ideal microenvironment for healing processes. Collagen dressing is therefore always chosen as one of the wound care strategies [77, 78]. Instead of topical application of collagens, early autologous collagens accumulation in the proximal dermal and epidermal wound was found induced by H2 and persisted in the whole healing processes. Untargeted metabolome analysis for the wound bed tissue revealed that most of the identified differential metabolites in H2 group (Fig. 2K) are related with the components, precursors, or inducers of collagen synthesis, such as 4-hydroxyproline [79], L-proline [80], and asymmetric dimethylarginine [81]. Taken together, ECM deposition promoted by H2 could contribute to the fast re-epithelialization, tight DEJ, and natural moist healing environment. ## The early activation of autologous EpSCs proliferation and differentiation regulated by H2 treatment Once the cells at the wound edge begin to migrate, epithelial cells behind the edge proliferate; this continues until new epithelium covers the damaged tissue [82]. Epidermal growth and thickening rely on the surrounding components such as the ECM, as well as on the activation of EpSCs. To restore the functional epidermal barrier, EpSCs proliferation is followed by differentiation [83, 84]. When IFESCs and HFSCs are recruited to the IFE after injury, they progressively lose their initial identities and differentiate [85]. Different stem cell markers involved in IFESC and HFSCs niches [86], such as K5 [87], Lgr6 [88], K15 [89], and p63 [90] were examined here. We observed early proliferation (since day 1 post-wounding, about 1–2 days earlier than the control group) of IFESCs and HFSCs during the first 3 days post-wounding (Fig. 4A, B, G–I), as well as thickening of proximal stratum corneum in the H2 group (Fig. 2A), though the differentiation trajectory of these stem cells still needs to be tracked. Two other stem cell–related studies reported that H2 prolonged the replicative lifespan of bone marrow multipotential stromal cells in vitro [46], and protected hematopoietic stem cell from irradiation injury [47]. In vivo studies on the effect of hydrogen on stem cell proliferation and differentiation, and the relationship between hydrogen and EpSCs in wound healing have not been studied. Our study revealed that H2 promoted the early EpSCs proliferation and differentiation in different time series (day 1–3) and space series (leading edge, mid-end and distal part of wound) during wound healing for the first time. The following differentiation of the EpSCs then surprisingly turned out to be K14+/α-SMA+ myoepithelial-like cells, which may further contribute to collagen deposition, epidermis migration and the maintenance of the youthful state of cells. In addition, some studies have already mentioned the mobilization and contribution of MSCs/stroma cells to regeneration of injured epithelia [91]. In our study, in vitro experiment also proved that H2 could activate MSCs and induce the transformation from MSCs towards myofibroblast-like direction, with the phenotypes of αSMA+, Vemintin+, and better ability of collagen producing (Figure S7) and moving (Fig. 5G, H), which could further contribute to the wound closure. The combined major benefits of earlier ECM deposition and stem cell proliferation and differentiation after H2 treatment, as well as vessel formation and cell migration, promoted haemostasis, re-epithelialization, and reduced scab formation. However, whether EpSCs in a collagen-rich environment are more likely to differentiate to keratinocytes remains unclear [92]. Besides, in order to identify which EpSCs subgroups were first triggered by H2 and their subsequent differentiation trajectory throughout wound healing, more studies on the EpSCs linage tracking are still needed. ## Potential mechanism of H2-induced high cellular activity in various cells Notably, though most of the current reports have focused on H2’s anti-ROS and anti-oxidative functions [93], our study revealed that these anti-oxidative functions are not the key means by which H2 (O2-indenpendent) accelerates wound healing. One direct evidence could be NAC, which is well-known as an effective ROS scavenger [94], had no statistical effect on the healing processes in our study (Fig. 1C, D). Meanwhile, the healing effects in $33\%$ O2 treatment had no significant difference comparing with the control group (Fig. 1C, D). Consequently, the mechanism of selective antioxidant cannot explain the H2 induced early accumulation of ECM and activation of EpSCs observed in our study, and that, these H2-induced beneficial effects in wound healing is not O2 dependent. Besides, we discovered that H2 promoted cell viability in vitro of different cell types, which manifested in more tube formation for endothelial cells (Fig. 1N), faster cell migration for fibroblasts (Fig. 5E, G, I), and better epithelialization for keratinocytes (Fig. 5A). This is also reflected in EpSCs early proliferation, however, the proliferation seemed to be under a strict regulation, with proliferation during the first 2 days post-wounding (Fig. 4A, B, G–I) and differentiation from day 3 onward (Fig. 3A, G). The promoted cell viability was quite consistent with what we observed in the H2 group in vivo, including early blood vessel formation (visible since day 3 post-wounding, Fig. 1L, M), fibroblast aggregation (approximately day 3 post-wounding, Fig. 3J, L) in the proximal wound and faster re-epithelialization (visible since day 3 post-wounding, Fig. 2A). These observations above revealed that H2 was able to boost the cell viabilities of diverse cells in different ways, at different time points but in the same histological space during wound healing. Combining with our results (Fig. 5C, D, K–N; Figure S3H), one of the possible explanations for the better cell viability could be H2 induce robust mitochondrial activities and less structurally aberrant mitochondria during wound healing. This will further promote such as cell proliferation [95] and migration [96] abilities. Though the mechanism of H2 remains unclear, recent studies revealed that H2 may be multi-targeted and mainly based on enzymatic reactions. Higher organisms may have hydrogen metabolism abilities based on hydrogenase, especially for the mitochondrial-related hydrogen metabolism [97, 98]. The cell membrane may have the enzyme activity of hydrogen metabolism, and ion channels are also possibly regulated by H2 [99, 100]. Recent study also put forward that, Fe-porphyrin is a H2-targeted molecule, acting as a biosensor and catalyst for H2 [101, 102]. Taken together, our study indicates that the role of H2 may function at the level of very basic biological processes of life, which needs to be further explored in the future. ## Conclusion For this study, we used a H2 chamber (no requirement for body fixation or anaesthesia) with a murine aseptic wound model. Considering its both inhalation and wound surface contact with H2, we also put efforts in the nasal inhalation (chronic wound) [103] and topical H2 sustained-release dressings (cutaneous wound) (Fig. 6) treatment in wound care, and both non-invasive interventions received good effects. Clinical investigations and other wound models with more complexity are also being conducted. There is also evidences that topical hydrogen intervention improved the wound healing in vivo [104]; however, comparing to focusing on the anti-infection and anti-inflammation effects, our research, instead, have indicated more functions of H2 in wound healing. Without combination with any specific medicine, we revealed that H2 alone was able to increase the rate of wound healing, with advantages (Fig. 7) including but not limited to promoting ECM deposition, autonomous stem cell early proliferation, blood vessel formation, cell viability, and natural moist healing pattern. We believe that this highly effective therapeutic method of H2 treatment holds promise for further clinical wound treatment, as well as applications in the tissue damage repair, regeneration and other wider fields. Fig. 7Schematic representation of the skin under H2 treatment and normal conditions during the early stage wound healing. In addition to anti-inflammation (especially for Th1- and Th17-related systemic inflammation), H2 treatment brings multiple other benefits (shown in the figure) including faster epidermis thickening, earlier proliferation (red nuclei) of epidermal stem cells (basal cells and hair follicle cells), early differentiation of basal cells in the wound edge (differentiate towards α-SMA+ cell type), earlier and better ECM deposit, faster blood vessel formation, mitochondrial damage repair, and moist healing process (less blood scab formation) ## Supplementary Information Additional file 1: Supplemental Figure S1. H2 slightly increases growth factors concentration at wound sites at day 1-3 after wounding. A, B. Profiles of tissue growth factors PDGF and EGF among the three groups at time points of day 1, day 2 and day 3 post-wounding. C, D. Concentrations of tissue growth factors bFGF and TGFβ-1 among all three groups at day 3. Data in A and B processed Two-way ANOVA test, and data in C and D processed unpaired t test. All data were plotted as Mean±SEM. P-value < 0.05; P-value <0.01; * P-value < 0.001; no stars for P-value > 0.05. Supplemental Figure S2. Whole-mount scanning showing H2 promoted early tube formation at the first 2 days after wounding. A and B. Panoramic scanning of wound edge and the representative immunofluorescence images of CD31 (green) and k14 (red) expression in the leading edge (L, 0-1 mm from wound edge), mid-end (M, 1-2 mm from wound edge) and distal (D, 2-3 mm from wound edge) of the wound at day 1 and day 2 post wounding respectively. White dotted line indicates the boundary between the epithelium and dermis. White arrowhead indicates tube formation. Scale bar = 100 μM. Supplemental Figure S3. Whole-mount scanning showing H2 promoted early tube formation at the first 2 days after wounding. A and B. Representative microscopic images and the quantification of in vitro blood vessel formation of Human Umbilical Vein Endothelial Cells (HUVEC) at 12 h after different treatments. Red hatched line outlines the newly formed tubes. Data in B were processed unpaired multiple T test. All of the data are plotted as Mean±SEM. * P-value < 0.05; P-value <0.01; * P-value < 0.001; no stars for P-value > 0.05. Scale bar = 100 μM. Supplemental Figure S4. Comprehensive gene set function enrichment analysis of differential gene expression induced by $66\%$ H2 treatment at the first 3 days post wounding. A. Heat map showing the differentially expressed genes (DEGs) among the three time points (D1H vs D1C, D2H vs D2C, D3H vs D3C). B. Venn diagrams showing the overlapping number of DEGs among the comparative data of three time points. C. Counting of total, up- and downregulated DEGs among the comparative data of three time points. D. Heat map showing four clusters identified from all the DEGs. E. GO-BP analysis in each individual cluster, the GO terms for genes related with extracellular matrix organization and muscle cell differentiation were significantly enriched in D1H and D3H, separately. F. Top 10 enriched up and down GO-BP in D1H by GSEA. G. Top 10 enriched up and down GO-BP in D2H by GSEA. H. Top 10 enriched up and down GO-BP in D1H by GSEA. Supplemental Figure S5. Transcriptomics and metabolomics correlation analysis reveal that differential genes and metabolites of the D3H group are enriched in tight junction and focal adhesion pathways. A. Correlation plot of DEGs and differential metabolites between D3H and D3C. B. Count of D3H vs D3C DEGs enriched in KEGG pathways. Supplemental Figure S6. ECM components col-I, fibronectin, laminin, and integrin deposition 2 days after wounding are increased in the H2 group especially in the proximal wound edge. IHC staining showing the difference between D2H and D2C in ECM deposit of day 2 post wounding. A-D indicated Col-1, fibronectin, laminin and integrin expression in D2H and D2H, separately. Scale bar = 100 μm. Supplemental Figure S7. H2 conditioned medium promotes collagen deposition in vitro in MSCs, fibroblasts and keratinocytes. A. Three positive markers in MSCs phenotype. B. Five negative markers in MSCs phenotype. C. α-SMA, Vimintin, and Col-1 expression in the HUCPF (fibroblast) between $66\%$ H2 and control groups 24 h after treatment. D and E. Col-1 deposit in the HaCat (keratinocyte) and HPF (fibroblast) cells between $66\%$ H2 and control groups 24 h after treatment. Supplemental Figure S8. 24-, 48-, and 72-h time points tissue cytokine profiling and four-color Flow CytoMetry (FCM) of different targets showing quantification of CD3+, CD4+, and CD8+ T cells, as well as of Th1, Th2, Th17 and Treg subgroups. A. FCM quantification of CD4+ and CD8+ cell distribution in all three groups. B. T-bet+/CD4+ T-cell distribution, double checked against IFN-γ+/CD4+ T-cell distribution. C. GATA3+/CD4+ T-cell distribution, double-checked against IL-4+/CD4+ T-cell distribution. D. IL-17A+/CD4+ T-cell distribution. E. CD25high/CD127low T-cell distribution. F. Example figures of gating strategy used in cytometry flow test, and the first four plots showed procedure of gating strategy from one of the typical samples in Th1 subgroup cells analysis, the form showed the cell abundance during the gating strategy. G. Pro-inflammatory cytokine profile (TNF-α, IL-1β, and IL-6), Th17-related cytokine (IL-17a and IFN-γ), and Anti-inflammatory cytokine IL-10 expression profile in all of the three groups at three time points. Data in A-C and G processed Two-way ANOVA test, and data in D and E processed unpaired t test. All data were plotted as Mean±SEM. * P-value < 0.05; P-value <0.01; * P-value < 0.001; no stars for P-value > 0.05. Supplemental Figure S9. High concentration of H2 promoted early mussel and nerve repair. A. Left: Scheme of a mouse gastrocnemius strike model. Right: Timeline of animal experiments and daily H2 treatment. B. Capture of the gastrocnemius strike area and blood perfusion 0-72 hours post-wounding in the $66\%$ H2 Control groups. C. & D. Quantification of diameter and blood perfusion of the gastrocnemius strike area between two groups. E. & F. Representative immunofluorescence images for NFH (green) nerve injury repair and axons maturation in dermal wounds of at day 3 and 11 post wounding. Data in C & D were processed unpaired T test. All of the data are plotted as Mean±SEM. * P-value < 0.05; P-value <0.01; * P-value < 0.001; no stars for P-value > 0.05; Scale bar = 100 μm. White dotted line in E & F indicates the boundary between the epithelium and dermis. Arrow in E & F indicates NFH+ cells. Table S1. List of primary and secondary antibodies used in experiments. IHC indicates immunochemistry, IF indicates immunofluorescence; FC indicates flow cytometry. Table S2. Gradient conditions for reversed phase C18 separation. Table S3. Gradient conditions for HILIC separation of polar metabolites. Table S4. DEGs annotation of all the 6 groups. Table S5. DEGs annotation of D1H vs D1C. Table S6. DEGs annotation of D2H vs D2C. Table S7. DEGs annotation of D3H vs D3C. Table S8. Raw data of nontargeted metabolomic analysis between D3H and D3C. Table S9. 12 metabolites identified through hydrophilic-product test between $66\%$ H2 and Control group. Table S10. Top 20 significant pathways discovered by nontargeted metabolomic analysis between the D3H and D3C groups. Additional file 2: Movie S1. Live cell imaging of HPF cells under H2 medium condition within 24 h. HPF cells stained by actin-GFP images were taken every one hour, and 0, 4, 8, 12, 16, 20 and 24 h images were collected to build the video. Bar indicates 1000 μ. Additional file 3: Movie S2. Live cell imaging of HPF cells under normal condition within 24 h. HPF cells stained by actin-GFP images were taken every one hour, and 0, 4, 8, 12, 16, 20 and 24 h images were collected to build the video. Bar indicates 1000 μM. ## Preprint version online This manuscript has been posted online in Research Square as a preprint version since July 5th, 2022. 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--- title: 'Patients’ acceptance of a shared cancer follow-up model of care between general practitioners and radiation oncologists: A population-based survey using the theoretical Framework of Acceptability' authors: - Tiffany Sandell - Heike Schütze - Andrew Miller - Rowena Ivers journal: BMC Primary Care year: 2023 pmcid: PMC10044765 doi: 10.1186/s12875-023-02032-6 license: CC BY 4.0 --- # Patients’ acceptance of a shared cancer follow-up model of care between general practitioners and radiation oncologists: A population-based survey using the theoretical Framework of Acceptability ## Abstract ### Introduction International and national guidelines highlight the need for general practitioner involvement during and after active cancer treatment and throughout long-term follow-up care. This paper aimed to evaluate patients’ acceptance of radiation oncology shared follow-up care using the Theoretical Framework of Acceptability (TFA). ### Methods This cross-sectional study was conducted at two cancer care centres in the Illawarra Shoalhaven region of Australia. A sample of patients scheduled for a radiation oncology follow-up consultation in 2021 were sent a 32-point self-complete paper-based survey. Data were analysed using descriptive, parametric and non-parametric statistical analysis. This paper followed the Checklist for Reporting of Survey Studies (CROSS). ### Results Of the 414 surveys returned ($45\%$ response rate), the acceptance for radiation oncology shared cancer follow-up care was high ($80\%$). Patients treated with only radiotherapy were 1.7 times more likely to accept shared follow-up care than those treated with multiple modalities. Patients who preferred follow-up care for fewer than three years were 7.5 times more likely to accept shared care than those who preferred follow-up care for five years. Patients who travelled more than 20 minutes to their radiation oncologist or to the rural cancer centre were slightly more likely to accept shared care than those who travelled less than twenty minutes to the regional cancer centre. A high understanding of shared care (Intervention Coherence) and a positive feeling towards shared care (Affective Attitude) were significant predictive factors in accepting shared radiation oncology follow-up care. ### Conclusion Health services need to ensure patient preferences are considered to provide patient-centred cancer follow-up care. Shared cancer follow-up care implementation should start with patients who prefer a shorter follow-up period and understand the benefits of shared care. However, patients’ involvement needs to be considered alongside other clinical risk profiles and organisational factors. Future qualitative research using the TFA constructs is warranted to inform clinical practice change. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12875-023-02032-6. ## Introduction Once cancer patients complete active treatment, they require long-term follow-up to monitor for treatment-related side effects, recurrence, and psychosocial support [1–3]. The usual model of care is the hospital-based oncologist-led model [4, 5]. There is usually little formal involvement with the patient’s general practitioner in this model of care [6, 7]. While the oncologist-led model suits many patients, it may not always meet patients’ physical and psychosocial needs [8, 9]. For some patients, a shared care model might be more appropriate, tailored to their tumours, treatments, locality (metropolitan, regional or rural), access to specialists, and specific physical and emotional needs and preferences [10]. Shared care differs from the partial or whole transfer of care, where aspects of care are wholly transferred from the oncologist to another provider, such as a general practitioner. A shared cancer follow-up model of care harnesses the expertise of health professionals [11] and involves the explicit sharing of information and coordination of follow-up care [12]. Shared care is widely used in antenatal care and for managing patients with asthma, diabetes and ischaemic heart disease [13–16]. There is a growing body of evidence supporting the benefits of shared cancer follow-up models of care [17–21]. Randomised controlled trials have shown no difference in cancer recurrence rates or quality of life when a general practitioner is involved in follow-up care [22–25]. A shared cancer follow-up model of care offers several advantages to patients, health providers and health services. Patients generally find general practitioner appointments are more accessible than specialist appointments [26, 27]; there are fewer duplication of tests and clinical questions; reduced travel time; and more accessible parking [28, 29]. Benefits for general practitioners include increased knowledge and awareness of their patient’s overall health [30], addressing their patient’s unmet psychosocial needs [31], and improving relationships with specialists [32]. A shared care model benefits oncologists by allowing more time for newly diagnosed patients, patients on active treatment, advanced-stage and complex patients [33], and involvement in research and development activities. Additionally, the cost of shared cancer follow-up care to the health system is less than standard oncology follow-up care [34, 35]. A shared cancer follow-up model of care may vary for each medical discipline (medical oncology, radiation oncology, haematology, surgical) and tumour type (breast, prostate, head and neck, abdomen, lung, etcetera). For example, in one model for shared care for colorectal patients, the general practitioner and oncologist alternate the appointments quarterly [36]. In another model specific to radiation oncology shared follow-up care for breast, prostate and colorectal cancer patients, the radiation oncologist consultations cease entirely after three years after treatment, and patients see their general practitioner [37]. In that model the general practitioner follows a prescribed clinical follow-up assessment, and the radiation oncologist oversees and reviews the consultation results; this model is reliant on health technology and the two-way transfer of clinical information in real-time [37]. A core principle of shared cancer follow-up care is the acceptability of all parties: the oncologist, the general practitioner and the patient [38]. General practitioners are willing to accept a greater role in cancer follow-up care if there is improved information sharing and they are provided with clear clinical follow-up guidelines or protocols [39–41]. However, increased workload concerns remain [42–44]. Oncologists are more likely to prefer an oncologist-led model instead of a shared care or general practitioner-led model, as they feel they have the specialised knowledge for follow-up care [45]. However, oncologists are receptive to general practitioners taking a greater role in the more standardised aspects of follow-up care for low-risk patients [6], such as managing long-term and late side effects, blood tests and physical examinations. Several qualitative studies have found that patients appreciate their general practitioners taking a greater role in their long-term care [9, 29, 41, 46, 47]. Despite increasing evidence of the effectiveness of shared cancer follow-up care, data on implementation is limited. Treatment types (chemotherapy, immunotherapy, radiotherapy, surgery, etcetera) cause different short-term and long-term side effects [48], and differences in acceptance based on treatment type may help inform implementation. However, there is limited quantitative research on patients’ acceptance of a shared cancer follow-up model of care specific to radiation oncology patients, to allow generalisability to larger samples. This study aimed to evaluate patients’ acceptance of a shared cancer follow-up model of care between their general practitioner and radiation oncologist using the Theoretical Framework of Acceptability, in the Illawarra Shoalhaven Local Health District. ## Study design, setting and participants The Checklist Reporting of Survey Studies (CROSS) guided this study (Supplementary file 1). This cross-sectional study used a purpose-developed survey and was set in one regional, Illawarra Cancer Care Centre, and one rural, Shoalhaven Cancer Care Centre, Australia. This region provides health services for around 400,000 people, including radiotherapy-related services for over 6,000 distinct people for treatment and consultations annually. The study population was patients on a radiation oncology follow-up regime at one of these cancer centres in 2021. In Australia, oncologists are guided by the American Society of Clinical Oncology, the National Comprehensive Cancer Network, and the National Institute for Clinical Excellence guidelines for follow-up care. Follow-up care is between five to 10 years, however, the actual frequency depends on the individual patient’s health, stage,treatment, and an oncologist’s individual approach. ## Data sampling and data collection In 2021, there were 6,036 distinct patients scheduled for radiation oncology follow-up appointments in the study sites. We calculated that three-hundred and sixty-two completed surveys were required to obtain a $95\%$ confidence interval, with a $5\%$ margin of error. We anticipated a $40\%$ response rate. This was assumed because paper-based surveys obtain response rates of $46\%$ compared to online surveys of $36\%$ [49], and we were using a mixture of the two. Therefore, we sent 950 paper-based surveys and patients could elect to return the paper survey in the prepaid envelope provided or complete the survey online using the provided Quick Response Code or weblink. A proportional stratified random sampling approach was employed based on years since treatment. Years since treatment strata were divided into < 1 year, 1–2 years, 3–4 years, 5 years, 6–10 years and > 10 years. The sample from each stratum was randomly selected using a Microsoft Excel formula to generate the participant list. ## Survey A 32-point survey was developed by the authors and comprised four sections: demographics, health and cancer-related information, access to healthcare, and acceptance of shared care. The options in the demographics, health and cancer-related information, and access to healthcare sections were adapted based on previous survey designs [50–52], and are described below. Demographics: These included age, sex, postcode, country of birth, primary language spoken, ethnicity, relationship status, level of education, housing situation, employment status, and income. Health and cancer-related information: The cancers with the highest incidence [48] (breast, prostate, lung, colorectal, pelvis and head and neck) were included, as well as an ‘Other’ option. Additional information included: the staging at diagnosis, the primary hospital where radiation oncology treatment was received, other treatments received, and years post active treatment. A five-point scale ranging from ‘Excellent’ to ‘Poor’ based on World Health Organisation recommendations [53] was used to measure self-reported health status. Access to healthcare: Questions included whether the patient had a regular general practitioner, how often they visited their different doctors, the time required to travel to their doctors and the primary mode of transport, and how often they would like a consultation for their radiation oncology follow-up care, and how many years they would prefer follow-up care. Acceptability of shared cancer follow-up care: Acceptance of shared care was based on the Theoretical Framework of Acceptability (TFA) [54]. The TFA is a multi-construct theoretical framework designed explicitly to assess the acceptability of healthcare interventions from the perspectives of the people who receive the intervention and those who deliver it [54]. The TFA can be applied quantitatively or qualitatively and used prior to a health intervention to form judgments about whether the participants expect the intervention to be acceptable or unacceptable. Assessment of anticipated acceptability prior to participation can highlight which aspects of the intervention could be modified to increase acceptability and thus, participation [54]. The seven constructs of the framework used to determine overall acceptability are Affective Attitude, Burden, Ethicality, Intervention Coherence, Opportunity Costs, Perceived Effectiveness and Self-efficacy. Questions were developed based on these constructs and measured using a five-point Likert scale from ‘Strongly disagree’ to ‘Strongly agree’ (see Table 1 for example). Table 1Theoretical Framework of Acceptability constructs and example statements used in the questionnaireTFA ConstructsDefinitionTFA questions on 5-point Likert scale (Strongly disagree to Strongly agree) Affective Attitude Anticipated Affective Attitude: how an individual feels about the intervention, prior to taking part. I would be satisfied for my radiation oncology follow-up care to be shared with my general practitioner, so long as the radiation oncologist is still involved. Burden Anticipated burden: the perceived amount of effort that is required to participate in the intervention. It is easier to get to my general practitioner than the hospital.(Transport, time, parking, accessibility) Ethicality The extent to which the intervention has good fit with an individual’s value system. I would value my radiation oncologist and general practitioner working together to share my follow-up care. Intervention Coherence The extent to which the participant understands the intervention and how it works. I understand that shared radiotherapy follow-up care will benefit me, my doctors and the health system. Opportunity Costs Anticipated opportunity cost: the extent to which benefits, profits, or values must be given up to engage in the intervention. In order to have shared follow-up care, I would need to give up some of my time or my values on shared-care. Perceived Effectiveness Anticipated effectiveness: the extent to which the intervention is perceived to be likely to achieve its purpose. I believe that shared radiotherapy follow-up care will benefit me, my doctors and the health system. Self-efficacy The participant’s confidence that they can perform the behaviour(s) required to participate in the intervention. If I had the choice:a. I would choose to have my follow-up care with only my radiation oncologist.b. I would choose to have shared follow-up care with my general practitioner so long as my radiation oncologist is involved.c. I have confidence in my choice above. The survey was refined with feedback from four general practitioners and two radiation oncologists. The survey included as few questions as possible to reduce the burden on patients and improve the response rate [55]. Readability was scored at Year Nine level, which is slightly higher than the Australian desired standard of Years Six to Eight [56]. The survey was piloted on ten follow-up patients and ten cancer centre staff for content validity. The average completion time was 4.5 minutes. The final version was available in printed form and online using Qualtrics XM. ## Reliability and validity Internal consistency reliability estimates how much total test scores would vary if slightly different items were used [57]. The reliability across the seven constructs was assessed by computing Cronbach’s α, with the minimum acceptable value of α = 0.70. The coefficients for the constructs totalled α = 0.78, indicating an acceptable level of internal consistency [58, 59]. Table 2 shows that the reliability would slightly improve if the Opportunity Costs construct were removed; however, the research team deemed the improvement small and did not delete it. The construct validity of the TFA constructs (that is, how accurately the constructs measure what they say they do) [60] was calculated with Pearson’s correlation coefficient of the patient’s responses to an item with their total scores. A validity coefficient above 0.35 is strongly valid [61], and all constructs were positively associated. Table 2Reliability and validity analysis of acceptability constructsCorrected item total correlationCronbach’s Alpha if item deletedPearson correlationSig (2-tailed)Affective Attitude0.6030.7270.684< 0.001Burden0.1820.8190.373< 0.001Ethicality0.7560.6990.764< 0.001Intervention Coherence0.7530.7040.787< 0.001Perceived Effectiveness0.7720.6950.796< 0.001Opportunity Costs0.2810.8030.523< 0.001Self-Efficacy0.3730.7710.413< 0.001 ## Statistical analysis Data were analysed using the statistical software package SPSS version 29 [62]. Frequencies and percentages were calculated for categorical variables and reviewed for normality. Two forms of acceptance scores were generated. For the first, an average score across all TFA constructs, with opportunity scores reversed to align from a negative to a positive scale. A patient’s summated score was divided by the number of constructs constituting the scale, thereby creating a mean that falls within the range of the values for the response continuum options. All items comprising the construct’s scale were assumed to have equal weight when calculating a summated score. The second form of acceptance score was achieved by dichotomising the data into ‘Accept’ and ‘Not Accept’. The Accept score was generated from the ‘Strongly agree’ and ‘Agree’ response categories, and the Not accept score was generated from ‘Neither agree/disagree’, ‘Disagree’ and ‘Strongly disagree’. The dichotomised data were used to understand whether acceptance could be predicted based on any of the TFA categorical constructs (logistic regression). Parametric tests included multinomial and ordinal logistic regression. If requirements for parametric test procedures were not met, non-parametric tests were used: Chi-Square, Kruskal Wallace Test and posthoc analysis. All tests were 2-sided; statistical significance was defined as p ≤ 0.05. Weighted adjustments were used to compensate for missing data. ## Ethical consideration Ethical approval for this study was obtained from the Joint University of Wollongong and Illawarra Shoalhaven Local Health District Human Research Ethics Committee. Patients were provided with a participant information sheet about the study’s aim and procedures and informed that consent was tacit upon completing the survey and that responses would be anonymised. Patients did not receive payment or an honorarium. ## Results Of the 950 surveys sent, 414 were returned (response rate of $45\%$); 371 had no missing data. Twenty-eight surveys were returned to sender (26 were no longer at that address, and two stated that the patient was deceased). Most (383 of 414) surveys were returned via post ($92\%$). Patient demographics, health characteristics and healthcare access are presented in Table 3. Age and sex did not significantly influence a patient’s preference regarding their choice of follow-up care, and there were no significant socio-demographic variables on the acceptance for shared care. Table 3Demographics of radiation oncology respondentsN (%)N, (%) Sex Cancer Male171, (41.3)Breast193, (46.6)Female243, (58.7)Colorectal9, (2.2)Head/Neck54, [13]Lung25, [6]Prostate91, [22]Pelvis9, (2.2)Other33, [8] Age Stage at diagnosis < 405, (1.2)I91, [22]41–5038, (9.5)II76, (18.4)51–6013, 2(32.9)III57, (13.8)61–70201, (50.1)IV25, [6]> 7125, (6.2)Not known165, (41.1) Education Treatment Year 10126, (31.2)Only radiotherapy141, (34.1)Year 1242, (10.4)Radiotherapy and other273, (65.9)Certificate117, [29]Undergraduate43, (10.6)Postgraduate52, (12.9)Prefer not to say24, (5.9) Relationship Years since treatment Married278, (67.5)Less than 1 year122, (29.8)Single25, (6.1)1–2 years82, [20]De-facto13, (3.2)2–3 years59, (14.4)Divorced37, (9.03–4 years51, (12.5)Widow53, (12.9)4–5 years45, [11]Prefer not to say6, (1.5)5–10 years43, (10.5)> 10 years7, (1.7) Housing Health Rent48, (11.7)Excellent46, (11.3)Own341, (82.8)Very good131, (32.2)Other14, (3.4)Good149, (36.6)Prefer not to say9, (2.1)Fair68, (16.7)Poor13, (3.2) Employment Main hospital treated at Casual15, (3.7)Illawarra Cancer Care Centre - Regional262, [64]Part-time40, (9.8)Shoalhaven Cancer Care Centre - Rural147, [36]Full-time32, (7.8)Unable to work18, (4.4)Retired295, (72.1)Prefer not to say9, (2.2) Income Travel time to General practitioner (one way) <$15,00058, (15.2)$15,000–29,99997, (25.5)0–20 minutes361, (90.5)$30,000–49,00063, (16.5)21–40 minutes27, (6.7)$50,000–74,99946, (12.1)> 40 minutes11, (2.8)$75,000-100,00023, [6]> 100,00010, (2.6)Prefer not to say84, [22] Country of birth Travel time to Radiation oncologist (one-way) Australia/New Zealand324, (78.3)United Kingdom46, (11.1)0–20 minutes156, (40.1)Europe31 (7.5)21–40 minutes159, (40.9)Africa5 (1.2)41–60 minutes51, (13.1)Asia5 (1.2)> 1 hour12, (3.1)Canada3 (0.7)> 2 hours11, (2.8) Primary language Preferred radiation oncology frequency English404, (97.6)Every second month11, (2.8)Other10, (2.4)Every three months48, (12.1)Every six months108, (27.3)Once a year167, (42.2)No more visits wanted62, (15.7) Identifies as Aboriginal and/or Torres Strait Islander Preferred year for follow-up No follow-up wanted38, (9.6)For 1 year67, [17]No398 [98]For 3 years32, (8.1)Yes9 [2]For 5 years174, (44.1)For 10 years47, (11.9)For lifetime37, (9.4) One-third of the patients ($$n = 141$$, $34\%$) were treated with only radiotherapy, and two-thirds ($$n = 273$$, $66\%$) were treated with radiotherapy and chemotherapy and/or surgery. More patients reported their health as either ‘Excellent’ or ‘Very good’ ($43.5\%$), followed by ‘Good’ ($36.6\%$) and ‘Fair’ or ‘Poor health’ ($19.9\%$). Almost all patients had a regular general practitioner ($98\%$); $90\%$ lived within a 20-minute drive of their general practitioner, and $40\%$ lived within a 20-minute drive of their radiation oncologist. Table 4 shows a high acceptance of radiation oncology shared care across the different tumour types. However, no statistically significant results were found with patient acceptance of shared care between the tumour group, cancer staging, or years since treatment. Table 4Acceptance for radiation oncology shared follow-up careAccept Shared CareN (%)Do not accept shared careN (%)N Total average acceptance 325, ($80\%$) 79, [20] 405 Breast149, [79]39, [21]188Colorectal7, [78]2, [22]9Head/Neck44, [83]9, [17]53Lung20, [83]4, [17]24Prostate74, [82]16, [18]90Pelvis9, [100]0, [0]9Other cancer23, [72]9, [28]32 ## Theoretical Framework of Acceptability Constructs Table 5 shows patients’ acceptance for shared follow-up care across each construct in the Theoretical Framework of Acceptability. For Affective Attitude $85\%$ agreed that they would be satisfied for their follow-up care to be shared with their general practitioner as long as the radiation oncologist was still involved. Ethicality: $88\%$ agreed a shared cancer follow-up model fits with their values. Intervention Coherence: $88\%$ agreed that they understood the benefits of shared cancer follow-up care for themselves, their doctors and the health care system. Perceived Effectiveness: $87\%$ agreed that shared care was likely to achieve its purpose. Self-Efficacy: $75\%$ elected to have shared follow-up care; sub-analysis showed $97\%$ had confidence in their choice (p = < 0.001). Table 5Acceptance for radiation oncology shared follow-up care according to the Theoretical Framework of Acceptability constructsTFA ConstructsAgreeDisagreeAffective attitude$85\%$$15\%$Burden$30\%$$70\%$Ethicality$88\%$$12\%$Intervention Coherence$88\%$$12\%$Perceived Effectiveness$87\%$$13\%$Opportunity Costs$33\%$$77\%$Choose Shared Care$75\%$$15\%$Self-Efficacy$93\%$$7\%$ ## Acceptance and preferences for follow-up care Patients treated with only radiotherapy were associated with an increase odds of accepting shared care, odds ratio 1.707 ($95\%$ CI 1.051–2.773), Wald χ2[1] = 4.668, $p \leq 0.031.$ Additionally, patients who self-reported ‘Very good’ health had a statistically significant higher acceptance of shared care than those who self-reported their health as ‘Good’ ($$p \leq 0.008$$). However, health status was not a strong predictor of accepting shared care (χ2[4], 7.951, $$p \leq 0.093$$). The majority of patients preferred to have their radiation oncology follow-up for five years ($44\%$). Patients who preferred follow-up care for one year were 2.9 times more likely to accept shared care ($$p \leq 0.025$$), than those who wanted follow-up care beyond five years; and those who preferred follow-up care for three years were 7.5 times more likely to accept shared care ($$p \leq 0.012$$) than those who preferred care beyond five years. Patients treated at the regional hospital were 1.8 times more likely to want follow-up care to continue for over 10 years ($$p \leq 0.027$$), and five times more likely to want follow-up for life (p = < 0.001), compared to patients treated at the rural hospital. These results align with travel time. Patients who travelled less than 20 minutes one-way to their radiation oncologist had a slightly lower acceptance for shared care (mean rank = 186.08, $$p \leq 0.025$$) than those who travelled more than 20 minutes (mean rank = 207.15, $$p \leq 0.025$$). Although not significant, patients treated at the rural hospital had a slightly higher average acceptability score of shared care ($\frac{3.94}{5}$ compared to $\frac{3.86}{5}$ from the regional hospital). Logistic regression predicted patients’ acceptance of shared care (see Table 6). Patients with a high understanding of shared care (Intervention Coherence) were predicted to be seven times more likely to accept a shared cancer follow-up model of care; those with a high Affective Attitude were predicted to be three times more likely; those with a high Ethicality were two and half times more likely; and those with high Self-Efficacy were three times more likely. Other constructs were not significant in predicting acceptance of shared care. Table 6Odds ratio of Theoretical Framework of Acceptability and shared care acceptancedfSig. ORAffective Attitude1< 0.0013.231Burden10.3061.131Ethicality10.0312.497Intervention Coherence1< 0.0017.111Opportunity Costs10.1620.824Perceived Effectiveness10.7811.129Self-Efficacy10.0073.467 ## Discussion This multi-centre cross-sectional study evaluated patients’ acceptance of a shared cancer follow-up model of care between their general practitioner and radiation oncologist using the Theoretical Framework of Acceptability (TFA). We found that $80\%$ of patients accepted a radiation oncology shared follow-up model of care, and $75\%$ would choose shared care compared to the oncologist-led model if given a choice. Patients treated only with radiotherapy were more likely to accept shared follow-up care, and patients who preferred follow-up care for fewer than three years were more likely to accept shared follow-up care. The TFA constructs of Intervention Coherence, Affective Attitude and Self Efficacy were significant predictors of acceptance for shared cancer follow-up care. Previous qualitative research has found that patients are willing to accept shared cancer follow-up care if their oncologist remained remains involved and can oversee their care [6]. Although previous research does not distinguish between patients treated with only radiation therapy or other modalities, this study confirms that most radiation oncology patients would accept shared cancer follow-up care provided their radiation oncologist was still involved. However, the extent to how the oncologist was to remain involved was not explicitly addressed. It has been suggested that for the oncologist to remain involved and oversee the patient’s care, there is a need for improved two-communication and linkage of medical records between health professionals [6, 63]. Some patients require follow-up appointments with multiple specialists: radiation oncologist, medical oncologist, surgeon (for example, urologist, breast surgeon), and shared care has been highlighted as beneficial in reducing the number of appointments and duplication of assessments [6, 64]. However our results found that patients who only received radiotherapy treatment were more likely to accept shared follow-up care, and no significant difference with years since treatment was found. This is an interesting result, as patients treated with only one modality have fewer follow-up consultations than those treated with multiple modalities (who would be more likely to benefit from having fewer appointments). The higher acceptance for patients treated with only radiotherapy may be due to other unknown factors, such as long-term toxicity and treatment side effects and warrants further investigation. To our knowledge, this is the first quantitative study to apply the TFA, which helped determine factors that may predict a patient’s acceptance of a radiation oncology shared follow-up model of care. Patients with good Intervention Coherence, Affective Attitude and Self-Efficacy were significantly more likely to accept a shared care model. Additionally, these constructs were also useful in predicting acceptance and could be useful for health services to undertake readiness assessments. This finding is also supported by the Social Cognitive Theory that goes beyond the individual behaviour (Health Belief Model and Theory of Reasoned Action/Planned Behaviour) and considers interactions with social and environmental influences. According to Bandura [65], if people lack awareness of how their lifestyle habits affect their health, they have little reason to change; conversely, knowledge creates the precondition for change [65]. Therefore, the knowledge and understanding (Intervention Coherence) regarding the benefits of shared cancer follow-up care is important to consider before transferring the care of patients to their general practitioner in a shared care model. This finding is also supported by a recent study that found women need to be provided with the evidence that shared follow-up care is effective, so they can form a thorough understanding (Intervention Coherence) of what shared is, who is responsible for what and to understand that shared care will not negatively impact their health outcomes [66]. The TFA allows researchers and health services to determine which constructs require further attention to increase acceptance before implementing health interventions. Although there are several system barriers to implementing shared cancer follow-up care (such as the need for defined health professional roles [6], protocols, evidence-based guidelines [40, 46, 67], and communication tools [28]), acceptability to patients is fundamental. Our results support that shared cancer follow-up care needs to be individualised based on the patient’s cancer type, treatment type, current health, and personal preferences [50]. The American Society of Clinical Oncology suggested that “models of risk are needed to stratify survivors into different levels of intensity and setting for follow-up care. Components needed in such a model include risk recurrence, the persistence of moderate to severe toxicity or therapy, risk of serious physical late effects and psychosocial status" [68 p.634]. Another form of stratification to select appropriate patients for a shared care model beyond the clinical paradigm is to evaluate the patient’s acceptability toward shared care. In addition to the risk stratification for cancer patients, essential elements for shared care include improved communication between the general practitioner and oncologist [32, 69, 70]. It is equally important to provide patient-centred care, including engaging with patients and understanding their needs and preferences [71]. We show that patients with a strong understanding (Intervention coherence) of the benefits of shared care are seven times more likely to accept a shared care follow-up model. ## Study limitations To our knowledge, this was the first study that used the Theoretical Framework of Acceptability quantitatively, and there is limited guidance on applying the framework in survey format. The study was specific to radiation oncology follow-up; some patients may have confused this with their medical oncology or surgical oncology follow-up. Although this study had a good response rate, there is no information about the $55\%$ who declined to participate. It is possible that those who did not respond were less likely to accept shared cancer follow-up care, and response bias may be present. The authors were unable to conduct a non-report analysis. Additionally, there were few responses from colorectal cancer patients; this may be due to fewer colorectal cancer patients being treated with radiotherapy compared to breast and prostate patients. Finally, this study was conducted across a regional and rural population and may not be generalisable to the metropolitan population. However, based on our results, patients who travel less than 20 minutes to their oncologist were slightly less likely to accept shared care and may produce similar results in a metropolitan area where people live closer to cancer centres. The lead author is a critical realist researcher and acknowledges that many unobservable structures and events may influence the results. ## Conclusion There is a need to normalise shared cancer follow-up care into practice. However, normalising shared cancer care requires a multifaceted approach and support from specialists, general practitioners and patients. Based on the findings of this study, informing patients about the concept and benefits of shared care is important to foster acceptance. Follow-up care should be based on individual clinical risk and patient preference for follow-up care. Further investigation is needed to establish how the oncologist is to remain involved and oversee care in a shared care model, and to qualitatively research the acceptance among radiation oncologists, general practitioners and patients using the TFA constructs to inform clinical practice change. ## Electronic supplementary material Below is the link to the electronic supplementary material. Supplementary Material 1 ## References 1. Bernat J, Wittman D, Hawley S, Hamstra D, Helfand A, Haggstrom DA. Symptom burden and information needs in prostate cancer survivors: a case for tailored long-term survivorship care. 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--- title: 'EDDSN-MRT: multiple rodent tracking based on ear detection and dual siamese network for rodent social behavior analysis' authors: - Bingbin Liu - Yuxuan Qian - Jianxin Wang journal: BMC Neuroscience year: 2023 pmcid: PMC10044788 doi: 10.1186/s12868-023-00787-3 license: CC BY 4.0 --- # EDDSN-MRT: multiple rodent tracking based on ear detection and dual siamese network for rodent social behavior analysis ## Abstract ### Background Rodent social behavior is a commonly used preclinical model to interrogate the mechanisms underpinning various human neurological conditions. To investigate the interplay between neural systems and social behaviors, neuroscientists need a precise quantitative measure for multi-rodent tracking and behavior assessment in laboratory settings. However, identifying individual differences across multiple rodents due to visual occlusion precludes the generation of stable individual tracks across time. ### Methods To overcome the present limitations of multi-rodent tracking, we have developed an Ear Detection and Dual Siamese Network for Multiple Rodent Tracking (EDDSN-MRT). The aim of this study is to validate the EDDSN-MRT system in mice using a publicly available dataset and compare it with several current state-of-the-art methods for behavioral assessment. To demonstrate its application and effectiveness in the assessment of multi-rodent social behavior, we implemented an intermittent fasting intervention experiment on 4 groups of mice (each group is with different ages and fasting status and contains 8 individuals). We used the EDDSN-MRT system to track multiple mice simultaneously and for the identification and analysis of individual differences in rodent social behavior and compared our proposed method with Toxtrac and idtracker.ai. ### Results The locomotion behavior of up to 4 mice can be tracked simultaneously using the EDDSN-MRT system. Unexpectedly, we found intermittent fasting led to a decrease in the spatial distribution of the mice, contrasting with previous findings. Furthermore, we show that the EDDSN-MRT system can be used to analyze the social behavior of multiple mice of different ages and fasting status and provide data on locomotion behavior across multiple mice simultaneously. ### Conclusions Compared with several state-of-the-art methods, the EDDSN-MRT system provided better tracking performance according to Multiple Object Tracking Accuracy (MOTA) and ID Correct Rate (ICR). External experimental validation suggests that the EDDSN-MRT system has sensitivity to distinguish the behaviors of mice on different intermittent fasting regimens. The EDDSN-MRT system code is freely available here: https://github.com/fliessen/EDDSN-MRT. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12868-023-00787-3. ## Background Rodents are highly social mammals and are typically group-housed. Therefore, as expected, social interaction models based on rodent tracking are valuable experimental tools for investigating the mechanisms underpinning disease states alongside genetics, epigenetics, and pharmacotherapy for assessment of risk, vulnerability and the development of improved treatment strategies [1–7]. Conventional rodent tracking paradigms are usually based on video recordings of behaving rodents captured by a single overhead optical camera. As such, the experimenter must distinguish video frames by the presence or absence of visual occlusion and then track rodents in occlusion and non-occlusion frames respectively. Therefore, the main challenge of multi-rodent tracking is how to correctly identify individual rodents after they touch, cross, or are occluded by one another (i.e., the occlusion condition). Previous studies have addressed the occlusion interference problem with multi-rodent tracking primarily from three perspectives. First, is the use of social behavior models where physical contact between individuals is prevented, such as the three-chamber social test [8–10]. In these kinds of models, individuals are isolated either in individual cages or separated by Perspex walls, thus preventing conspecific interactions. However, because moving between areas is limited, such approaches do not permit a comprehensive investigation of the behavioral trajectories of spontaneous and freely behaving mice [16–19]. Second, is the use of bio-loggers (e.g., labels or tags) or special devices, such as radio frequency identification (RFID) or the use of multiple cameras during data recording [11–13]. With the aid of special equipment, these approaches can achieve a high level of tracking accuracy. However, attaching or implanting sensors into rodents has many disadvantages, such as the high cost of such devices and complex surgical requirements that could be considered an additional intervention. In addition, wearable devices and, in particular, implanted devices, may negatively impact their normal behavioral trajectories. For example, a transmitter implanted into the skull may necessitate long post-surgical recovery times, cause reduced range of motion, and loss of appetite leading to weight loss [20]. Intraperitoneally implanted transmitters have been reported to decrease spontaneous behaviors, such as running wheel activity [21, 22]. Wearable tags may negatively impact vision and olfaction, with unwanted effects on the behavior of conspecifics [23]. Third, is the use of end-to-end methods based on monocular videos with single-view depth estimation. This state-of-the-art method of Multiple Object Tracking (MOT) has been widely implemented in the tracking of pedestrians [14], vehicles [15], and animals in complex environments [16, 17, 20]. However, the inability to label recorded individuals, and the similarity across individuals’ appearances and a wide range of shapes, has led to undesirable methods to obtain accuracy, such as manual calibration and tracking over long time even when recordings are made in laboratory open field tests (OFT) with featureless, circular backgrounds. To overcome the limitations of current methodologies, the aim of this study is to develop a behavioral recording system based on Ear Detection and Dual-Siamese Network for Multiple Rodent Tracking (EDDSN-MRT) in laboratory environments. We propose that the EDDSN-MRT system will address the aforementioned challenges in the tracking of occluded frames. We will validate our EDDSN-MRT system using a publicly available dataset on behaving mice and compare our results with a selection of state-of-the-art methods [24, 25] to determine whether our system can complete tracking operation in occlusion fragments and perform comparably, if not better, than those in current use. In addition, we will validate our system using an additional dataset to determine whether the EDDSN-MRT system can perform behavior analysis, including characterization of locomotion and movement phenotyping, and group-level location distribution profiling. ## Results for ear detection network training We first evaluated our ear detection network (EDN) on Dataset A and compared these results with several state-of-the-art object detection methods (Table 1). Dataset A contained a freely behaving mouse in the open field test (OFT) which was determined to be suitable for training and testing ear detection. Table 1Comparison of performance for our ear detection network on dataset ABenchmarkEpochResolutionOur EDN(P)Yolov5 %Yolov3 %Efficient Det %mAP at 0.5501280$72097.07\%$ (0.0918)96.1593.2295.41mAP at 0.5:0.9501280$72064.32\%$* (0.0018)59.7645.1352.29EDN, ear detection network; mAP, mean average precision.means $P \leq 0.05$ We found that our EDN increased mAPat 0.5:0.9 (mean average precision at Intersection over Union which is abbreviated as IoU, is from 0.5 to 0.9) by $12.03\%$ compared with a well-known object detection model proposed in [27] (called “Efficient Det”) ($52.29\%$), showing the effectiveness of data augmentation and adaptive anchor box functions. Due to the extreme size of subjects in Dataset A (about 5 to 8 pixels each), these two functions exerted a greater impact on performance in this dataset compared to the public dataset. However, the difference was smaller but significant in mAP at 0.5 (mean average precision at IoU is 0.5, $97.07\%$ vs $95.41\%$, $$P \leq 0.0038$$), suggesting the metric index of the original network was already too high to be obviously improved. When we compared the results of the object detection model YOLOv5 [28] and the EDN presented in this study, we found that our EDN had a relatively higher mAP at 0.5 ($97.07\%$ vs $96.15\%$, $$P \leq 0.0918$$) and significantly higher mAP at 0.5:0.9 ($64.32\%$ vs $59.76\%$, $$P \leq 0.0018$$) compared with YOLOv5. As mentioned above, the small difference on mAP at 0.5 may be due to the fact that the performance of the original framework is already very high and therefore it is hard to outperform. Compared to the second best-performing detection model (Yolov5), the EDN improved mAP at 0.5:0.9 by about $4\%$.It indicates that the EDN with our new designed Neck and Head modules achieves a better ear object detection performance. ## Results for multi-rodent tracking At the core of the tracker is a biometric feature (ear) based algorithm which provides immense flexibility to track multiple mice. Examples of tracking videos obtained using our proposed methods are available to view in Supplementary Material (Additional file 1: Movie S1, Additional file 2: Movie S2). As shown in Table 2, we evaluated the EDDSN-MRT system on Dataset B. This dataset is a public dataset containing 6 video clips [24]. We have numbered the videos B1 to B6. Both B1 and B2 videos contain 2 individuals. The total number of frames in B1 and B2 are 16000 and 36468, respectively. It shows that the missing IDs of the three methods (Toxtrac:idtracker.ai:EDDSN-MRT) are 8730:0:0 and 32441:0:0 (proportional figures on B1 and B2). But the ID drifting is 0:349:135 and 0:1101:730. Importantly, the results of idtracker.ai and EDDSN-MRT show that the number of missing IDs is zero. For the results of Toxtrac, the Drifting ID is zero. Numerically speaking, the detection performance of idtracker.ai and EDDSN-MRT should be better. However, due to the poor detection performance of Toxtrac, many IDs were lost, therefore the problem of ID drift is removed, i.e., since the ID cannot be detected, there is no tracking operation. The MOTAs (Multiple Object Tracking Accuracy) results were $72.6\%$: $97.8\%$: $99.1\%$ and $55.4\%$:$97.0\%$:$98.0\%$. The ICRs (ID Correct Rate) were $67.1\%$:$98.1\%$:$99.5\%$ and $38.4\%$:$96.9\%$:$99.0\%$. As such, regardless of whether MOTA or ICR was used as the comprehensive evaluation index, it was determined that idtracker.ai and EDDSN-MRT perform well, and EDDSN-MRT is comparatively better than all those tested (all $P \leq 0.05$). The performance of Toxtrac was far worse than EDDSN-MRT and idtracker.ai. Table 2Performance comparison of multiple rodent tracking on all frames in dataset BToxtrac [25]idtracker.ai [24]EDDSN-MRT (Ours)IDNoMissSwitchDriftMOTAICRMissSwitchDriftMOTAICRMissSwitchDriftMOTA(P)ICR(P)B$1287301796072.63\%$$67.11\%$$023434997.79\%$$98.18\%$$0013599.16\%$ (< 0.001)$99.58\%$ (< 0.001)B2232,44112,$522055.42\%$$38.35\%$$01170110196.95\%$$96.89\%$$01673098.00\%$ (< 0.001)$98.98\%$ (< 0.001)B3211,$2577195072.95\%$$55.83\%$$252380049197.01\%$$89.12\%$$01045197.84\%$ (< 0.001)$98.90\%$ (< 0.001)B42790713,$903080.95\%$$47.81\%$$6633218098.96\%$$98.62\%$$0018599.11\%$(0.117)$99.56\%$ (< 0.001)B5464,37059,$250068.19\%$$39.34\%$217513,$726098.93\%$$92.20\%$$01483299.18\%$ (< 0.001)$99.58\%$ (< 0.001)B6[1]14None2NoneNoneNoneNone$90407420096.43\%$$89.02\%$$024124098.34\%$ (< 0.001)$99.16\%$ (< 0.001)B6[2]14None2NoneNoneNoneNone$68004120096.90\%$$92.54\%$$00126198.28\%$ (< 0.001)$99.14\%$ (< 0.001)1Since there is an occluded fragment in video B6 caused by manual operation, individual information cannot be obtained in this fragment. Therefore, video B6 is divided into two parts to implement our method after deleting this fragment. idtracker.ai is not affected by this occlusion2Toxtrac does not run on video B6, therefore no results are presentedMOTA, Multiple Object Tracking Accuracy; ICR, ID Correct Rate. No., the number of rodents in the video. * means $P \leq 0.001$ The duration and frame numbers of videos B3 and B4 were very close, therefore they are combined for discussion. Unlike the results of B1 and B2, the number of Missing IDs was not zero for the idtrackerai’s results of B3 and B4. Therefore, these results are made with undetected IDs in both videos, and the number of errors due to ID switching increased dramatically, greatly exceeding the number of errors due to ID drifting. This also indicates that idtracker.ai has degraded performance on these two videos. Compared with the issue of a large increase in ID missing in the output of idtrakcer.ai, the number of Missing IDs in the result of EDDSN-MRT was still zero. This indicates that the performance of EDDSN has not declined while the difficulty of tracking individuals in the video increasing, demonstrating the superior performance of the EDDSN-MRT system. The performance on B3 and B4 of Toxtrac was similar to that on B1 and B2 inasmuch as a large number of Missing IDs occurred, demonstrating poor performance in object detection. The MOTA and ICR indicators of the three methods on B3 and B4 were also similar to those on B1 and B2, with EDDSN-MRT getting the highest score, idtrackerai second, and Toxtrac the worst. Video B5 and B6 are different from the previous videos in that they contain 4 mice in each. The idtrackerai and EDDSN-MRT were run on these two videos for comparisons. Due to the occlusion caused by manual operation for a period in B6, it was cropped into two segments for the EDDSN-MRT run. Toxtrac could not be run on B6, resulting in missing data for these two videos (Table 2). As the results in Table 2 demonstrate, our EDDSN-MRT method consistently generates output with no missing IDs, sporadic ID switching and ID drift. This suggests that the performance of our method has not degraded in this kind of video, where more subjects are present, and the video duration time is longer. By comparison, the performance of Toxtrac and idtracker.ai show greater degradation. It is worth mentioning that the number of ID drift errors in the results of idtracker.ai have been reduced to 0 at this time (similar to the results of Toxtrac). However, there are still a lot of ID drift errors in EDDSN-MRT. As in the previous analysis, the number of ID drift errors dropped to 0 does not mean better tracking performance. Rather, because the detection performance is so poor, most video frames do not even enter the stage of ID tracking. If we observe the two-evaluation metrics from a global perspective, we will find that MOTA is insensitive to ID switching errors. The fact that only a few ID switches occur but the mice hold the wrong ID for a long period of time does not significantly reduce the MOTA assessment. We repeated the above assessment on occlusion frames within Dataset B to verify the robustness of our method where subjects are occluded. We compared the tracking performance of all three methods in occlusion frames of the videos used (Table 3). The results show that EDDSN-MRT performs significantly better than Toxtrac and idtracker.ai in terms of ICR and MOTA in occluded frames (all $P \leq 0.001$).Table 3Performance comparison of multiple rodent tracking on occluded frames in Dataset BToxtrac[25]idtracker.ai[24]EDDSN-MRT (Ours)IDNoMissSwitchDriftMOTAICRMissSwitchDriftMOTAICRMissSwitchDriftMOTA(P)ICR(P)B$12665161909.84\%$$1.42\%$$021830189.82\%$$90.84\%$$0011997.05\%$ (< 0.001)*$98.52\%$ (< 0.001)B2220,$9641151010.65\%$$6.1\%$$098643092.54\%$$90.29\%$$01472194.12\%$ (< 0.001)$96.97\%$ (< 0.001)B$32841221604.25\%$$3.91\%$$176150444372.27\%$$60.80\%$$0645190.19\%$ (< 0.001)$95.00\%$ (< 0.001)B$4277374656049.82\%$$30.97\%$$4018217097.72\%$$97.68\%$$0018598.50\%$ (< 0.001)$99.25\%$ (< 0.001)B5461,21015,$080020.26\%$$4.50\%$$18928112089.54\%$$81.07\%$$0283298.26\%$ (< 0.001)$99.13\%$ (< 0.001)B6[1]14None2NoneNoneNoneNone$34291622079.81\%$$73.32\%$$012123890.05\%$ (< 0.001)$94.95\%$ (< 0.001)B6[2]14None2NoneNoneNoneNone$27562130076.02\%$$65.57\%$$00121188.11\%$ (< 0.001)$94.06\%$ (< 0.001)MOTA, Multiple Object Tracking Accuracy; ICR, ID Correct Rate. No., the number of rodents in the video. ** means $P \leq 0.001$ ## Ablation study for Video B1 To verify the effectiveness of each component in EDDSN-MRT, we designed an ablation study for Video B1. The first component of the ablation study was designed to demonstrate the effectiveness of ear detection-based methods (EDB) using tracking with traditional torso detection-based methods (TDB) (Table 4).Table 4Results of the ablation study to verify the effectiveness of each EDDSN-MRT componentMethodICRID1P valueTBD + IEDN + DSN$86.77\%$16,000P < 0.0001EBD + EDN + DSN$98.20\%$16,000P < 0.0001EBD + IEDN + SSN$67.40\%$16,000P < 0.0001EBD + IEDN + DSN$99.58\%$($P \leq 0.0001$)16,000–1The P value is obtained by comparing the ICR of each other method and the EDB + IEDN + DSN methodEBD, Ear Based Detection method; EDN, Ear Detection Network(original); DSN,Dual-Siamese Network; ICR,ID Correct Rate; IEDN,Improved Ear Detection Network; TBD,Torso Based Detection method; SSN, Single-Siamese Network The results indicate that TBD + IEDN + DSN used the rodent torso as the target to implement object detection (Torso Based method, TDB) and perform tracking operations, which performed well in terms of correct IDs ($86.77\%$). However, using the ears as targets improved object detection and correct IDs ($99.58\%$), and performed significantly better than TBD ($P \leq 0.0001$). The second component of the ablation study was to demonstrate the effectiveness of the improved ear detection network (IEDN), which uses ear detection with the original PANet (EDN). The second and fourth row of Table 4 shows that improved PANet can significantly improve ICR from $98.2\%$ to $99.58\%$ ($P \leq 0.0001$). Combined with the data shown in Table 2, it is clear that the object detection framework using enhanced PANet has a greater ability to locate targets (improved mAP at 0.5:0.9 from $58.58\%$ to $64.32\%$, $P \leq 0.0001$), making this method suitable for variable environments. These results also indicated that the IEDN is effective for both rodent detection and tracking. The dual-Siamese network framework used in this study has two independent Siamese networks: one is used to process image information of rodent subjects, and the other one is used to preserve spatial information. To show the effectiveness of the dual-Siamese network, we compared its performance with the traditional Single-Siamese network (SSN), which only processes images to validate the effectiveness of DSN. The ICR of DSN is $32.17\%$ higher than the one of SSN (Table 4, $P \leq 0.0001$). The reason may be that the area of the mouse ear is very small—even in 1920 × 1080 resolution, it is still only 30 × 30 pixels in size. Furthermore, it is difficult to solely use image features for tracking without using spatial information for constraints. These factors validate the necessity of DSN and also show how the presence or absence of spatial information can have a big impact on the performance of the entire tracking framework. ## Results of velocity We monitored the movement of 32 subjects and obtained 32 tracking trajectories, the average velocities of each subject, and the velocities of each subject per 5-min time block (the video is 40 min in total). Compared with the single-session experiment, the group analysis reveals diverse locomotion characteristics. It has been suggested that as individuals age, damaged mitochondria produce less adenosine triphosphate (ATP) and more reactive oxygen species (ROS) accumulate, resulting in depression-like symptoms and in turn a weakening of locomotion ability [29, 30]. This was also observed in the results of this experiment (Fig. 1), where the older mice (aged 18 months) demonstrate a lower average velocity in both the AL (ad libitum feeding) and the IF (intermittent fasting) groups (both $P \leq 0.05$). Compared with the older mice, the younger mice (aged 3 months) with the same feeding schedule had the greater frequency of ambulation. According to the previous research [31], an IF intervention may alleviate depressive symptoms, which could improve locomotor performance and range of motion of monkeys and rodents. Fig. 1Monitoring of individual and group locomotion characteristics—Assessment of velocity. a Average velocity of the 3-month group ($$n = 8$$) for both intermittent fasting (orange bars) and ad libitum feeding groups (blue bars), and b average velocity of the 18-month group ($$n = 8$$) for both intermittent fasting (orange bars) and ad libitum feeding groups (blue bars). All data are presented in 5-min time blocks. Bars indicate group-level averages, error bars indicate standard deviation, and individual dots represent individual subjects (mice) We recorded the average speed of mice of each group over 40 min (2400 data per group) and performed Wilcoxon rank sum test on the speed data of two groups of mice in the same age. There were significant differences in velocity between IF and AL mice in both young and old groups (both $P \leq 0.001$). And IF mice had significantly higher average velocities compared with AL mice in young (6.08 vs. 5.04 cm/sec) and old (3.32 vs. 2.54 cm/sec) groups, consistent with previous findings [31]. In order to clarify in which time period the difference in velocity primarily occurred, we performed the Wilcoxon Rank Sum Test on both age groups within the 40-min time period in 5-min units. We found that significant differences in velocity were concentrated in the 21-25 min period ($P \leq 0.05$ in both age groups) (Table 5). This pattern was observed in both young and old age groups. Furthermore, we observed that IF mice were more active than the average level of activity during this period (Fig. 1a, b), which was not found in the AL mice. Table 5Wilcoxon Rank Sum Test results on the velocity of intermittent and ad libitum feeding mice in young and old age groups across 5-min units of timeTime (min)1–56–1011–1516–2021–2526–3031–3536–403-month0.03910.07810.25000.14840.00780.46090.14840.054718-month0.64060.54690.74220.38280.00780.14840.14840.6406 ## Spatial distribution of mice and time spent in a specific location The AL mice in the 18-month age group were walked further and were more widely distributed within their environment (Fig. 2A–D). By contrast, mice in the IF group were more likely to cluster together. This phenomenon was most observed in the older, 18-month-old mice. To quantify this, we calculated the two-dimensional (2D) standard deviation distribution coordinates of these mice. The standard deviation in 2D Euclidean space is the extension form of the one in 1D space and can be calculated as follows (Eq. 1):1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma = \sqrt {\frac{{\mathop \sum \nolimits_{$i = 1$}^{n} \left({x_{i} - \overline{x}} \right)^{2} }}{n}}$$\end{document}σ=∑$i = 1$nxi-x¯2nFig. 2The spatial distribution of mice ($$n = 8$$ per graph) and time spent in a given region. Histograms indicating spatial location and time spent in the location for all mice in each of the feeding regimens and age groups. Graph A shows the distribution of the 3-month-old AL group, B shows the distribution of the 3-month old IF group, C shows the distribution of the 18 month old AL group, and D shows the distribution of the 18-month old IF group. Each histogram was constructed by computing the percentage of time spent in a given pixel. Data were smoothed and presented in log scale However, mice are distributed within a 2D matrix with two variables, x (horizontal coordinate) and y (vertical coordinate). Therefore, to extend Eq. 1 to a 2D matrix, it is written as follows (Eq. 2):2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\sigma = \sqrt {\frac{{\mathop \sum \nolimits_{$i = 1$}^{n} \left({x_{i} - \overline{x}} \right)^{2} + \left({y_{i} - \overline{y}} \right)^{2} }}{n}}$$\end{document}σ=∑$i = 1$nxi-x¯2+yi-y¯2n The result shows that in the young mice group, the standard deviation of the AL and IF mice is 20.62 cm vs. 19.05 cm, respectively. In the old mice group, the standard deviation of the AL and IF mice is 18.59 cm vs. 15.82 cm, respectively. These results demonstrate that in both age groups, the AL mice have a larger spatial distribution. In order to reduce the error caused by the difference in areas of activity in individual mice versus the overall activity area of the group, we analyzed the activity area of every mouse separately. Since the video resolution is 1280 × 960, we divided the main region of the open field (from 320 to 960 on the horizontal axis, and 240 to 720 on the vertical axis) into 12 regions. Each region was 160 × 160 in size and numbered 1–12 (Fig. 3a) and histograms were generated for all groups (i.e., young vs. old mice, and AF vs. IF mice) (Fig. 3b). Finally, we plotted the histograms for each individual mouse to represent their location within the open field test and the proportion of time each mouse spent within the twelve described locations (Fig. 4). Although significant differences were not found (Wilcoxon Rank Sum Test), a trend was observed suggesting that the IF mice preferred to stay in fewer areas compared to the AL mice, and the space within which IF mice were distributed was far smaller than the AL mice. This finding was consistent across both individuals and groups. Fig. 3Representative photo of mice in the open field test and histograms of each group’s distribution and time spent in each location within the open field test. A The open field was divided into 12 regions for analysis, and B histograms were created to show the spatial distribution of mice and time spent in each location. OFT, open field testFig. 4Histograms showing each individual’s spatial distribution and proportion of time (%) spent in each of the twelve locations within the open field test ## Discussion This study presents a novel approach for ear detection, the EDDSN-MRT system, which avoids occlusion interference in multiple object detection analyses. This approach makes multiple rodent tracking based on object detection accessible and is an improvement on whole-body detection which is vulnerable to occlusion. To adapt the EDDSN-MRT system for detecting ears of small sizes, we improved the existing PANet structure to obtain more detailed features from low-level layers. In the conventional architecture of object detection networks, PANet is an independent component for feature extraction. Therefore, this improvement could be applied to most current ODNs similar to Yolo. Furthermore, it is feasible that the EDDSN-MRT system would be compatible and adaptable to a new ODN with better performance in the future. Since spatial and image information is extracted by an ODN, we used a dual-Siamese-network to measure the similarity between images of a pair of ears and spatial information in adjacent frames to assign identification to individual mice. Comprehensive and unbiased locomotion phenotyping is an emerging and powerful approach for assessing abnormal social behaviors in animal models of mood and depressive disorders [29–31]. In this study, we validated the application of the EDDSN-MRT system in the monitoring of social behavior of intermittent fasting and ad libitum feeding mice of different ages. Interestingly, we found that mice with an intermittent fasting intervention were significantly more active in spontaneous movement compared to the ad libitum feeding mice. This difference was most obvious in the 20–25-min timeframe (Table 5, both $$P \leq 0.0078$$). Previous studies have suggested that an intermittent fasting intervention could modulate mood and social behaviors in rodent models, relieving symptoms of depression and anxiety in mice [34–36]. This relief of symptoms would be evinced by an increase in spontaneous locomotion and a larger dwelling distribution of mice. However, the results of the open field test presented here showed the opposite findings. Compared with the ad libitum feeding mice, the mice with the IF intervention had a smaller dwelling range. This could be interpreted as a sign of stable or increasingly worse depressive and/or anxiety symptoms. However, it is well-known that fasting induces a lower body temperature [37–44]. This likely results in reduced physical agility and the desire to maintain body temperature by clustering, leading to a smaller range of locomotion. Therefore, intermittent fasting not only impacts on the mood of mice, but also on their physiological functioning. Lastly, we would like to discuss the limitations of our proposed system. Mouse (or rodent) ears are a type of biometrical characteristic (BMC), but the BMC tracking performance heavily depends on the ODN designed for the specific feature. However, in some cases using ears for rodent tracking may be unreliable because of various problems such as not all rodents have such distinctive ears, and some types of rats (e.g., those with white fur) show very slight differences between the fur colour and the ear colour, In this case, it is difficult to identify the ears well, thus, we would need to select a new BMC for tracking. As a next step, we are considering using generative adversarial network or semantic image segmentation to generate visible BMC marks for rodent subjects to enhance the performance of the ODN. Solving these problems will extend the applicability of our framework to the benefit of the animal behavioral research community. ## Conclusion The EDDSN-MRT is an automated pipeline framework for multiple rodent tracking. The system is robust to solve the occlusion problem in multiple individual tracking via tracking rodent ears as opposed to the entire rodent’s body. EDDSN-MRT can greatly improve the study of rodent movement and behavior by reducing the video processing time, avoiding observer bias, and allowing transparent, reproducible workflows. Experimental results show that when compared with the current approaches, our proposed EDDSN-MRT achieves better performance in identification assignment for tracking individual mice. It also helped us to observe unexplained effects of intermittent fasting on rodent behavior in the laboratory. ## Method In the following sections, we demonstrate several advantages of the EDDSN-MRT system for tracking multiple rodents compared with several existing state-of-the-art animal tracking object detection methods using multi-rodent behavior datasets. ## Experimental procedures We first divided frames into occlusion frames and non-occlusion frames via a segmentation process, followed by implementation of tracking operations (see Fig. 5 for the pipeline of the proposed EDDSN-MRT system). Because of the occlusion of individuals, some blobs in occlusion fragments could contain multiple individuals in space. As such, it was not possible to assign identification directly in the same manner as that in non-occlusion frames. To overcome this, the following three steps were implemented for the tracking operation in occlusion frames. The first step was ear labeling. Before tracking in occlusion frames, we first selected rodent ears as the key points for tracking since they are least likely to be occluded by individuals touching or crossing. The ear images, as opposed to the whole body, were used as machine learning input in order to train an ear detection network (EDN) based on Path Aggregation Network (PANet) [26] to locate and identify the ears of individual rodents. This step enabled the extraction of the ears’ (and individuals’) position in space and its image characteristics. In addition, we utilized a dual-Siamese network for spatial information and image characteristics of the detected ears as additional input to calculate the similarity between two frames that were used to assign identification of each rodent. Within the EDN, similarity calculating and ID assigning in occlusion fragments were performed. We then tested the EDDSN-MRT system using a publicly available dataset on behaving mice. We compared the results of the EDDSN-MRT system with a selection of state-of-the-art methods [24, 25] to determine whether our system could complete tracking operations in occlusion fragments and perform comparably, if not better, than those in current use. In addition, we validated our system using an additional dataset to determine whether the EDDSN-MRT system could perform behavior analysis, including characterization of locomotion and movement phenotyping, and group-level location distribution profiling. Fig. 5The pipeline of the proposed EDDSN-MRT system. Data preprocessing: [1] recordings are captured from a single optical camera; [2] frames with and without individual occlusion are identified; Tracking individuals in non-occlusion frames: [1] an algorithm based on blob overlapping is used to assign identities; Tracking in occlusion frames: [1] the ear detection network is trained with images of labeled ears; [2] the object detection network is used to characters the spatial and image features of individual ears; [3] a dual-Siamese network is trained using the spatial and image features of individual ears; Final Tracking Result Generation: the final result is a combination of tracking in both occlusion and non-occlusion frames ## Dataset A This unpublished dataset contains one clip which was used in training and testing of the object detection network (ODN). The video stream was recorded inside a glass chamber (size 50 length × 30 width × 35 height cm3). The chamber did not have a roof and the walls were high enough to prevent the mice from escaping. The bottom of the cage was covered with a polyvinyl chloride plastic sheet for building a featureless background. The camera was set 50 cm from the top of the ground. The sample was one male C57BL/6 J mouse (aged 3 months, obtained from the SLRC laboratory animal center, Shanghai, China) who was single-housed in an individually ventilated cage (Type 500) in a temperature (22° ± 2 °C), humidity (45–$65\%$) and light controlled room with a 12–12 h light–dark cycle (12 h of lights on starting at 6:00 am, and 12 h of lights off starting at 18:00 pm). The length of the video for training and testing was 17 min and 20 s, with 51,600 frames in total. The clip had 1080P original resolution and 60FPS frame rate. Details of Dataset A are shown in Table 6.Table 6Details of the three datasets used in this studyDatasetIDVideo nameNumber of samplesAgeIntervention typeFrames per secondDurationResolutionAA11C57BL13-mouthNone6017′20”1920 × 1080BB12aguties2UnknownUnknown4905′19”984 × 557BB22negroscanosos2UnknownUnknown4912′24”984 × 557BB32negroslisocanoso2UnknownUnknown4907′06”984 × 557BB42negroslisos2UnknownUnknown4907′06”984 × 557BB54 black mice [1]4UnknownUnknown2533′58”1272 × 909BB64 black mice [1]4UnknownUnknown2452′54”1272 × 894CC13 m-IF83-mouthPeriodic fasting4840’1280 × 720CC23 m-AL83-mouthNone4840’1280 × 720CC318 m-IF818-mouthPeriodic fasting4840’1280 × 720CC418 m- AL818-mouthNone4840’1280 × 720 ## Dataset B Dataset B is a public dataset that contains 6 clips of video used for validation of tracking systems performance [24]. Two videos with four mice were recoded inside a translucent plastic cage (size 30 length × 47 width × 35 height cm3) chamber inside a bigger tank made of glass. There was no roof on the chamber and the walls were high enough to prevent the mice from escaping. Four videos with two mice were recorded in a transparent plastic cage (size 18 × 32 × 20 cm3) covered with a transparent Perspex roof to prevent the mice from escaping. In both cases, the bottom of the cage was covered with sawdust for the comfort of the animals. Cameras were set around 110 cm and 100 cm from the top of the ground for the four-mice and two-mice videos, respectively. With the exception of the agouti mouse in the video named 2aguties, the other mice were presumed to be C57BL mice. Details of Dataset B are also shown in Table 6. ## Dataset C This dataset (unpublished) contains 4 clips of video used for monitoring of rodent movements in experiments. The video streams were recorded inside a plastic cage size 60 length × 45 width × 37 height cm3). There was no roof on the cage and the floor was uncovered. The camera was set 50 cm from the top of the floor. The sample was a group of male C57BL/6 J mice ($$n = 32$$ subjects in total) obtained from the SLRC laboratory animal center, Shanghai, China). Two groups were obtained, one aged 3-month and the other, 18-months ($$n = 16$$ for each age group). The mice were housed in IVC cages (Type 500, 4 mice per cage) in a temperature (22° ± 2 °C) and humidity (45–$65\%$) controlled room with 12–12 h light–dark cycle (12 h of lights on starting at 6:00 am, and 12 h of lights off starting at 18:00 pm). For each age group, the mice were divided into intermittent fasting (IF) and ad libitum feeding (AL; the sham group) groups (as shown in Table 6). The paradigm of IF involves periodic dietary restriction in a fasting week, in which the mice are fasted every other day, i.e., fasted one day and fed ad libitum one day. Feed pallets for the IF group were provided or removed at 10:00 am every day. The periodic fasting operation lasted for one week, and the mice in the IF groups were allowed to be fed ad libitum for every other week. For each age group, one of them was set as the IF group and the other, the AL (sham) group. Water was available ad libitum for all mice, regardless of group allocation. The recording operations were performed 8 weeks later when the mice were put on fasting, and filming was between 8:00–10:00 am. Both the IF and AL group animals were fasted overnight with no access to food for 8–10 h before recording. The length of each video was 40 min. Details of Dataset C are shown in Table 6. ## Data processing Like most conventional multiple animal tracking approaches, we divided the frames into occlusion frames and non-occlusion frames as part of preprocessing. The first step was segmentation [45, 46], where given a frame of video, it was necessary to distinguish between pixels associated with subjects (i.e., the mice) or the background. In this step, each frame was normalized with respect to its average intensity to correct for illumination fluctuations. It was also possible to implement background subtraction by generating a model of the background calculated as the average of a collection of frames obtained via subsampling the video. And according to the standard notation in the terminology the image processing field, here we refer to a collection of connected pixels that are not part of the background as a blob. The second step was frame classification, where frames were divided into occlusion frames and non-occlusion frames. In the open field test, the number of rodent subjects was a constant value declared by the user. It was possible to perform a comparison between the number of calculated experimental subjects and the number of actual experimental subjects to distinguish whether or not frame occlusion occurred. Put simply, when the number of blobs in a frame corresponded to the actual number of subjects, we considered this frame as a non-occlusion frame. In contrast, if the number of blobs and subjects did not match, we designated this an occlusion frame. ## Tracking in non-occlusion frames In non-occlusion frames, the mice are not occluded by default. Thus, one blob can be used to represent one individual. In this case, blob data can be used to generate continuous individual trajectories that track the motion of individual subjects. In videos with high frame rates, a rodent’s location in space does not change too much in the gap between two adjacent frames. Therefore, if we overlay two adjacent frames into one image, the pair of blobs representing the same individual would share a large number of pixels in space. As such, in adjacent frames, the blob with the most overlapping pixels inherits the identification of the blob in the previous frame and the identifications can be assigned for every blob frame-by-frame. Technologies in non-occlusion tracking are simple and validated [47]. ## Tracking in occlusion frames Because of the occlusion of subjects, some blobs in occlusion fragments would contain multiple individuals in space. Therefore, we cannot assign identification directly like the operation in non-occlusion frames. In this study, the tracking operation in occlusion frames consisted of three main steps. The first step was ear labeling, and the ear was used for tracking in occlusion frames as opposed to the whole body of subjects. Following this, the object detection network was used to extract the location of ears in occlusion frames. The last step was using a dual-Siamese network to assign identification to located ears. ## Ear labeling Since the video in Dataset A only contained a single rodent individual, the labeling could be completed by implementing two embedded single-target tracking operations with manual calibration. The first embedded operation was used to track the entire body of the subject in order to build a new video with cropped frames (the frames only contained the region of the rodent’s body). The second one was used to extract ears in the video for labeling in the original video clip. Due to the featureless background of the original video, the single target tracking operations could be simply replaced by two threshold segmentation processing. The first one was used to segment rodents and the background. The second one was for the ears and the body. ## Ear detection network Because of the extremely small size of mouse ears, the conventional detection network lacked interpretability of extremely small size objects, resulting in a low accuracy. In this case, improving the detection of small objects was necessary. In this step, we applied YOLOv5 [28] as the prototype framework due to its flexibility in modification to improve it (Fig. 6a).Fig. 6The structure of the proposed object detection network. a The detection network consists of three main parts: The [1] Backbone, a replaceable convolutional neural network for clustering and forming image features from fine and coarse gained images; The [2] Neck, a series of network layers for fusing and combining image features which are then sent to the predicting network, and [3], The Output, a network for prediction of image features, generation of bounding boxes and prediction of results. b The sub-module components of the detection network: [1] Convolutional layer. [ 2] Batch normalization operation. [ 3] Leaky Relu activation function. [ 4] Slicing operation. [ 5] Concatenate function puts slices into a block with 4X channels As shown in Fig. 6, the EDDSN consists of three main parts: the Backbone, the Neck and the Output (the Head). The Backbone module is a convolutional neural network that aggregates and forms image features at different granularities. The Neck module is a series of layers to mix and combine image features which are passed forward to prediction. Then, the features from the Neck module were input into the Output module that used convolution layers to achieve ear detection. The sub-module components of the EDDSN are shown in more detail in Fig. 6b. The Focus module transferred spatial information to the channel dimension on the input images to help reduce the parameters used in the network to get faster inference without mAP penalty. The CBL module consisted of a Conv + BN + Leakyrelu activation function. The Conv is convolutional network and BN is batch normalized processing. The Res-unit, which is borrowed from the residual structure of the Resnet network, is used to build a deeper network. The CSP1_n is borrowed from the CSPNet network structure, is composed of a CBL module, a Res-unit module, and Conv and Concate. The CSP2_n is borrowed from the CSPNet network structure, which consists of Conv and n Res-unit modules. The Concate module is the Focus structure, which first concatenates multiple slice results, and then feeds them into CBL module; the SSP module uses the maximum pooling method to perform multi-scale fusion [48, 49] The images were first input to the Backbone for feature extraction, and then fed to PANet for feature fusion. Finally, the Head is the output of the detection results. Similar to other methods in the same field, transfer learning methods using pre-trained models can shorten the training time and improve accuracy. Here, we tested the performance of the YOLOv5 models with and without pre-trained weights, and the results are shown in Table 7. It can be seen that on mAP at 0.5 and mAP at 0.5:0.9 (mean average precision at IoU is 0.5 and from 0.5 to 0.9), the performance of the models with pre-trained weights performs relatively better. Table 7Performance difference between pretrained and non-pretrained modelsModel (Large CSPDarknet as backbone)mAP at 0.5 %mAP at 0.5:0.9 %Improved framework with pretrained97.07 ($$P \leq 0.63$$)64.32 ($$P \leq 0.0017$$)Original YOLOv5 with pretrained96.1058.58Improved framework without pretrained96.8259.73Original YOLOv5 without pretrained96.1858.15 Due to the requirement to use transfer learning strategy in this study, the pretrained weight was loaded to model Backbone (CSPDarknet) for improving performance and this part of the framework cannot be modified in structure (illustrated by the box marked as Backbone: CSPDarknet in Fig. 6a). The Model Neck is an inverted pyramid structure similar to PANet. And in this case, because of the inability to make modifications in the backbone part, the improvement could only be implemented on the Neck and Head. Instead of the original structure, the improved Neck and Head have the fourth connection of information stream from the low-level layer of the model’s Backbone (illustrated as the red box in Fig. 6a). This modification would improve the pyramid structure for better performance in obtaining low-level information and detailed information, thereby making a better performance for detecting objects of extremely small size. ## Identification assignment based on a dual Siamese network Essentially, multiple object tracking in the video stream is a kind of identification assignment in adjacent frames. For ear tracking, it was necessary to associate each cropped image of ears in a frame with the ones in the previous frame. As we know, the biometrics characteristic extracted from a frame of a high-speed video has the uniqueness of morphology with the ones of the same individual in adjacent frames, as well as spatial information. Therefore, the similarities of image characteristics and spatial information of ears can be used as measurement metrics to implement identification assignment. Hence, we propose a fusion framework with dual Siamese-networks as Backbones that can process both spatial information and image feature information. The network structure is shown in Fig. 7. The spatial information and image feature information of a single ear in two adjacent frames are processed respectively by two independent Siamese networks [50–52]. Since the inputs of two independent Sub-Siamese-Networks are not of the same kind, the architectures of each are different. The Siamese network for processing images (as shown in Fig. 6b) is like another traditional Siamese network for matching images, in that it needs a convolutional network to extract features. Therefore, ResNet50 [50] was selected to perform this function. However, in the sub-network for processing coordinates, this convolutional architecture was omitted since the coordinate is input as a vector. The network parts described share the weights during training, so that the paired data pass through the exact same network architecture. Then, both sub-networks feed the vectors into the similarity checker with the contrastive loss [51, 52] to measure the similarity scores between image pairs and coordinate pairs. Finally, the results are concatenated as the input for another full connected network to finally obtain the similarity measurement to complete identification assignment. Fig. 7The structure of the proposed tracking network. a Dual-Siamese-Network: Input information: [1] images as input 1; [2] spatial information as input 2; Siamese network to process ears: the two networks in each Siamese-network are identical, with shared weight matrices at each layer; Similarity calculating network: using outputs of Siamese network to calculate the similarity of ears in adjacent frames to assign identifications; b Sub-siamese network for image processing; c Sub-siamese network for coordinate processing ## Generation of the final tracking results Generally, the final tracking result is a combination of the results in occlusion frames and non-occlusion frames. The key to the combination is to link the tracking trajectories in both kinds of frames. Here, we used a frame-classifying operation to make every occlusion fragment incorporate one previous frame and one subsequent frame (these frames were non-occlusion frames). And then, these frames were employed for tracking using both strategies (the one for occlusion and the one for non-occlusion frames) and were assigned with the same identification to link the trajectories in two kinds of fragments. ## Implementation details for ear detection network The improved EDN was trained and tested on Dataset A. The clip for training and testing of ODN with 1080P original resolution and 60FPS frame rate, was used to take one image every other 5 frames. In total, 10314 frames were extracted randomly, which means that 20,628 images of mouse ears were used as the training input. And 2166 frames (4332 images) were used for testing. Due to the application of transfer learning strategy, the CSPdarknet [48] was used as the default Backbone model of EDN. In the training procedure, the resolution of the input video was 1280 × 720 and the number of epochs was 50, the batch size was set to 8 and the learning rate was set to 0.01. The main hyper-parameters of ear detection network are shown in Table 8.Table 8The main hyper-parameters of the ear detection networkHyperparametersThe optimal settingInput resolution1280 × 720Train epoch50Batch size8OptimizerSGDInitial learning rate0.01Final OneCycleLR learning rate0.2SGD momentum0.937 There are 4 different pre-trained CSPdarknet models on MS-COCO [53] dataset ranging from the smallest one with 7.5 million parameters and 140 layers to the largest with 89 million parameters and 284 layers. In Table 9, which shows the ear detection performance of these 4 pre-trained models, we see that the “Large” pre-trained model achieves the highest mAP at 0.5:0.9 (mean average precision from 0.5 to 0.9 interaction over union), thus, in this study, we used the “Large” pretrained model. Table 9Performance comparison of different volumesModel volumeLayersParametermAP at 0.5 %mAP at 0.5:0.9 %Small2807.9 million97.2562.87Medium37823.5 million96.6363.24Large47651.7 million97.0764.32Extreme57496.5 million94.7350.56 ## Implementation details for training and testing with dual-Siamese network Since Dataset A is currently the only accessible dataset to do automatic labeling, this dual-Siamese-network was trained with the ear images and spatial information extracted from Dataset A. To be compatible with a lower frame rate video (the video in Dataset A has a high frame rate), the ear data used to train the target detection network was used here (i.e., one frame was taken every 3 frames, so the actual frame rate in training was only 20 frames per second). The images and spatial information in adjacent frames were automatically marked as positive samples, and the two with a time interval of more than one minute were automatically marked as negative samples. Obviously, the number of positive samples constructed in this way is limited, at most 24958. Negative samples can greatly exceed this amount. Here, we randomly selected 20,000 positive samples and 20,000 negative samples from it as the training dataset for the dual-Siamese network. Clips in Dataset B for comparison of tracking system performance were used as input with original parameters and resolutions. Details are shown in Table 9. Special attention should be paid to the video B6. The total number of frames in this video is 76191 by preprocessing of video-to-frames. But there was a fragment of human interference in the video. Therefore, the interfered with fragment needed to be removed (with 2108 frames in total) for the EDDSN-MRT to work properly. This left two non-interfered fragments (with 37,483 and 36,603 frames) which were processed using our methods. ## Implementation details for rodent experiment validation on intermittent fasting intervention Intermittent fasting (IF) is an increasingly popular dietary approach used for weight management and maintenance of overall health [54]. Tracking individual subject’s trajectories provides a noninvasive approach for the assessment of locomotion changes in animal models with different interventions. We collected data from 32 mice (n18m-IF = 8, n18m-AL = 8, n3m-IF = 8, n3m-AL = 8; Table 6) in Dataset C with our tracking system and subjected them to distributions of temporal features (e.g., velocity) analyses. Clips in Dataset C were used with original parameters and resolutions. By only evaluating spontaneous movement without any induced conditions, we demonstrated the usability and unbiased character of our framework for individual and social behavior monitoring in animal models. By applying the tracking system in this experiment, differences in group average and individual velocities and location distribution between the IF and AL groups can be observed. ## Ear detection methods To show the effectiveness of the proposed ear detection network, we compared it with several object detection methods as follows:YOLOv3 YOLOv3 is the 3rd version of YOLO series [48]. It employs a multi-scale schema, predicting bounding boxes at different scales. This allows Yolov3 to be more effective for detecting smaller targets when compared to the previous version YOLOs. It uses dimension clusters as anchor boxes in order to predict bounding boxes around the desired objects in given images. Logistic regression is used to predict the object score for a given bounding box. Here, it was trained with Adam optimizer with a learning rate of 0.001, the number of epochs set to 50, batch size set to 8, resolution at 1280 × 736 (YOLOv3 network only accepts resolutions whose value is an integer multiple of 32), and momentum at 0.9.2.YOLOv5 The YOLOv5 model is a detector consisting of a cross-stage partial network (CSPNet, as shown as Fig. 5b) [26] backbone, and a “Head” model with Path Aggregation Network (PANet) for instance segmentation. The Backbone network combined with a Spatial Pyramid Pooling (SPP) network [56] that was used to resist object deformation. The model was trained with SGD optimizer with a learning rate of 0.01, epoch set to 50, batchsize set to 8, resolution at 1280 × 720, and momentum at 0.937.3.EfficientDet The EfficientDet is an object detection framework built by the Google Brain team [27]. It achieved state-of-the-art accuracy on the popular MS-COCO dataset [53]. It includes pre-trained models classed from D0 to D7, which each have different numbers of parameters (D0 with the fewest and D7 with the highest). In the application purpose considered (for video frame processing, there is the requirement of execution speed), the EfficientDet D1 was selected. It was trained with SGS optimizer with a learning rate of 0.00005, epoch set to 50, batchsize set to 8, and momentum set to 0.9. ## Animal tracking methods To show the performance of the proposed EDDSN-MRT, we compared it with several existing state-of-the-art animal tracking methods as follows:Toxtrac Toxtrac [25] is an automated open-source executable software for image-based tracking that can simultaneously handle several subjects for monitoring in laboratory environments. It can be used for high-speed tracking of insects, fish, rodents or other species to provide useful locomotor information in animal behavior experiments. It was implemented with the threshold set to 90, minimum Object size set to 2000, maximum Object size set to 40,000, and maximum Distance/Frame set to 100. The numbers of individuals corresponded to the number of mice in the video.2.idtracker.ai Idtracker.ai [24] is an image-based multi-animal tracking system that uses convolutional neural networks to identify each of the individuals in the video. It uses offline training strategy. In the videos with a higher density of individuals, idtracker.ai extracts frames of the single individuals to train an image classification network to identify individuals. It was implemented with the area set as [2000,4000], and intensity was set as 80. The number of blobs was set equal to the number of individuals featured in each video. The range was set equal to the number of frames of each video. ## Metrics for ear detection As the methods for many conventional object detection networks, the mean average precision– mAP at 0.5 and mAP at 0.5:0.9 are introduced as evaluation metrics to quantitatively measure the detecting performance. These evaluation metrics are based on the Intersection over Union (IOU) of the ground truth and detected bounding boxes.\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$IoU = \frac{Area of overlap between bounding boxes}{{Area of union between bounding boxes}}$$\end{document}IoU=AreaofoverlapbetweenboundingboxesAreaofunionbetweenboundingboxes We set the threshold to determine whether the object is a true positive. mAP at 0.5 means when IoU is set to 0.5, the average precision of all categories is calculated independently and then averaged by the number of categories. In addition, mAP at 0.5:0.9 illustrates the average mAP over different IoU thresholds (from 0.5 to 0.9, in steps of 0.1). ## Metrics for multiple rodent tracking We used the widely accepted metric multi-object tracker accuracy (MOTA) proposed in the 2016 MOT Challenge [61]. To evaluate the performance of trackers, we used the py-motmetrics library. The MOTA tracking performance measure used in this study is the most commonly used metric to benchmark MOT solutions (Eq. 3).3\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$MOTA = 1 - \frac{{\mathop \sum \nolimits_{t} FN_{t} + FP_{t} + IDSW_{t} }}{{\mathop \sum \nolimits_{t} GT_{t} }}$$\end{document}MOTA=1-∑tFNt+FPt+IDSWt∑tGTtwhere false negative (FN), false positive (FP) and identity switch (IDSW) are the three types of errors that occur. False negatives are defined as an object that is not tracked, false positives are defined as tracked objects which should not be tracked, and identity switches describe two objects that should be tracked but they swap identities. The GT indicates the absolute number of individual identities. The direct output of the tracker is a series of IDs, which are mapped to our manually annotated tracks. The result of this implementation is a large number of ‘‘tracklets’’ (partial tracks), subsets of which belong to individual identities. This paper also introduces the metric ICR (ID Correct Rate). The ICR means the number of images correctly identified over the total number of individual images validated [24, 25] (Eq. 4).4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ICR = 1 - \frac{{\mathop \sum \nolimits_{t} Miss_{t} + Switch_{t} + Drift_{t} }}{{\mathop \sum \nolimits_{t} GT_{t} }}$$\end{document}ICR=1-∑tMisst+Switcht+Driftt∑tGTtwhere the missing identities (Miss), the switched identities (Switch) and the drifted identities (Drift) are the three types of errors that occur. Via the mapping between output and manually annotated tracks, it can be identified when the tracker is not able to detect an object (missing identities), when the tracker detects an object with the wrong position (drifting identities), or when the identities (two or more) tracks are switched. It must be emphasized that these methods are designed based on a constant number of experimental subjects. this design strategy would prevent the tracker from providing more false positive trajectories than the real number of experimental individuals.. ## Statistical analysis For the proportion indicators such as ICR, MOTA and mAP, we performed the "N-1" Chi-squared test to assess for significant effects. To determine whether there were significant differences between two variables, we first performed the Shapiro–Wilk test and Levene’s test to assess for normality and homogeneity of variance, respectively. Following, for normally distributed variables, we performed Student’s T test, and for non-normal variables we performed the Wilcoxon Rank Sum Test. 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--- title: Neuroprotection of NRF2 against Ferroptosis after Traumatic Brain Injury in Mice authors: - Hao Cheng - Pengfei Wang - Ning Wang - Wenwen Dong - Ziyuan Chen - Mingzhe Wu - Ziwei Wang - Ziqi Yu - Dawei Guan - Linlin Wang - Rui Zhao journal: Antioxidants year: 2023 pmcid: PMC10044792 doi: 10.3390/antiox12030731 license: CC BY 4.0 --- # Neuroprotection of NRF2 against Ferroptosis after Traumatic Brain Injury in Mice ## Abstract Ferroptosis and iron-related redox imbalance aggravate traumatic brain injury (TBI) outcomes. NRF2 is the predominant transcription factor regulating oxidative stress and neuroinflammation in TBI, but its role in iron-induced post-TBI damage is unclear. We investigated ferroptotic neuronal damage in the injured cortex and observed neurological deficits post-TBI. These were ameliorated by the iron chelator deferoxamine (DFO) in wild-type mice. In Nrf2-knockout (Nrf2−/−) mice, more sever ferroptosis and neurological deficits were detected. Dimethyl fumarate (DMF)-mediated NRF2 activation alleviated neural dysfunction in TBI mice, partly due to TBI-induced ferroptosis mitigation. Additionally, FTH-FTL and FSP1 protein levels, associated with iron metabolism and the ferroptotic redox balance, were highly NRF2-dependent post-TBI. Thus, NRF2 is neuroprotective against TBI-induced ferroptosis through both the xCT-GPX4- and FTH-FTL-determined free iron level and the FSP1-regulated redox status. This yields insights into the neuroprotective role of NRF2 in TBI-induced neuronal damage and its potential use in TBI treatment. ## 1. Introduction Traumatic brain injury (TBI) is caused by external mechanical forces and has a high rate of disability and mortality. More than 10 million people worldwide suffer from TBI every year [1,2]. Since many factors affect the consequences of brain injury, TBI pathogenesis requires full elucidation. Many types of cell death are involved in neuronal damage after TBI, including necrosis, apoptosis, pyroptosis, and necroptosis [3,4,5,6]. It has been proven that TBI-induced neuronal necrosis and parenchymal hemorrhage release a large amount of free iron into the peripheral space, which further leads to ferroptosis due to excessive oxidative damage to neurons and glial cells [7,8]. Increased levels of antioxidant factors exert protective roles against ferroptosis [9,10], further indicating that ferroptosis is closely related to the disturbance of cellular redox homeostasis. Recent studies, including our own, have indicated that ferroptosis plays an important role in neuronal death and neural dysfunction after TBI [11,12,13], intracerebral hemorrhage [14], and spinal cord injury [15]. Iron is important in the biological process of ferroptosis. Excess free iron induces lipid peroxidation by promoting free radical production and impairing biomembrane function, leading to GSH depletion, increased COX2 levels, and toxic lipid peroxidation products, such as 4-HNE and MDA [16,17,18,19], which in turn lead to tissue edema, cytotoxic oxidative damage, and even cell death in damaged sections [20,21,22,23]. Ferroptosis, a newly recognized regulatory form of cell death associated with lipid peroxidation and iron metabolism disorders, is attributed to lethal lipid reactive oxygen species (ROS) regulated by glutathione peroxidase 4 (GPX4), and is inhibited by iron chelators and lipophilic antioxidants [16,24,25,26]. Under physical conditions, iron is stably stored in cells in the form of ferritin (consisting of FTH, which acts as a ferroreductase, and FTL, which stores large amounts of iron) [27]. Ferritin is the center of iron metabolism. It has been reported that the loss of FTH results in oxidative stress and impairs cortical iron homeostasis in mice [28,29]. Mutations in FTL contribute to brain iron dysregulation, early morphological signs of neurodegeneration, and motor coordination deficits [30]. Therefore, ferritin plays an important role in the stability of iron levels and in neuroprotection. Nuclear factor erythroid-derived 2-related factor 2 (NRF2, NFE2L2) is a dominant member of the CNC-bZIP family and a key transcription factor that regulates the balance of oxidative stress in the body [31]. Many studies have shown that NRF2 exerts neuroprotective effects by antagonizing ferroptosis in rodents with spinal cord injury, subarachnoid hemorrhage [32,33], and TBI [34]. Nrf2 deficiency in mice leads to the aggravation of redox imbalance [34,35], while NRF2 agonists significantly ameliorate TBI-induced mitochondrial dysfunction, oxidative damage, and inflammatory response [36,37,38]. Recent studies have provided evidence that NRF2 inhibits intracellular iron accumulation [39], lipid peroxidation, and ferroptosis in neural systems in both in vitro [40] and in vivo models [33,41]. Although previous studies have linked NRF2 with iron-related pathological progression, the exact role of NRF2 after TBI remains unclear. To illustrate the potential role of NRF2 in iron-related damage after TBI, wild-type (WT), Nrf2-knockout (Nrf2−/−) and dimethyl fumarate (DMF) [42]-treated mice were used to establish a controlled cortical impact (CCI) model and, subsequently, to detect iron metabolism and neuronal ferroptosis after TBI. Our study aimed to provide new evidence regarding the neuroprotective mechanism of NRF2. We showed that NRF2 is neuroprotective against TBI-induced ferroptosis through both the FTH-FTL-determined free iron level and the xCT-GPX4- and FSP1-determined redox statuses. Thus, our results shed new light on strategies for the treatment of TBI by targeting NRF2. ## 2.1. Animals Adult male C57BL/6J mice were obtained from China Medical University, and Nrf2-knockout (Nrf2−/−) mice (20–25 g) were gifted by Dr. Jingbo Pi (School of Public Health, China Medical University). Mice were housed in a pathogen-free room, which was maintained at a controlled temperature (23 ± 1 °C) on a 12 h light/dark cycle, with water and feed available freely. Genotyping was performed by polymerase chain reaction (PCR) using genomic DNA isolated from tail clips, as previously described [43] (Figure S1A,B). All procedures were performed in accordance with the National Guidelines for the Care and Use of Laboratory Animals. Animal experiments were reviewed and approved by the China Medical University Animal Care and Use Committee (AUP: KT2020135). ## 2.2. Model Handling and Sample Collection We used 72 wild-type (WT) and 54 Nrf2−/− mice (8–12 weeks old) in this study. The mice were randomly divided into the following groups: WT ($$n = 18$$), WT+Vehicle (saline or methylcellulose) ($$n = 36$$), WT+DFO (deferoxamine dissolved in saline) ($$n = 18$$), WT+DMF (dimethyl fumarate dissolved in methylcellulose) ($$n = 18$$), Nrf2−/− ($$n = 18$$), Nrf2−/−+Vehicle ($$n = 18$$), and Nrf2−/−+DFO ($$n = 18$$). In addition, each group of mice was subdivided into sham and TBI (1, 3 days post-injury [dpi]) subgroups ($$n = 6$$/subgroup). In the TBI group, mice were placed in the prone position on the stereotaxic apparatus after being anesthetized with $4\%$ isoflurane in oxygen. A craniotomy was induced midway between the bregma and λ lateral to the left sagittal suture, and a vertical impact on the cortex was made using a controlled cortex impact (CCI) apparatus (PinPoint™ PCI3000, Hatteras Instruments, Grantsboro, NC, USA) with the following impact parameters: diameter impactor, 3 mm; depth, 1 mm; velocity, 1.5 m/s; and residence time, 50 ms. In addition, mice in the sham group underwent the same operation, but without cortical impact. The administration of DFO [44] (50 mg/kg/day, intraperitoneal, MedChemExpress, Shanghai, China) and DMF [45] (50 mg/kg/day, intragastric, MedChemExpress, Shanghai, China) was based on a previous study. A schematic flow diagram of the grouping and treatment is shown in Figure S1C. The mice were then anesthetized with pentobarbital sodium and sacrificed at the indicated times. Ipsilateral cortex tissues were collected after heart perfusion with cold phosphate-buffered saline and stored at −80 °C for protein and gene analysis. For morphological analysis, mice were perfused with $4\%$ paraformaldehyde. Staining images were captured using a Zeiss Axio Scan. Z1 confocal microscope system (Zeiss, Jena, Germany). ## 2.3. Immunofluorescence, TUNEL, and Fluoro-Jade C Staining Immunofluorescence (IF) staining was performed as described previously [46]. To detect damage to cortical neurons, co-staining with NeuN and TUNEL was performed using a TUNEL BrightGreen Apoptosis Detection Kit (A111-01; Vazymem, Nanjing, China). The primary and secondary antibodies which we used are listed in Supplementary Table S1. Fluoro-Jade C (FJC) staining was conducted to detect neuronal degeneration in the injured cortical tissues according to the manufacturer’s instructions (#AG310, Millipore, Burlington, MA, USA). ## 2.4. Nissl Staining and Perl’s Staining Nissl staining was performed according to the manufacturer’s instructions (Beyotime Biotechnology, Shanghai, China). Perl’s staining was conducted to detect iron in neurons according to the manufacturer’s instructions (G1422; Solarbio, Beijing, China), and the staining signal was developed using 3,3-diaminobenzidine. Stained images were acquired using a Zeiss Axio Scan. Z1 confocal microscope system (Zeiss, Jena, Germany). ## 2.5. Iron Content Determination The iron level in the ipsilateral cortex was detected according to the instructions for the reagent (ab83366, Abcam, Cambridge, UK), protein concentration was determined using a BCA assay kit, and the amount of iron was normalized to the total protein level and expressed as iron level (nmol)/total protein level (mg). ## 2.6. Western Blotting Analysis Ipsilateral injured cortices was collected and lysed in RIPA buffer (Beyotime, Shanghai, China) with protease inhibitor (PMSF) (Beyotime, Shanghai, China), and protein concentration was determined using the BCA assay kit (Beyotime, Shanghai, China). Western blotting was performed as previously described [46]. The relative band intensity was quantified using NIH ImageJ software and normalized to β-ACTIN. The antibodies which we used are listed in Supplementary Table S1. ## 2.7. Glutathione Detection Glutathione (GSH) content in the ipsilateral injured cortex was detected using GSH assay kit (A006-1-1; NanJing JianCheng Bioengineering Institute, Nanjing, China) as previous described [29]. The protein concentration of the ipsilateral injured cortex was determined by a BCA assay kit (Beyotime, Shanghai, China), and GSH content was expressed as GSH level (μmol)/total protein (mg). ## 2.8. Transmission Electron Microscopy The mitochondrial ultrastructure of the neurons in the injured cortex was examined by transmission electron microscopy (TEM), as described previously [15]. Ultrathin sections of tissues were prepared and visualized using a transmission electron microscope (Philips CM120, Holland). ## 2.9. Quantitative Real-Time PCR The total RNA of the injured cortices was isolated with TRIzol reagent (279510, Thermo Fisher Scientific, CA, USA) and quantitatively determined using NanoDrop 2000C (Thermo Fisher Scientific, USA). Reverse transcription to cDNA was performed using the PrimeScript™ RT reagent kit (TRR047A, Takara Biotechnology, Japan), and RT-qPCR was performed using the SYBR® Premix Ex Taq™ II RT-PCR kit (RR820A, Takara Biotechnology, Japan) for quantity analysis of the mRNA. mRNA levels were normalized to β-Actin. The primer sequences which we used are listed in Supplementary Table S2. ## 2.10. Assessment of Neuronal Function Neurological severity scores (NSS) were used to assess the motion function of mice after TBI as previously described [47]. We monitored mice for the presence of mono- or hemiparesis, inability to walk on a 3-centimeter-wide beam, inability to walk straight, and loss of startle behavior. Higher scores indicated more severe damage to the mice. In addition, the pole test was used to further assess the motion function of the mice after TBI. The time to climb the round rod was recorded; the longer the time, the more serious the injury to the mice [48]. ## 2.11. Statistical Analysis Data are presented as the mean ± SD. The number of positive cells was counted and analyzed independently by researchers who were not involved in the trials using ImageJ software (version 6.0; National Institutes of Health). Data between groups were compared using two-way analysis of variance followed by Tukey’s post hoc multiple comparison test. Data were compared between two groups using Student’s t-test. Statistical analyses were performed using GraphPad Prism 8.0 (GraphPad Software, La Jolla, CA, USA), with $p \leq 0.05$ considered statistically significant. ## 3.1. Ferroptosis Is Related to Neuronal Damage and Dysfunction after TBI TBI results in diffused necrosis and vascular rupture, which contribute to iron overload in the injured tissues. To confirm iron deposition in neurons after TBI in our CCI model, we performed co-staining with Nissl and iron and detected robust iron deposition in the ipsilateral cortical neurons of mice at 1 and 3 days post-injury (dpi) (Figure 1A). To explore the neuronal toxicity of the accumulated iron, the iron chelator deferoxamine (DFO) was used to scavenge the free iron. The deposition of iron in the injured cortex declined sharply after treatment (Figure 1B). This was accompanied by a reduction in TUNEL-positive neurons in the peripheral lesion area (Figure 1C,D). These results demonstrated that TBI-induced neuronal damage is closely associated with the iron overload. To confirm the occurrence of ferroptosis after TBI, we performed double-immunostaining for ferroptotic markers and NeuN. We found that 4-HNE and COX2 levels were sharply increased in NeuN (+) cells in the periphery of lesion sites after TBI, which was ameliorated by DFO treatment (Figure 2e and Figure S2a,b, lower panels). In addition, the expression of the ferroptosis-related genes Acsf2 and Ptgs2 was upregulated after TBI; this was suppressed by DFO treatment (Figure 1F). Furthermore, a neurological assessment was conducted to evaluate the influence of iron overload on neural function in the mice, as shown in Supplementary Figure S3a,b. DFO treatment alleviated both motor dysfunctions (Supplementary Figure S3a,b) after TBI in mice. With these results taken together, we confirmed that ferroptosis contributes to neuronal damage and neurological dysfunction following TBI. ## 3.2. Nrf2 Deficiency Aggravates Neurological Dysfunction and Neuronal Damage after TBI in Mice NRF2 has been shown to be a key neuroprotective factor against TBI in mice [49,50]. Robust expression of NRF2 occurs in neurons at 3 dpi in mice [51]. To confirm the neuroprotective roles of NRF2, WT and Nrf2−/− mice were used to examine neurological function after TBI. The neurological severity score (NSS) and the pole test were applied to evaluate the motor function of the mice after 3 dpi, and the results showed that the NSS of the Nrf2−/− mice was higher than that of the WT mice (Figure 2a), as was the average time for climbing rods (Figure 2b), suggesting that Nrf2 deletion aggravated TBI-induced motor dysfunction in mice. Subsequently, the spatial memory of TBI mice at 14 dpi was evaluated using the Barnes maze, and the movement traces of the mice on the platform were recorded (Figure 2c). The escape latency, travel distance, and number of errors were significantly different between Nrf2−/− and WT mice after TBI (Figure 2d), suggesting that Nrf2 deficiency aggravated neurological dysfunction and spatial memory impairment caused by TBI. Thereafter, fluoro-jade C (FJC) staining was used to reveal neuronal damage in the ipsilateral cortex, as shown in Figure 2c,d. Nrf2 knockout exacerbated TBI-induced neuronal degeneration after TBI. In addition, evaluation of the co-localization of TUNEL and NeuN in the injured cortex demonstrated an increase in TUNEL-NeuN double-positive cells after TBI (Figure 3a,b), which was aggravated by Nrf2-knockout, accounting for the aggravated neurological damage after TBI in Nrf2-knockout mice. Taken together, Nrf2 deletion aggravates neurological deficits and neuronal damage in TBI mice. ## 3.3. NRF2 Deletion Aggravates TBI-Induced Ferroptosis NRF2 regulates oxidative stress and neuroinflammation, and plays a neuroprotective role after TBI [50]. To determine whether NRF2 is associated with the pathogenesis of iron accumulation and ferroptosis after TBI, we first detected iron deposition and ferroptosis in Nrf2−/− mice at 3 dpi. We examined iron levels in the ipsilateral injured cortex. Compared to WT mice, Nrf2-deficient mice showed higher levels of iron at 3 dpi (Figure 3a). Furthermore, increased levels of 4-HNEand COX2 (Figure 3b,c) and decreased levels of GSH (Figure 3d) were observed in the injured cortices of Nrf2−/− mice as compared with WT mice. The mRNA levels of ferroptosis-related genes, such as Acsf2 and Ptgs2, were further increased by TBI in Nrf2-knockout mice (Figure 3e). In addition, transmission electron microscopy (TEM) was used to explore the ultrastructure of the injured neurons at 3 dpi. As shown in Figure 3f,g, exacerbated shrinkage, cristae disappearance, and high electron density of mitochondria in neurons were observed after TBI, and these effects were aggravated in Nrf2−/− mice. These results suggested that Nrf2-deletion enhanced TBI-induced iron accumulation and exacerbated neuronal lipid peroxidation and ferroptosis after TBI. ## 3.4. DMF Mitigates TBI-Induced Ferroptosis and Neurological Deficits To further clarify the protective role of NRF2 in TBI-induced ferroptosis, we treated mice with the NRF2 agonist dimethyl fumarate (DMF). DMF treatment increased the levels of NRF2 and the expression of Nrf2; it also regulated downstream genes, such as Ho-1, Nqo1, Gclc, and Gclm (Supplementary Figure S4a,c). Moreover, oxidative products or protein levels of 4-HNE and COX2 in the injured cortices were decreased after the use of DMF (Figure 4a,b). In line with this, the immunoreactivity of 4-HNEand COX2 in neurons was also downregulated (Figure 4c and Figure S4d). In contrast, the level of GSH increased at 3 dpi after DMF treatment (Figure 4d). Consequently, a reduction in damaged neurons (TUNEL-positive) was observed after treatment with DMF (Supplementary Figure S4e,f). The mRNA levels of Acsf2 and Ptgs2 were decreased in the injured cortices after DMF administration (Figure 4e). Our data indicate that DMF alleviated TBI-induced lipid peroxidation and ferroptosis after TBI. In addition, we evaluated the effects of DMF on neurological function after TBI. As shown in Figure 4f,g, DMF improved the NSS and pole-climbing ability of TBI mice. Our results demonstrated that NRF2 activation improved neurological function, at least in part, by alleviating TBI-induced ferroptosis in mice. ## 3.5. NRF2 Regulates Ferroptosis through FTH-FTL, xCT-GPX4, and FSP1 after TBI To explore the possible molecular mechanism by which NRF2 exerts effects in TBI-induced ferroptosis, we examined the impact of some candidate factors on the pathways of iron metabolism at 3 dpi. We found that NRF2 deficiency resulted in decreased FTH and FTL, whereas both the protein (Figure 5a,b) and mRNA levels (Figure 5c,d) were increased by DMF. Since xCT and GPX4 are recognized as important factors for the synthesis and function of glutathione [52], the protein and mRNA levels of xCT and GPX4 were further investigated. As shown in Figure 4d and Figure 5a,b,e,f, a decrease in xCT, GPX4, and GSH caused by TBI was reversed after the administration of DMF, whereas the levels of xCT and GPX4 were decreased in Nrf2-knockout mice, particularly after TBI, demonstrating that xCT and GPX4 levels are highly dependent on NRF2 (Figure 4d and Figure 5a,b,e,f). Because ferroptosis suppressor protein 1 (FSP1) has been recognized as an important ferroptotic inhibitor [53,54], we tested the expression of FSP1 at the protein and mRNA levels. As shown in Figure 5a,b,g, FSP1 was positively regulated by NRF2 (Figure 5a,b,g). Thus, NRF2 affects iron metabolism and ferroptosis after TBI by regulating FTH-FTL, xCT-GPX4, and FSP1 levels. ## 4. Discussion Iron-related redox imbalances and ferroptosis are important factors that aggravate TBI outcomes. NRF2 has been demonstrated to be the predominant transcription factor regulating oxidative stress and inflammation after TBI [49,50]. However, the roles and mechanisms of NRF2 in iron-dependent regulated cell death after TBI have been unclear. In this study, we demonstrated a novel neuroprotective effect of NRF2 involving inhibition of iron overload and antagonization of ferroptosis after TBI, which was partially mediated by FTH-FTL and xCT-GPX4 FSP1 (Figure 6). Ferroptosis is a newly recognized form of regulated cell death that is mainly characterized by the availability of redox-active iron and the loss of lipid peroxidative repair capacity [55]. We and others have found that ferroptosis contributes to neuronal loss and neurological dysfunction after TBI [11,13]. TBI leads to the accumulation of free iron in the interstitial space due to hemorrhage and cellular necrosis. Excess iron has been proven to be involved in the production of ROS, lipid peroxidation, inflammation, and autophagy in TBI models [23,56]. Iron accumulation is associated with deficits in spatial learning, spatial memory, and long-term prognosis, which are ameliorated by the use of ferroptotic inhibitors in both TBI [57] and craniocerebral injury [58]. In our present study, TBI-induced iron accumulation and lipid peroxidation led to neuronal impairment and abnormal neurological function. These results indicate that iron overload and ferroptosis are closely associated with TBI-induced neurological dysfunction. NRF2 is the most important nuclear transcription factor involved in redox balance regulation. Loss of NRF2 causes sensitivity to oxidative damage [59]. Many studies have shown that NRF2 has a wide range of protective effects against cortical lesions, edema, and motor and neurological damage after TBI [60,61], which are related to anti-oxidative, anti-inflammatory, anti-apoptotic, and ubiquitination regulatory activities [31,49,62]. Considering that lipid peroxidation is the main characteristic of ferroptosis, NRF2 would be involved in the pathogenesis of TBI-induced neural damage. Recent studies have provided evidence that NRF2 may participate in the regulation of ferroptosis [49]. In the present study, we found that NRF2 is not only involved in iron metabolism, but also in the antioxidant process of ferroptosis. The present study provides solid evidence for a novel neuroprotective role of NRF2. Excess iron reacts with hydrogen peroxide to generate vast numbers of hydroxyl radicals, leading to tissue impairment [63]. Ferritin, composed of FTH and FTL, which that can accommodate up to 4500 iron atoms [64], is a ubiquitous intracellular spherical iron storage protein that plays a key role as a dynamic iron buffer in organisms. By isolating and stabilizing excess free iron, ferritin exerts an antioxidative effect and reduces the damage caused by iron overload [65,66]. Many studies have shown that NRF2 plays a significant role in iron storage and iron metabolism [50,67], and this is supported by the evidence that NRF2 acts as a transcription factor to regulate Fth and Ftl in rat livers [68]. Moreover, the presence of antioxidant response elements (AREs) has been confirmed in the promoter regions of mouse Ftl and Fth [69,70]. In this study, the loss of Nrf2 resulted in a decrease in FTH and FTL levels after TBI, followed by an increase in free iron levels, which might have contributed to neuronal dysfunction. In this work, we use deferoxamine (DFO) as an iron chelator after TBI, although there are queries regarding whether DFO can penetrate BBB, alleviate TBI-induced ferroptosis, and remove excessive iron, as our previous and present studies have shown in WT mice [13]. Thus, we believe that at least post-TBI BBB damage leads to easier entry for DFO, which may be beneficial to its anti-ferroptotic and neuroprotective effects. Recent studies have provided evidence that FSP1 plays an antioxidant role parallel to the xCT-GPX4 pathway, and that it exerts an inhibitory effect on lipid peroxidation and ferroptosis [71,72]. The Xc-antioxidant regulatory system (cystine–glutamate antiporter system) plays a significant upstream role in the ferroptosis signaling cascade [73,74,75]. It consists of light chain xCT (SLC7A11) and heavy chain 4F2 (SLC3A2), which transport extracellular cysteine into cell plasma for glutathione biosynthesis [73,74,75]. Previous studies have found that NRF2 upregulates xCT (SLC7A11) [76,77] and exerts neuroprotective effects by inhibiting ferroptosis [76,78]. GPX4, a downstream factor of xCT, plays an irreplaceable role in ferroptosis pathogenesis. Loss of GPX4 is a crucial event in ferroptosis [79,80], and promotes cognitive impairment [81]. Considering that both GPX4 and GCLC/GCLM (key enzymes for synthesizing glutathione) are transcriptionally regulated by NRF2 [82,83], we confirmed that the expression of xCT and GPX4 was highly dependent on the activation of NRF2 by using Nrf2-knockout and DMF-treated mice. This was consistent with a previous study showing that activation of NRF2 promoted functional recovery by increasing GPX4 levels after spinal cord injury [33]. Additionally, FSP1, a newly discovered ferroptotic antagonist, plays an antioxidant and anti-ferroptosis role by reducing ubiquitin (CoQ) to antioxidative dihydroubiquitin (CoQH2) [71,72]. Herein, the expression of FSP1 was interrupted by Nrf2-knockout and upregulated by the administration of DMF, which was consistent with the previous identification of Fsp1 as a downstream gene of NRF2 [84]. Therefore, we affirmed that NRF2 exerts neuroprotective effects against TBI-induced ferroptosis by regulating not only the xCT-GPX4 pathway, but also the GPX4-independent FSP1. In the present study, we expanded the neuroprotective effects of NRF2 after TBI on the regulation of iron metabolism and ferroptosis. However, this study had some limitations. Although the protective effects and the potential mechanisms of NRF2 on TBI- induced neuronal ferroptosis were discussed in this study, there remains a need to explore the potential roles of NRF2 in iron metabolism and ferroptosis in glial cells. 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--- title: 'Oxidative Stress and Cardiovascular Risk Factors: The Coronary Artery Risk Development in Young Adults (CARDIA) Study' authors: - Amir S. Heravi - Di Zhao - Erin D. Michos - Henrique Doria De Vasconcellos - Bharath Ambale-Venkatesh - Donald Lloyd-Jones - Pamela J. Schreiner - Jared P. Reis - James M. Shikany - Cora E. Lewis - Chiadi E. Ndumele - Eliseo Guallar - Pamela Ouyang - Ron C. Hoogeveen - Joao A. C. Lima - Wendy S. Post - Dhananjay Vaidya journal: Antioxidants year: 2023 pmcid: PMC10044794 doi: 10.3390/antiox12030555 license: CC BY 4.0 --- # Oxidative Stress and Cardiovascular Risk Factors: The Coronary Artery Risk Development in Young Adults (CARDIA) Study ## Abstract Introduction—Oxidative stress is linked to cardiovascular diseases (CVD) and is suggested to vary by sex. However, few population-level studies have explored these associations and the majority comprise populations with advanced CVD. We assessed urinary isoprostane concentrations, a standard measure of oxidative stress, in a relatively young and healthy cohort, hypothesizing that higher oxidative stress is associated with an adverse cardiometabolic profile and female sex. Methods—Oxidative stress was measured in 475 women and 266 men, aged 48–55 years, from the Coronary Artery Risk Development in Young Adults (CARDIA) study using urinary 8-isoprostane (IsoP) and 2,3-dinor-8-isoprostane (IsoP-M). Multivariable-adjusted regression was used to evaluate cross-sectional associations. As secondary analysis, previously measured plasma F2-isoprostanes (plasma IsoP) from another CARDIA subset was similarly analyzed. Results—Mean (SD) ages for men and women were 52.1(2.3) and 52.2(2.2) years, respectively ($$p \leq 0.46$$), and $39\%$ of the participants self-identified as Black (vs. White). Before adjustments, female sex was associated with higher median urinary IsoP (880 vs. 704 ng/g creatinine in men; $p \leq 0.01$) and IsoP m (1675 vs. 1284 ng/g creatinine in men; $p \leq 0.01$). Higher body mass index (BMI), high-density cholesterol (HDL-C), and triglycerides, current smoking, and less physical activity were associated with higher oxidative stress. Diabetes was not associated with urinary IsoP but was associated with lower IsoP m and plasma IsoP. Higher serum creatinine showed diverging associations with higher plasma and lower urinary isoprostane concentrations. Conclusions—Different isoprostane entities exhibit varying association patterns with CVD risk factors, and therefore are complementary, rather than interchangeable, in assessment of oxidative stress. Still, consistently higher isoprostanes among women, smokers, less active persons, and those with higher BMI and plasma triglycerides could reflect higher oxidative stress among these groups. While urinary isoprostanes are indexed to urinary creatinine due to variations in concentration, caution should be exercised when comparing groups with differing serum creatinine. ## 1. Introduction Oxidative stress—defined as perturbed balance with excess reactive oxygen species (ROS) vis-a-vis the body’s antioxidant defense system [1]—has been linked to the development of many cardiovascular diseases (CVD), including oxidation of lipoproteins [2], atherosclerosis [3] and heart failure [4]. Connections between oxidative stress and CVD risk factors such as diabetes [5], tobacco use [6], and endothelial dysfunction [7] have also been suggested. Furthermore, some suggest sex disparities in CVD, especially after menopause, may be partially attributable to sex differences in oxidative stress as the result of loss of potential protective effects of sex hormones on modulation of oxidative stress [8,9,10]. On the other hand, higher oxidative stress in premenopausal women compared with men is also reported [11]. Thus, the exact role of oxidative stress in the pathophysiology of CVD and whether true sex differences in oxidative stress exist remain equivocal [12,13]. Isoprostanes are prostaglandin-like byproducts of arachidonic acid peroxidation induced by ROS and are considered a standard marker for in vivo oxidative stress [14,15]. Isoprostanes are both mediators and indicators of oxidative stress and a measure of the redox status of the internal milieu across many human diseases [16]. Yet, few epidemiologic studies have characterized the relationship between CVD risk factors and isoprostanes. Moreover, the majority of existing studies were performed in populations with advanced CVD, rarely offered parallel assessments of different isoprostane metabolites or compared associations in plasma vs. urinary isoprostanes [17]. We measured urinary concentrations of 8-isoprostane (urinary IsoP) and its metabolite 2,3-dinor-8-isoprostane (urinary IsoP-M) in a subset of participants in a middle-aged and relatively healthy population-based cohort, and reanalyzed previously measured plasma F2-isoprostanes (plasma IsoP). We postulated that CVD risk factors, including behavioral risk factors such as smoking and physical activity, metabolic conditions such as diabetes, hypertension and adverse lipid profile and female sex would be independently associated with greater oxidative stress, as measured by higher urinary isoprostanes. ## 2.1. Study Population Coronary Artery Risk Development in Young Adults (CARDIA) is a community-based, multicenter, observational, longitudinal cohort study sponsored by the National Heart, Lung, and Blood Institute. CARDIA enrolled 5115 women and men between 18 and 30 years of age, free from CVD, in 1985-86 from 4 centers (Birmingham, AL; Chicago, IL; Minneapolis, MN; and Oakland, CA, USA) [18]. The institutional review boards of all participating study sites approve the study annually, and all participants provide written informed consent. The present study is a cross-sectional analysis of CARDIA participants for whom urinary isoprostane concentrations were measured at the year 25 (Y25) follow-up exam (2010–2011) as part of an ancillary study. Complete inclusion and exclusion criteria for the ancillary study, for which the primary goal was to assess changes in cardiac structure and function during the menopausal transition, are listed in Figure 1. For this analysis, the study population comprised 48- to 55-year-old CARDIA participants who satisfied criteria for availability of questionnaire data, cardiac imaging, and biospecimens in line with the objectives of the parent ancillary study. Participants with estimated glomerular filtration rate (eGFR) <60 mL/min/1.73 m2 using the Modification of Diet in Renal Disease (MDRD) equation [19] were excluded to avoid interpretability issues for urine-based assays in the setting of impaired renal function. Based on the above criteria, 475 women and 266 men were included (Figure 1). Plasma-free (non-esterified) IsoP concentrations from the year 15 (Y15) exam were available from another prior CARDIA ancillary study [20] which comprised 2999 participants at the Y15 exam with available computed tomography coronary artery calcium score. We used plasma IsoP measurements and their concurrent exposure variables for a secondary analysis, similar to that of the urinary markers. ## 2.2. Measurement of Exposure Variables Assessment of exposure variables was performed for each visit using standardized questionnaires, physical exam, and laboratory measures, as described previously [18]. Body mass index (BMI) was calculated as weight (kg) divided by height (m) squared. All participants were asked to fast for 12 h before each clinic visit. Blood pressure was measured using the Omron device (Omron Healthcare Inc., Lake Forest, IL, USA) at Y25. Plasma total cholesterol, high-density lipoprotein cholesterol (HDL-C) and triglyceride concentrations were measured using enzymatic methods. Physical activity was assessed with the CARDIA physical activity questionnaire accounting for the frequency and intensity of physical activity (the metabolic equivalent of task for each exercise category multiplied by the sum of months of infrequent participation plus 3 times months of frequent participation in the prior year) and reported as “exercise units” (EU) [21]. Diabetes was defined as the presence of any of measured fasting glucose ≥126 mg/dL or use of glucose-lowering medications (in any previous exams), 2 h post-load glucose ≥200 mg/dL (during a 75 g oral glucose tolerance test at years 10, 20, and 25) or an HbA1c ≥$6.5\%$ (at years 20 and 25). ## 2.3. Isoprostane Measurements Urinary IsoP and IsoP m were measured at the Atherosclerosis Clinical Research Laboratory at Baylor College of Medicine (Houston, TX, USA) using untimed urine samples typically collected midmorning after nocturnal fasting. Samples underwent sequential washing with solvent mixtures on mixed anion solid phase exchange columns for subsequent measurement of isoprostanes via gas chromatography-mass spectrometry (GC-MS) with negative chemical ionization [22]. Assay variance was assessed using two pools of quality control samples for each isoprostane entity and yielded intra-assay covariance of variance (CV) of 5–$7\%$ (Supplementary Table S1). To account for variations in urine concentration, each isoprostane measurement was indexed to urinary creatinine measured using the Jaffe rate method [23]. Plasma IsoP measurements were obtained from samples frozen promptly after collection, shipped overnight, and kept frozen at −80 °C until undergoing GC-MS within 1 year of collection. This process was reported to result in no ex vivo production or disintegration of isoprostanes. An internal standard was added to the samples to quantify assay isoprostane recovery and the analytical variation within 3 control pools was $10\%$ or less [20]. Plasma IsoP measures total F2-isoprostanes, a composite of isomers among which 8-isoprostane is the best studied [16]. ## 2.4. Statistical Analysis Population characteristics were reported as mean ± standard deviation, median [interquartile range] or number (percentage) as appropriate, and group differences were tested using Student’s t-, rank sum or χ2 tests, respectively. Creatinine indexed urinary IsoP and IsoP-M, plasma IsoP, and plasma triglycerides were log-transformed due to right-skewed distribution. Cross-sectional associations between urinary isoprostane concentrations (as dependent variables in separate models) and CVD risk factors (independent variable) were explored using multivariable-adjusted linear regression. The models were progressively adjusted (except if a variable was already included as variable of interest, e.g., when evaluating BMI, Model 1 adjusts for age, race, sex, college attainment and study field center, since BMI itself is automatically included as an independent variable): Model 1 adjusts for age, race, sex, college attainment, BMI and study field center. Model 2 includes covariates from model 1 plus serum creatinine. For each CVD risk factor except smoking-related variables, Model 3 introduces additional adjustments for smoking status (current, former, or never), and cumulative pack years. Model 4 (our primary model) includes covariates in Model 3 plus diabetes, use of medication for hypertension, systolic blood pressure, use of lipid-lowering medication, total cholesterol, HDL-C, fasting triglyceride concentration (log transformed) and physical activity. In linear regression of tobacco use variables, smoking status and cumulative pack years were not mutually adjusted (Model 3 was removed), as the two were highly correlated (data not shown). Since log-transformed isoprostane concentrations were used, the model results are presented as exponentiated beta-coefficients reflecting the association of each risk factor with isoprostane concentrations as a ratio (ratio > 1 indicates a positive and <1 indicates an inverse relationship). Two-sided p-values < 0.05 were considered statistically significant. All analyses were performed on Stata version 15.1 (StataCorp LP, College Station, TX, USA). ## 2.5. Sensitivity Analysis The analyses were repeated after replacing the diabetes variable with Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), fasting glucose, diabetes medication use, or hemoglobin A1c to assess robustness of the models. Separately, indexing by urinary creatinine was replaced by adjustment for this factor as an independent variable in each regression model [24]. In urinary isoprostane models, potential interactions between race and sex with each of the CVD risk factors of interest were explored and interpreted after application of Bonferroni correction. In plasma isoprostane models, analysis was repeated within the subgroup who also had Y25 urine isoprostanes. ## 3.1. Population Characteristics and Urinary Assays Characteristics at Y25 are shown in Table 1 (Y15 in Supplementary Table S2). Women and men were similar in age (mean 52.2 vs. 52.1 years, respectively), proportion identifying as Black ($38.5\%$ vs. $39.8\%$) and current smoking status ($13.4\%$ vs. $14.0\%$). Compared with men, women reported less physical activity and had lower systolic blood pressure, fasting triglyceride and glucose concentrations, but higher total and HDL cholesterol concentrations. Women were less likely to use medications for hypertension, less likely to have diabetes and had lower urinary and serum creatinine concentrations compared with men. Median concentrations of both urinary isoprostanes (indexed to urinary creatinine) were higher in women than men ($25\%$ higher for IsoP and $30\%$ higher for IsoP-M). ## 3.2. Urinary IsoP vs. Cardiovascular Risk Factors Mutually adjusted associations between urinary IsoP and CVD risk factors from our primary model are shown in Figure 2. Current smoking (vs. never) and higher cumulative pack years of smoking were associated with higher IsoP. Conversely, more physical activity, higher serum creatinine, and Black race (vs. White) were associated with lower IsoP concentrations. Progressively adjusted associations are reported in Table 2. In model 1, female sex (vs. male) and higher systolic blood pressure (borderline) were also associated with higher IsoP, and cholesterol lowering medications were associated with lower IsoP but their associations were attenuated after further adjustments. HDL-C exhibited borderline significant positive correlation with IsoP after adjustments. No associations between IsoP and age, BMI, diabetes, antihypertensive medication use, triglycerides or total cholesterol were noted regardless of adjustments. ## 3.3. Urinary IsoP m vs. Cardiovascular Risk Factors Mutually adjusted associations between urinary IsoP m and CVD risk factors are shown in Figure 3 (progressively adjusted models in Table 3). Similar to IsoP, IsoP m concentrations were higher in female compared with male participants in Model 1, but not in Model 4. However, adjustment for serum creatinine led to only partial attenuation for IsoP m and this association retained its statistical significance with further adjustment for traditional CVD risk factors. Similar to IsoP, current smoking, higher cumulative pack years of smoking and lower serum creatinine were associated with higher IsoP-M. On the other hand, IsoP m exhibited direct relationships with BMI and fasting plasma triglycerides which IsoP had not. IsoP m was not associated with cholesterol-lowering medication use, race, measures of hypertension (systolic pressure or medication use) or physical activity. In the mutually adjusted model, HDL-C was positively associated with IsoP-M- and total cholesterol showed a borderline-significant inverse association, but these associations were not present before adjustments. ## 3.4. Plasma Isoprostanes vs. Cardiovascular Risk Factors Mutually adjusted associations between plasma IsoP and CVD risk factors are shown in Figure 4 with progressively adjusted models reported in Table 4. Female sex, current smoking, higher BMI, HDL-C, and plasma triglycerides were associated with higher plasma IsoP; however, more physical activity, Black race, and diabetes were associated with lower isoprostane concentrations. Contrary to both urinary assays, plasma IsoP did not demonstrate an inverse association with serum creatinine, and was in fact associated with higher serum creatinine in the main model (model 4). Some CVD risk factors were associated with plasma IsoP only in specific models. A direct relationship between systolic blood pressure and plasma IsoP was attenuated after adjustments, while hypertension medication use became inversely associated with plasma IsoP in the main model. On the other hand, use of cholesterol-lowering medication was associated with higher plasma IsoP in the final model, but did not show an association in the less adjusted models. ## 3.5. Secondary and Sensitivity Analyses Findings were similar after adjusting for urinary creatinine as an independent variable instead of indexing (data not shown). Replacing the original variable for diabetes mellitus with HOMA-IR or fasting glucose showed a consistent pattern of lower isoprostanes with impaired glucose metabolism (Supplementary Table S3). Plasma IsoP Models reconstructed in the participant subgroup common to Y15 and Y25 showed no significant change in findings except for the anticipated increase in error estimates (Supplementary Table S4). In exploratory analysis, we found possible interaction by sex and race between urinary isoprostanes and BMI similar to that which has been reported previously [25,26], such that there was a trend towards an inverse association between BMI and IsoP in men, a trend towards a direct association in women and a trend towards stronger association between IsoP m and BMI in White compared to Black individuals. However, interactions were not statistically significant after Bonferroni correction (Supplementary Table S5 and S6). ## 4. Discussion Oxidative stress is implicated in the pathogenesis of CVD, and it is regarded as an attractive potential therapeutic and preventive target awaiting a promising breakthrough into clinical practice [27,28,29,30]. However, supporting evidence is largely derived from studies in animals or individuals with advanced CVD, and randomized controlled trials of antioxidant supplementation in at-risk individuals have not shown benefits [31]. Such discordance demands a closer look at oxidative stress in population-level studies. The principal goal of our study was to characterize the relationships between CVD risk factors and oxidative stress in a relatively healthy cohort with appropriate adjustment for potential confounders. We evaluated associations between in vivo oxidative stress as measured by urinary IsoP and its 2,3-dinor metabolite (IsoP-M) with CVD risk factors in a middle-aged subset of a diverse community-based cohort, with additional secondary analysis using previously collected plasma IsoP. ## 4.1. Female Sex We report higher creatinine-indexed urinary IsoP and IsoP m and higher plasma IsoP in women compared with men in our minimally adjusted models. However, we also found significant confounding by serum creatinine in the urinary assays. Additionally, adjustment for traditional CVD risk factors resulted in further attenuation, such that neither urinary isoprostane showed significant association with sex in the fully adjusted models. Our observations imply higher oxidative stress in women, as measured by isoprostanes; however, the magnitude of this difference may be exaggerated by confounders such as urinary creatinine in urinary assays. Future studies should measure isoprostanes in groups with similar physiology (such as women before and after menopause) and include measurements of sex hormones to more fully investigate potential associations between sex and systemic oxidative stress. ## 4.2. Smoking and Physical Activity The link between smoking and oxidative stress is well established and it is useful as a positive control for our analysis [16]. After minimal adjustment, all measured isoprostane concentrations were higher in current smokers compared with non-smokers. IsoP m showed >$50\%$ higher median among current smokers compared with non-smokers, suggesting this metabolite may be a more discriminative marker for oxidative changes in tobacco users. Interestingly, no clear difference in urine or blood isoprostane concentrations of former smokers (compared with never smokers) was found, implying the reversibility of elevated baseline oxidative stress secondary to tobacco use. We also found lower baseline oxidative stress in physically active participants, as reflected in lower IsoP in plasma and urine. This pattern was not seen for IsoP-M. As IsoP m is metabolically downstream of IsoP, this could indicate changes in the metabolism of isoprostanes. Studies measuring multiple isoprostane entities and physical activity are scarce, but a similar pattern with IsoP and another metabolite (downstream of IsoP-M) has been reported previously [32], suggesting low physical activity may be associated with IsoP buildup without change in downstream metabolites, albeit clinical significance of this observation remains unknown. ## 4.3. BMI Higher BMI was associated with higher urinary IsoP m and plasma IsoP, which is consistent with greater oxidative burden in obesity, as reported in previous studies [33], but not with urinary IsoP. The Framingham Heart Study previously reported an association between higher BMI and IsoP measured using enzyme-linked immunosorbent assay (ELISA), especially in women (significant interaction by sex) [25]; however, IsoP m or plasma IsoP were not measured. Insulin Resistance Atherosclerosis Study (IRAS) reported similar findings to our study, in which IsoP measured via GC-MS was not significantly associated with BMI, whereas IsoP m was [34]. While the findings in the IRAS study were not adjusted for other risk factors, our study found this association to be robust after adjustments. The IRAS study also reported an unexpected inverse relationship between baseline IsoP m and longitudinal weight gain [34]. The authors suggested that production of isoprostanes via lipid oxidation could be a protective compensatory mechanism in response to metabolic changes in obesity. They also found racial differences in associations between BMI and isoprostanes, such that BMI and isoprostane concentrations were correlated in White, but not Black participants, concluding this could signal weaker metabolic adaptability in the latter group [26]. We investigated these previously reported interactions between sex, race and BMI and found a trend toward a sex interaction between BMI and IsoP with non-statistically significant trends towards higher IsoP with higher BMI in women and lower IsoP with higher BMI in men (p-interaction 0.023 in the same direction as the Framingham study did not reach statistical significance after Bonferroni correction). While the interaction beta-coefficients for Black race and BMI were in the same directions reported in the IRAS study (direct association in Whites, no/borderline association in Blacks), this interaction was also not statistically significant (Supplementary Table S5). ## 4.4. Diabetes Laboratory research suggests that oxidative stress plays an important role in insulin resistance and pancreatic beta cell dysfunction [35,36]. The majority of the few epidemiologic studies assessing isoprostanes in diabetes report a direct cross-sectional relationship between them [16]. However, we found a surprising inverse association between diabetes and plasma IsoP (borderline association in the same direction was also present for IsoP m after adjustments). Replacing our original diabetes variable with other measures of glucose metabolism, such as HOMA-IR and fasting glucose, yielded similar results. Unexpected associations between isoprostanes and diabetes have been reported before. In one of the early studies, Feillet-Coudray measured higher urinary IsoP but lower plasma IsoP in patients with diabetes compared with controls via ELISA assays [37]. The authors concluded that urinary excretion of isoprostane may be higher in patients with diabetes, though our study shows no associations with urinary isoprostanes. In a more recent study, Ma et al. found no associations between two isoprostane entities measured by GC-MS and insulin resistance, and they suggested the lipid peroxidation process reflected by isoprostanes might be distinct from the oxidative reactions operative in development of insulin resistance [38]. The utility of isoprostanes in prediction of incident diabetes is also contested, and while some reports show higher future risk with high isoprostane concentrations [39], others describe an inverse association [40]. Given the observational and cross-sectional nature of our study, the mechanism underlying our unexpected findings remains unverified. Lower isoprostanes may result from factors that may affect isoprostane production rates, such as changes in lipid metabolism orarachidonic acid availability, changes to esterification (“freeing”) of isoprostanes or effects from certain hypoglycemic agents (we could not account for types of diabetes medications) among other possibilities [17,26]. Future studies that focus on developing our understanding of isoprostane biochemistry, especially in the setting of metabolic abnormalities, are warranted. ## 4.5. Creatinine and Kidney Function We report a novel, diverging pattern in blood and urinary isoprostane assays. In plasma, higher serum creatinine was associated with higher plasma IsoP after adjustments, which was consistent with higher oxidative stress. Conversely, higher serum creatinine was associated with lower urinary isoprostanes despite exclusion of those with abnormal kidney function, as determined by eGFR. Our study is one of the first reports of the latter paradigm in humans [41] or animals [42] and, to our knowledge, it is the only study to also include plasma isoprostane concentrations. Urinary isoprostanes are typically indexed to urinary creatinine concentrations to account for variability in urine concentration. Some argue that local production of IsoP in the kidney may limit the generalizability and interpretability of this urinary marker [17,43]. However, if local production was to dominate systemic oxidative stress, it would be expected that higher serum creatinine would correlate with higher urinary isoprostanes, whereas we found an inverse association. Meanwhile, a rarely considered issue is the dependency of urinary creatinine concentrations not only on the kidneys’ ability to filter and concentrate urine, but also its original concentration in the serum. Differences in serum creatinine (which correlate with body size, age and sex) could result in differences in urine creatinine irrespective of renal function and hydration, and thus, factors associated with differences in serum creatinine could confound associations of urinary creatinine indexed isoprostanes. In our analyses, we included serum creatinine as an early adjustment factor and also excluded individuals with eGFR <60 mg/dL/m2 to account for these confounders, and we found this to be particularly relevant in assessment of sex differences in oxidative stress. Our findings highlight the importance of adjustment for serum creatinine as a confounder in all future analyses of urinary isoprostanes, particularly if groups of interest are expected to have different serum concentrations. ## 4.6. Hypertension In our minimally adjusted models, IsoP concentrations were higher in urine and plasma in participants with higher systolic blood pressure, while use of antihypertensive medication was associated with lower plasma IsoP. This could be consistent with higher oxidative stress in those with uncontrolled hypertension [44] and reduction of oxidative stress achieved using commonly used antihypertensive medications [45]. However, given the nature of our study, we are unable to distinguish whether these differences stem from the vascular endothelium itself, secondary to end-organ distress, or are not causal at all. Future studies should consider blood pressure and antihypertensive use as independent factors that could affect isoprostane concentrations and use adjustments accordingly. ## 4.7. Plasma Lipids and Cholesterol Medications Statins, the cornerstone in lipid-lowering therapy, are reported to have pleiotropic effects ranging from lowering low-density lipoprotein cholesterol to reducing inflammation, and they are also believed to reduce oxidative stress [46,47]. On the other hand, reports to the contrary also exist [48,49]. Moreover, some studies suggest within-class differences [50,51,52], or similar effects from other types of cholesterol-lowering therapies [53]. In our study, cholesterol-lowering medication use was associated with lower IsoP, not associated with IsoP-M and associated with higher plasma IsoP (in some models). It is important to consider that isoprostanes are the result of lipid peroxidation themselves. Hence, associations may be due to changes in the metabolism and excretion of isoprostanes as opposed to true differences in in vivo oxidative stress [17]. Alternatively, the participant groups using these medications may not be entirely comparable in our urinary isoprostane vs. plasma isoprostane analysis due to differences in the timing of isoprostane sampling. At the time of plasma IsoP measurements, CARDIA participants were roughly 40 years old (Supplementary Table S2), so cholesterol-lowering pharmacotherapy in this group could be indicative of early dyslipidemia, or alternatively earlier access to outpatient medical care. Urinary isoprostanes were measured 10 years later when the average participant age was >52 years old, an age at which initiation of statin therapy is considerably more common. We report direct associations between higher fasting triglycerides and isoprostanes in urine IsoP m and blood IsoP. Similar associations have been previously reported, but as secondary findings and without adjustment for other CVD risk factors such as BMI [20,54]. Our results may suggest higher oxidative stress in hypertriglyceridemia, which can contribute to atherosclerotic disease. Contrary to triglycerides, HDL lipoproteins are often touted as a “scavenger” of oxidized lipids and are considered cardioprotective unless in extremely high concentrations [55]. In vitro studies have shown HDL (HDL3 in particular) to be the main lipoprotein carrier of isoprostanes in the blood [56], which may be consistent with their involvement in removal of these byproducts of oxidative stress. As urinary isoprostane assays measure de-esterified and hydrolyzed isoprostanes “freed” from the cells [17], a direct association linking higher HDL concentration and higher isoprostanes may be linked to increased “freeing” of this entity, resulting in higher measured plasma or urine concerntrations, despite potentially lower oxidative stress in tissues. Other potential mechanisms for this association may be differences in the quality of measured HDL, such as particle subtypes or sizes. The exact pathophysiologic mechanisms linking HDL cholesterol, isoprostanes and CVD remain unknown, and whether this association is cardioprotective or not requires further investigation. ## 4.8. Strengths and Limitations There are limitations to the presented study. Isoprostanes were measured only once per participant, and therefore we are unable to assess potential chronologic variations or longitudinal associations between oxidative stress and development of CVD risk factors. There are several methods used for isoprostane measurement, and the field lacks general assay standardization, limiting the interpretability of findings across studies. We used GC-MS, which is often considered the gold standard [57], though measurement inaccuracies are unavoidable. Due to the observational nature of the study, we cannot differentiate the effects of some risk factors (such as diabetes) from the effects of medications used to treat them, which may affect isoprostane concentrations or oxidative stress in general. We were bound by the inclusion/exclusion criteria that suited the original ancillary study, which included measurement of urinary isoprostanes, even though some of these criteria do not apply directly to the goals of our study. Our secondary analysis used plasma isoprostane measurements predating our primary urinary isoprostane analysis by 10 years and measuring total F2-isoprostanes without distinction between 8-isoprostanes and other subtypes. Therefore, comparison of associations may be limited by differences in prevalence of CVD risk factors present at each timepoint, differences in numbers of participants and the fact that isoprostane subtypes may have different association patterns (though demonstration of this point is arguably a strength of our analysis). Our study also has many strengths, including analysis of both IsoP and its metabolite IsoP m, which would be impervious to previously cited concerns about local production in the kidney and could add to the limited body of literature about differences in associations between risk factors and isoprostane sub-classes. Our results are further strengthened by inclusion of both urinary and plasma isoprostanes which, to our knowledge, is occurring for the first time in a community cohort study. Use of urinary isoprostanes as our primary biomarker is robust against ex vivo production of isoprostanes which would otherwise exhibit a great challenge in the previously collected samples typically available in biobanks of observational cohort studies. Our study population was derived from a well-characterized cohort, which allowed for adjustment for numerous potential factors as well as investigation of many CVD risk factors using standardized protocols. In addition, our study was performed in a relatively young and healthy population, while the majority of existing studies focus on populations with advanced disease. ## 5. Conclusions We provide broad and detailed analysis of associations between traditional risk factors for CVD, with urinary and plasma isoprostane concentrations which are well regarded as qualitative measures of in vivo systemic oxidative stress. 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--- title: 'Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists' authors: - Seyedehnafiseh Mirniaharikandehei - Alireza Abdihamzehkolaei - Angel Choquehuanca - Marco Aedo - Wilmer Pacheco - Laura Estacio - Victor Cahui - Luis Huallpa - Kevin Quiñonez - Valeria Calderón - Ana Maria Gutierrez - Ana Vargas - Dery Gamero - Eveling Castro-Gutierrez - Yuchen Qiu - Bin Zheng - Javier A. Jo journal: Bioengineering year: 2023 pmcid: PMC10044796 doi: 10.3390/bioengineering10030321 license: CC BY 4.0 --- # Automated Quantification of Pneumonia Infected Volume in Lung CT Images: A Comparison with Subjective Assessment of Radiologists ## Abstract Objective: To help improve radiologists’ efficacy of disease diagnosis in reading computed tomography (CT) images, this study aims to investigate the feasibility of applying a modified deep learning (DL) method as a new strategy to automatically segment disease-infected regions and predict disease severity. Methods: We employed a public dataset acquired from 20 COVID-19 patients, which includes manually annotated lung and infections masks, to train a new ensembled DL model that combines five customized residual attention U-Net models to segment disease infected regions followed by a Feature Pyramid Network model to predict disease severity stage. To test the potential clinical utility of the new DL model, we conducted an observer comparison study. First, we collected another set of CT images acquired from 80 COVID-19 patients and process images using the new DL model. Second, we asked two chest radiologists to read images of each CT scan and report the estimated percentage of the disease-infected lung volume and disease severity level. Third, we also asked radiologists to rate acceptance of DL model-generated segmentation results using a 5-scale rating method. Results: Data analysis results show that agreement of disease severity classification between the DL model and radiologists is >$90\%$ in 45 testing cases. Furthermore, >$73\%$ of cases received a high rating score (≥4) from two radiologists. Conclusion: This study demonstrates the feasibility of developing a new DL model to automatically segment disease-infected regions and quantitatively predict disease severity, which may help avoid tedious effort and inter-reader variability in subjective assessment of disease severity in future clinical practice. ## 1. Introduction Computed tomography (CT) is the most popular medical imaging modality used in clinical practice to detect lung diseases (i.e., lung cancer, chronic obstructive pulmonary disease, interstitial lung diseases, pneumonia, and others). To more accurately assess the severity of many lung diseases and predict patients’ prognosis, estimation of disease-infected volume and/or its percentage to the total lung volume plays an important role. However, subjective estimation of disease-infected regions or volume by radiologists is quite difficult, tedious, and inaccurate (due to the large intra- and inter-reader variability), which makes it often infeasible in busy clinical practice. Thus, to help solve this clinical challenge, developing computer-aided detection (CAD) schemes or methods has been attracting broad research interest. For example, the CAD-generated lung density mask has been well developed and tested to quantify percentages of emphysema-infected lung volume [1] or degree of lung inflammation [2]. However, quantifying other lung diseases, such as the pneumonia-infected lung volume, has not been well developed and evaluated. Thus, we propose to investigate the feasibility of developing new CAD schemes that can automatically segment pneumonia-infected regions depicted on CT image slices and quantify the percentage of the diseased lung volume, which has the potential to assist radiologists in more accurately and efficiently reading and interpreting chest CT images in diagnosis of pneumonia-infected disease diagnosis and assessment of its severity. In the last 3 years, SARS-CoV-2 virus named COVID-19 has infected millions of people globally [3] and it produces pneumonia-type diseases. Chest X-ray radiography and CT are two imaging modalities to assist diagnosis of COVID-19 induced pneumonia and/or monitor its severity [4]. While chest X-ray images are easier and faster to take, with lower cost, the CT scan is highly preferred mainly due to its three-dimensional nature and additional information to improve diagnostic accuracy [5,6]. Due to the wide and rapid spread of the COVID-19 virus, a large volume of chest X-ray images including CT images have been acquired in clinical practice. Meanwhile, several research image datasets with manual annotation masks have also become publicly available for researchers to develop new CAD schemes aiming to assist radiologists in more accurately and efficiently reading chest CT images to detect and diagnose COVID-19 induced pneumonia. Recently, in developing CAD schemes of medical images, deep learning (DL) models have been well recognized and widely used to perform the tasks of segmenting the disease-infected regions of interest (ROIs) [7,8] and detecting or classifying diseases using the automatically extracted image features [9,10]. In using COVID-19 image datasets to develop CAD schemes, most of the previous studies focused on developing DL models to detect COVID-19 cases or classify between the COVID-19 and normal or other types of pneumonia cases [11,12,13,14]. Although many previous studies reported the extremely high accuracy of using DL models to detect and/or classify the COVID-19 infected cases (i.e., ranging from 90–$100\%$ accuracy [15]), no previous DL model is robust and clinically acceptable due to training bias and a “black-box” type approach [16]. Thus, the motivation of this study is to overcome disadvantages of previous DL models and investigate how to optimally use DL models to assist radiologists through increasing their accuracy and efficiency of disease diagnosis in future clinical practice. For these purposes, we propose a hypothesis that, in the technology aspect, it is important to add an interactive graphic user interface (GUI) to the DL model as a visual aid tool to increase the transparency of the DL model and allow radiologists to visually inspect results of DL model-segmented infected lesions or regions. In this application aspect, it is important to perform more observer performance or preference studies using DL models, which can help researchers better understand how to optimally develop and apply DL models to the future clinical practice to assist radiologists. The objective of this study is to test our hypothesis. The study includes three steps or procedures. First, we build a novel ensembled DL model implemented with an interactive GUI to segment pneumonia-infected disease regions. Second, we conduct an observer reading and preference study that asks radiologists to estimate percentages of disease-infected volumes, assess disease severity, and rate their acceptance level for DL-generated lesion segmentation results. Third, we perform data analysis to compare agreement between the DL model and radiologists in the disease-infected region segmentation and disease severity assessment. The details of our study methods and results followed by discussions and conclusions are reported in this article. Specifically, Section 2 describes study datasets and the details of study methods to build a new DL model with a GUI tool and conduct the proposed observer study and data comparison analysis. Section 3 reports and explains study results. Section 4 discusses the unique characteristics or novelties and new observations or contributions of this study, as well as the limitations. Second 5 concludes this study and provides the take-home messages to the readers of this article. ## 2.1. Datasets In this study, three chest CT image datasets were used, which include two public datasets, namely, “COVID-19 CT scans” and “COVID-19 CT segmentation dataset “https://www.kaggle.com/andrewmvd/covid19-ct-scans (accessed on 17 May 2021)”. The first public dataset includes 20 CT scans of patients diagnosed with COVID-19 from two sources, Coronacases “https://coronacases.org/ (accessed on 17 May 2021)” and Radiopedia “https://radiopaedia.org/ (accessed on 17 May 2021)”. Although numerous COVID-19 image datasets are publicly available, one unique characteristic of the datasets selected in this study is that all CT images have been annotated by experts providing three separate masks for the left lung, right lung, and infection regions. The second public dataset contains 100 axial CT images acquired from more than 40 COVID-19 patients. A mask with three labels is provided by a radiologist for each CT image indicating ground-glass opacity (GGO), pleural effusion and consolidation regions. These two datasets were used to build and/or train the DL model of segmenting and qualifying the disease infected regions or volumes. Additionally, another independent testing dataset including 80 CT scans of COVID-19 patients acquired from “Hospital Regional III Hanorio Delgado” Arequipa, Peru, was also assembled. This dataset is used to test and evaluate the trained DL models and conduct the proposed observer reading and preference study. ## 2.2. Image Preprocessing To achieve higher reliability or robustness of the DL model, several image preprocessing techniques were employed to initially remove clinically unrelated images and normalize the remaining images. First, the “COVID-19 CT scans” dataset includes whole CT images of COVID-19 patients. However, some slices of each CT scan (i.e., in the beginning, and near the end of scan) usually contain very little lung area, thus not providing helpful information. Including these CT slices in the training data leads to a more unbalanced dataset. Thus, we removed up to $10\%$ of CT images at the beginning and near the end of each CT scan. Generally, all lung infection datasets are unbalanced since the number of infection mask pixels is significantly less than the pixels of the healthy lung and other normal tissues presented in the image. To create a more balanced training dataset, we removed all healthy CT slices with no infection mask. Second, since image normalization or standardization has been considered as an important preprocessing step when training deep neural networks to achieve high robustness or scientific rigor [17], we normalized all CT images by clipping the intensities outside the range [–1024, 600] HU. Specifically, if x > max, x’ = max, if x < min, x’ = min, and the remaining values are scaled between zero and one using a linear mapping equation: x’ = (x-xmin)/(xmax-xmin). Third, we applied the data augmentation technique to generalize and enlarge the dataset and mitigate overfitting. The main augmentation method adopted in this study is Elastic Transform [18] which is commonly applied in biomedical image analysis. The python library Albumentations [19] was used to perform the Elastic Transform and other affine transformations. Along with the elastic Transform, we also applied other common methods of horizontal and vertical flipping and random rotation to increase the size of training images. Figure 1 demonstrates the changes in a CT slice after applying an augmentation method in this study. Last, we applied another image preprocessing technique using several filters to further enhance image features detected on the CT image. In this step, several filters have been tested with various channel arrangements to enhance different textures and structures and consequently achieve better discrimination between healthy and infected regions. For example, contrast Limited Adaptive Histogram Equalization (CLAHE) is one of the filters that has been applied as a channel to the CT images. CLAHE is a variant of adaptive histogram equalization that limits contrast amplification to reduce noise amplification. This filter performs histogram equalization in small patches with high accuracy and contrast limiting. Figure 2 illustrates the effect of applying a CLAHE filter on a CT image. ## 2.3. Image Segmentation Models and Output Several common deep neural network models were selected and used in this study, including UNet [20], Feature Pyramid Network (FPN) [21], and Attention Residual UNet (AR-UNet) [22]. The Segmentation Models library [23] available on GitHub was also used to test various segmentation models with different backbones and parameters more conveniently. For each model, many parameters have been tested and modified, including loss functions, fixed and variable learning rates, encoders and decoders, and dropout rates. ## 2.3.1. Lung Segmentation The first step is to segment the lung area depicted on each CT slide. For this purpose, a publicly available model for lung parenchyma segmentation was used to create lung masks and segment the lung area [24]. In brief, this model used the UNet, with the only adaption being batch normalization after each layer. Figure 3 demonstrates an example of the created lung mask and the lung segmentation result using this mask. ## 2.3.2. Infection Area Segmentation The next step is to segment the disease infected lung regions (from fuzzy ground glass to consolidation patterns). For this purpose, various object detection and segmentation models with different hyper-parameters have been tested and employed to achieve the highest accuracy. First, the AR-UNet is selected to build the ensembled model in this step. AR-UNet model is an end-to-end infection segmentation network, which embeds an attention mechanism and residual block simultaneously into the UNet architecture. Hence, this model efficiently balances the limited training data. In this model, the attention path employs the attention mechanism to capture spatial feature details. The residual block involves the semantic information flow through a 1 × 1 convolution [25]. Based on the literature search and our experiments, we recognize that among many tested loss functions, the Binary cross-entropy loss and the Tversky loss [24] led to the best predictions. Binary cross-entropy is calculated as the following Formula [1] [26]. [ 1]LBCE=−∑$i = 12$tilogpi where ti is the truth value (either 0 or 1), and pi is the SoftMax probability for the ith class. To compute the Tversky loss function, a SoftMax along each voxel is applied [24]. Let P and t be the predicted and truth binary labels, respectively. The Dice similarity coefficient (D) between two binary volumes is identified and computed using Formula [2]:D (P, t) = 2\|Pt\|/(\|P\| + \|t\|)[2] Since, in most cases, non-lesion voxels outnumber the lesion voxels, one of the main challenges in medical imaging is imbalanced data, especially in lesion segmentation. Therefore, using the unbalanced data in training lead to predictions that are severely biased towards low sensitivity (recall) and high precision, which is not desired, particularly in medical applications where false-positive (FP) detections are much more tolerable than false negatives (FNs). To achieve an optimum balance between sensitivity and precision (FPs vs. FNs), we used a loss layer based on the Tversky index. This index allows us to put emphasis on FNs and leads to high sensitivity. Using the formula [2] in a training loss layer, it equally weighs recall and precision, FN and FP, respectively [24]. To weigh FNs more than FPs in the training of a network with highly imbalanced data where small lesions’ detection is essential, a loss layer based on the Tversky index is efficient. The Tversky index is computed as the Formula [3] [24]:Ti(P,t,α,β) = \|Pt\|/(\|Pt\| + β\|P⁄t\| + α\|t⁄P\|)[3] where α and β control the magnitude of penalties for FNs and FPs, respectively. Hence, the finally used Tversky loss function is defined as follows using Formula [4] [24]:[4]LTα,β=∑$i = 1$Np0iv0i∑$i = 1$Np0iv0i+β∑$i = 1$Np0iv1i+α∑$i = 1$Np1iv0i In the above equation, p0i and p1i are the probability of voxel i lesion and non-lesion, respectively. Additionally, v0i is 1 for a lesion and 0 for a non-lesion voxel and vice versa for the v1i. Since image segmentation accuracy and robustness depend on choosing and use of DL models along with optimal training parameters, to more accurately and robustly segment disease infection areas or blobs depicting on chest CT images, we developed, tested, and compared five models based on AR-UNet with different training parameters, as summarized in Table 1. Additionally, based on the hypothesis that if the five models contain complementary prediction scores of pixels belonging to a disease infected area, the fusion of the predictions of all five selected models can further improve image segmentation results (i.e., prevent under-segmentation as much as possible). While involving several models comes with a longer processing time, the more reliable and precise prediction is worth the extra time. For each of these models, we have used Adam optimizer with a learning rate of 0.01. ## 2.3.3. Segmentation of GGO and Consolidation Patches Moreover, besides the overall infected region segmentation, it is of great importance to distinguish between different stages of COVID-19-infected pneumonia developments in the lung and provide better assistance to radiologists to assess disease severity levels. The “COVID-19 CT segmentation dataset” provides manual annotations with 3 infection types, the ground glass opacity (GGO), pleural effusion, and consolidation. Since the pleural effusion type is not of great interest in this study, we only included the GGO and consolidation labels in the training dataset. Like the infection region segmentation model, we tested various neural network architectures and hyperparameters aiming to achieve the best predictions. We applied a FPN model to categorize different stages of the COVID-19 in the infected area. This model has 23,915,590 trainable parameters. As depicted in Figure 4, the patch segmentation is based on Residual-Network (ResNet) and FPN model. ResNet34 is the backbone, and FPN is the feature extractor network. The loss function for this model is the categorical cross entropy which computes the cross entropy between the labels and predictions. This loss function is common when there are two or more label classes. Although the staging model tends to over-segment the GGO regions, the consolidation segmentation is very accurate. To prevent the over-segmentation of the GGO area, the infection segmentation model is used to constrain the staging model. This model classifies each patch to three classes of normal tissue background, GGO, and consolidation. ## 2.3.4. Integrated Model and GUI In summary, three common deep neural network architectures were trained and employed in this study. For lung segmentation, we applied a publicly available model for lung parenchyma segmentation based on the UNet model. Additionally, an ensembled AR-UNet was developed for infection segmentation since the attention blocks have been shown to be very beneficial in image segmentation [22]. Moreover, an FPN model was applied to categorize the severity of the COVID-19 infected area. For each model, many parameters were tested and modified, including loss functions, fixed and variable learning rates, different encoders and decoders, and dropout rates. All models are written in Python, and the TensorFlow library is used to train and test the models. After extracting the lung and infected lesions by the two segmentation models, the percentage of the infected lung volume is reported along with the average Hounsfield units (HU) inside the infected region, which can indicate the density of the lesion of interest and hence the severity of infection. This information is reported for the left and right lungs for each CT slice as well as the whole CT. Finally, to assist radiologists in the diagnosis of COVID-19 infected pneumonia using the DL model generated quantitative results or predictive scores, we also designed a stand-alone graphical user interface (GUI) as an interactive “visual-aid” tool, which can be installed on any Windows-based computers without the need for any specific programing language or library. Figure 5 illustrates the flow diagram of the developed DL model method and GUI tool. ## 2.4. Image Postprocessing and Correction After observing the output of the lung segmentation model, it was noted that in several cases with severe disease infection, a small percentage of the lung may be missing from the segmentation as shown in Figure 6a, which typically represents the disease infection area. To recover the missed lung area if the lung segmentation error is visually observed from our GUI, the user (i.e., radiologist) can call a specially-designed image post-processing function that applies a unique conventional image processing algorithm inspired by the rolling ball algorithm [27] to automatically correct segmentation error. This algorithm starts with extracting the lung contours followed by several steps and morphological filters such as disk drawing, filling holes, median, and erosion operations. As shown in Figure 6, it can convert a jagged and rough lung boundary, as shown in Figure 6a, to a smooth one that covers the previously missed lung area, as shown in Figure 6b. While it might lead to a small over-segmentation in some cases, the previously missed area contains very important infected lesions that can significantly affect the assessment of severe cases. ## 2.5. Evaluation To evaluate new DL model performance, the model was first tested “as is” using an independent testing dataset of 80 CT scans. Next, we asked two expert chest radiologists to retrospectively read and review these 80 sets of CT images. Each radiologist read and examined half of the CT scans (40 patients) and reported the patient infection spread in percentage based on their judgment of the percentage of infected lung volume. These subjectively assessed values were then collected and compared to the values generated by the DL model. It is important to note that in this new testing image dataset of 80 clinical cases, there are no manually annotated lung and disease infection area segmentation marks. Thus, no Dice coefficients can be computed, and we only compared the agreement between the radiologists and the DL model in predicting the percentage of disease infected lung area (or volume) based on the predicted result of infection area ratio or spread scores between radiologists’ assessment and DL models. Moreover, in order to test radiologists’ confidence level to accept DL-generated infection area segmentation results, we showed radiologists the DL segmentation results displayed on the developed GUI and asked them to rate their acceptance level of the infection area segmentation of each CT slice with a score of 1 (poor segmentation) to 5 (excellent segmentation). Last, we asked the radiologists to assign each patient to the group of mild infection cases that are dominated by GGO or the group of severe infection cases that have a significant fraction of consolidation areas or blobs. We then compared the agreement between the DL model generated case classification results and the radiologists’ classification results. A corresponding confusion matrix was generated for the comparison and diagnostic accuracy computation. ## 3. Results Figure 7 shows several image examples of DL-model generated lung and infection segmentation results. The left column illustrates the raw CT images, while the second and third columns illustrate the masks of the segmented lung and disease infection areas, respectively. In addition, Figure 8 shows the patch segmentation results of GGO and consolidation areas (or blobs), respectively. By using the commonly used evaluation index in image segmentation namely, the intersection over union (IOU), the quantitative data analysis results show that IOUs are 0.78 and 0.88 for the disease-infection region segmentation model and for the patch model, respectively. Figure 9 shows a snapshot of the GUI window used in this study to obtain the subjective ratings from the radiologists. Using this GUI tool, radiologists can observe the raw CT image and the predicted segmentation side by side for better comparison. The radiologists can also rate the accuracy or acceptance level of the DL-generated disease infection area segmentation on each slice using a rating scale from 1 to 5, as well as provide their overall assessment of lung infection spread. Additionally, the lung segmentation is also visualized to make sure that the predicted spread scores are reliable. If a significant portion of the lung is missing, the radiologist can call and run the function to correct the segmentation errors as described in the Methods section of this paper. Figure 10 shows two diagrams that illustrate the distribution of our data analysis results to compare the agreement between the DL-model and radiologists in segmentation or estimation of disease-infected volumes, and acceptance level by radiologists of DL model generated disease region segmentation results. From these two summary or comparison diagrams, we observe the following study results. Additionally, the ratings of the testing cases with high spread score accuracy have been carefully analyzed to ensure that the high accuracy is not by chance. For example, among the testing cases with more than $95\%$ spread accuracy, the radiologists rated an acceptance score higher than 3 in over $78\%$ of cases, and among the testing cases with >$90\%$ accuracy, $84\%$ of cases received an acceptance rating higher than 3 indicating the DL segmentation is acceptable, and the spread score is reliable. Moreover, to evaluate the performance of our DL model in identifying different stages of COVID-19, the radiologists also put a label on the infected regions. Then, the results of our model and radiologists were compared together. Table 2 shows the confusion matrix of the disease staging performance. When using radiologists’ rating or disease level classification results as a reference (“ground-truth”), our DL model yields an $85\%$ ($\frac{68}{80}$) accuracy in predicting or classifying disease infection severity levels in this testing dataset. ## 4. Discussion In the last three years, large number of studies have been reported in the literature to develop DL-based models of detection and classification of COVID-19 infected pneumonia using chest X-ray radiographs and/or CT images. However, as reported in a comprehensive review study [16], no previous DL model was accepted in clinical practice to effectively assist radiologists. To effectively address or solve this challenge and make the DL model acceptable to radiologists, we conducted a unique model development and observer-involved comparison study. This study has the following unique characteristics and/or new observations. First, we tested a new hypothesis to quantify percentages of COVID-19 infected volume and demonstrated a potential application of a novel DL model in the segmentation of the COVID-19 generated pneumonia infection in chest CT images. One of the innovations of this study is that we developed a combined five AR-UNet models for the infected region segmentation and a novel lung segmentation correcting algorithm based on conventional image processing techniques to ensure all infected lesions are included in the prediction. Furthermore, we applied an FPN model to identify different stages of the COVID-19 infected area. Second, since physicians including radiologists have low confidence in accepting results generated by current “black box” type artificial intelligence (AI) or DL models, developing “explainable AI” tools [28] has been attracting broad research interest in the medical imaging field. Thus, we designed and implemented a graphic user interface (GUI) as an interactive “visual-aid” tool (Figure 9) that shows DL segmented disease infection areas. This stand-alone GUI allows radiologists to easily navigate through all generated outputs, rate each CT slice automatic segmentation, and submit their assessment of the percentage of lung volume with COVID-19 infection. Additionally, the radiologist can also call a supplementary image postprocessing algorithm to automatically correct the possibly identified segmentation errors. Our experience and results of the observer reading and preference study demonstrate that using this interactive GUI-based “visual-aid” supporting tool can provide radiologists with the reasoning of DL model generated prediction results and thus increase their confidence to use the DL model in their decision-making process of disease diagnosis. Third, based on our interaction with the radiologists, we learned that radiologists typically assign the patients into 3 classes of disease severity, namely, mild, moderate, and severe diseases, based on the distribution or domination of GGO, pleural effusion, and consolidation patterns. Thus, we believe that to increase its clinical utility, the DL model should also have a function or capability to assign each testing case to one of these three classes. Since in three image datasets used in this study, very few pleural effusion patterns exist, we developed a patch segmentation-based model to identify GGO and consolidation areas depicted on each CT image slice and then predict or classify the cases into either mild/moderate (A) and severe (C) classes as shown in Table 2. In this way, we were able to compare disease severity prediction results between the radiologists and DL model. In future studies, we need to collect more study cases with more diversity. Thus, we can apply the same DL concept to train the model that enables us to classify 3 classes of disease severity. Fourth, we conducted a unique observer reading and preference study involving two chest radiologists and reported data comparison results. Thus, unlike many previous studies in this field, which only reported Dice coefficients of agreement between DL model generated image segmentation results and the manual segmentation results of one radiologist, which does not have a real clinical impact due to the large inter-reader variability in manual image segmentation or annotation, we used a simple and more efficient or practical method to evaluate DL model segmentation results by asking radiologists to rate the acceptance level of DL model segmentation using a 5 rating scale. This practical approach has proved quite effective and higher clinically relevant in the medical imaging field [29]. Our study generates quite encouraging results or observations of the higher agreement between the DL-model generated segmentation and radiologists’ estimation of the COVID-19 infected region or volume, as well as the higher acceptance rate of radiologists to the DL model-segmented results (Figure 10). The above observations also demonstrate a new contribution of this study, which provides the research community with new scientific data or evidence. [ 1] Our study demonstrates a higher acceptance rate of radiologists to DL model generated results of disease-infected region segmentation. This supports the feasibility of improving the efficacy of radiologists in reading CT images to diagnose disease because the DL model can not only replace the tedious and time-consuming process of subjectively estimating the percentages of the pneumonia regions or volume, but also avoid or reduce the large inter-reader variability. [ 2] Our study also supports the importance of future evaluation studies to better investigate and find the optimal interaction between DL models and radiologists to reduce the application gaps and facilitate the process to make DL models or technology clinically useful or acceptable tools in future clinical practice. [ 3] Although this study only used COVID-19 cases to segment and quantify pneumonia regions or volume, if successful, the demonstrated new DL model and evaluation approach can be easily adapted to segment and quantify other types of virus infection pneumonia or other interstitial lung diseases (ILD) in future research studies. Last, we also recognize the limitations of this study, including the small image datasets and involving only two radiologists. Thus, this is a very preliminary study. The developed DL model along with the GUI tool needs to be further optimized and validated using large and diverse image cases. We also need to recruit more radiologists to evaluate model performance and potential clinical utility in future studies. Despite the limitations, we believe that this is a unique and valid study. ## 5. Conclusions In this study, we developed a new ensembled DL model to automatically segment and quantify the COVID-19 infected pneumonia region or volume and predict disease severity level. To increase the model transparency and radiologists’ confidence in considering or accepting DL model generated results, we designed and integrated an interactive GUI as a “visual aid” tool to the DL model. The most important novelty or contribution of this study is that we conducted a unique observer reading and preference study. The data analysis and comparison results demonstrate the higher agreement between DL model and radiologists in disease region segmentation or estimation and disease severity level prediction. However, this is a preliminary and concept-approval type study. More evaluation studies involving more radiologists and more diverse image cases are needed in future research. 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--- title: Dietary Guanidine Acetic Acid Improves Ruminal Antioxidant Capacity and Alters Rumen Fermentation and Microflora in Rapid-Growing Lambs authors: - Wenjuan Li - Zhaoyang Cui - Yaowen Jiang - Ailiyasi Aisikaer - Qichao Wu - Fang Zhang - Weikang Wang - Yukun Bo - Hongjian Yang journal: Antioxidants year: 2023 pmcid: PMC10044800 doi: 10.3390/antiox12030772 license: CC BY 4.0 --- # Dietary Guanidine Acetic Acid Improves Ruminal Antioxidant Capacity and Alters Rumen Fermentation and Microflora in Rapid-Growing Lambs ## Abstract Guanidine acetic acid (GAA) has been reported to improve growth performance, nutrient utilization, and meat quality in livestock. This study aimed to investigate whether coated GAA (CGAA) in comparison with uncoated GAA (UGAA) could have different effects on rumen fermentation, antioxidant capacity, and microflora composition in the rumen. Seventy-two lambs were randomly arranged in a 2 × 3 factorial experiment design with two diets of different forage type (OH: oaten hay; OHWS: oaten hay plus wheat silage) and three GAA treatments within each diet (control, diet without GAA addition; UGAA, uncoated GAA; CGAA, coated GAA). The whole feeding trial lasted for 120 days. The lambs in the OH group presented lower total volatile fatty acid (VFA), alpha diversity, Firmicutes, NK4A214_group, and Lachnospiraceae_NK3A20_group than those on the OHWS diet in the last 60 days of the feeding stage ($p \leq 0.05$). Regardless of what GAA form was added, dietary GAA supplementation increased the total VFA, microbial crude protein (MCP), adenosine triphosphate (ATP), and antioxidant capacity in rumen during lamb feedlotting ($p \leq 0.05$). However, molar propionate proportion, acetate:propionate ratio (A:P), and relative Succiniclasticum abundance decreased with GAA addition in the first 60 days of the growing stage, while the molar butyrate proportion and NK4A214_group ($p \leq 0.05$) in response to GAA addition increased in the last 60 days of feeding. These findings indicated that dietary GAA enhanced antioxidant capacity and fermentation characteristics in the rumen, but the addition of uncoated GAA in diets might cause some dysbacteriosis of the rumen microbiota. ## 1. Introduction Guanidine acetic acid (GAA), with a molecular formula of C3H7N3O2 and a molecular weight of 117.11, is synthesized in the kidney, liver, and pancreas from L-arginine and glycine, and then converted to creatine to participate in the metabolism of energy and proteins [1]. As a nutritive feed additive, GAA has been used to improve growth performance, carcass characteristics, and meat quality in pigs [2], chickens [3], bulls [4], and sheep [5]. Unlike monogastric animals, host ruminants and rumen microorganisms are mutually beneficial and symbiotic [6]. Rumen microbial fermentation of feeds produces volatile fatty acids (VFA) and microbial protein to provide most of the available energy and protein required by host ruminants [7]. A previous study in Angus bulls noted that dietary GAA addition shifted ruminal fermentation towards greater propionate production [4]. The addition of GAA increased rumen total VFA production and microbial populations [8], with the ruminal degradation rate of GAA reported to be 47–$49\%$ in cattle [9]. However, it is unclear whether the coated GAA could sacrifice the aforementioned effects on rumen fermentation. Oxidative stress in the rumen is detrimental to ruminant health [10]. Dietary additives with antioxidant features are often applied to avoid oxidative stress, especially during high-concentrate feeding [11]. Common antioxidant supplements include probiotic-based [12], selenium [13], and vitamin E [14]. As a nutritive additive, GAA donates an electron from its conjugate base and generates superoxide, a strong free radical [15], and may therefore be a direct pro-oxidant. However, GAA metabolites (e.g., creatine and arginine) might be able to quench free radicals after GAA ingestion [16,17,18]. A previous study on growing lambs reported that dietary GAA addition elevated the activities of serum catalase (CAT) and glutathione peroxidase (GSH-Px) and decreased malondialdehyde (MDA) content in skeletal muscle tissue [5]. However, it is not clear whether dietary GAA addition could present antioxidant capacity in the rumen, or what difference there could be compared with the coated GAA. It is well known that forage type is an important factor affecting rumen fermentation as growth performance in sheep feeding practice. For instance, feeding sheep oaten hay as a forage source was found to maintain the stable state of their rumen internal environment and the growth of rumen microorganisms [19]. Wheat silage (WS) can provide an interim forage during the period when the previous year’s hay or silage has run out and the present year’s has not been harvested. A previous study in finishing beef cattle noted that feeding WS was found to decrease the acetate:propionate ratio in rumen and presented greater growth performance [20]. However, relevant research on sheep is scarce. Considering sheep are less tolerant to acid whole silage as sole forage, lamb diets with WS plus OH in comparison with sole OH as forage type were applied in the present lamb feedlotting trial. The primary objective was to elucidate whether or not the coated GAA in comparison with uncoated GAA addition could improve rumen fermentation, antioxidant capacity, and how they affect rumen microflora in rapid-growing lambs, depending on the diet with different forage type sources. ## 2.1. Animal Ethics Statement The experimental animals, design, and animal management in the present study followed the Guidelines of the Beijing Municipal Council on Animal Care (with protocol CAU20171014-1) and were in accordance with the recommendations of the academy’s guidelines for animal research. ## 2.2. Guanidinoacetic Acid Products The uncoated GAA (available content of 984 g/kg, the average rumen degradation rate of UGAA is $50.9\%$) and coated GAA (available content of 600 g/kg, the packaging material was mainly fat powder, the average rumen degradation rate of CGAA is $15.8\%$). added in this study were in powder form and provided by Hebei Guang rui Company (Shijiazhuang, China). ## 2.3. Experimental Animals, Diets, and Design Seventy-two two-month-old healthy male small-tailed Chinese Han lambs (a breed of sheep native to Shandong Province of China, with a rate of reproduction reaching $229\%$) initially weighing 12 ± 1.6 kg in body weight (BW) were chosen as experimental animals and fed total mixed rations (TMR). A 2 × 3 factorial feeding experimental design was applied to divide the animals into two forage types of TMRs (OH: oaten hay; OHWS: oaten hay plus wheat silage), and three GAA addition groups (GAA: 0 g/kg; UGAA: uncoated GAA, 1 g/kg; CGAA: coated GAA, 1 g/kg). GAA (UGAA and CGAA) was added to the concentrates, then mixed with the corresponding forage, and divided into two daily feeds (08:00 and 16:00). Each treatment was randomly divided into four bamboo-slotted bedding pens, and each pen was arranged with three lambs, while clean, fresh water was available at all times. All rations were formulated to satisfy the nutrient requirement of 300 g gain/day [21]. The composition and nutrient levels of the experimental diet are shown in Table A1. The study periods consisted of 7 days of adaptation and 120 days of a 2-stage data collection. ## 2.4.1. Rumen Fluid Sampling Rumen fluid samples were collected at the end of every stage (d 60 and d 120). One hour after the morning meal, an oral stomach cannula (MDW15, Colebo Equipment Co., Ltd., Wuhan, China) and a 200 mL syringe were used to collect rumen fluid samples. The first 2 tubes of rumen fluid were discarded to avoid saliva contamination [8], and a 100 mL rumen fluid sample was collected from 8 lambs in each group. Ruminal pH was immediately determined by a digital pH meter (Testo205 type, Testo AG, Lenzkirch, Germany). Subsequently, the samples were strained through four layers of cheesecloth. A total of 0.25 mL of metaphosphoric acid (25 g/100 mL) was added to aliquots of 1 mL rumen fluid, which were centrifuged at 20,000× g at 4 °C for 15 min to determine the VFA, and two aliquots of 2 mL samples were taken to determine ammonia nitrogen (NH3-N) concentration and microbial protein (MCP). Three aliquots of 1 mL samples were taken to determine GAA, creatine, guanidinoacetate N-methyltransferase (GAMT), L-Arginine: glycine amidino-transferase catalyzes (AGAT), adenosine triphosphate (ATP), superoxide dismutase (SOD), catalase (CAT), gluta-thione peroxidase (GSH-Px), malondialdehyde (MDA), total antioxidant capacity (T-AOC), glutathione (GSH), and microbiota. ## 2.4.2. Rumen Fermentation Index Measurement The centrifuged sample referred to above was filtered using a 0.22 mm syringe filter. The VFA was quantified using a high-performance gas chromatograph (HPGC; GC-128; INESA Corporation) equipped with a hydrogen flame detector and a capillary column (FFAP, Zhonghuida Instruments Co., Ltd., Dalian, China; 50 m long, 0.32 mm diameter, 0.50 µm film). The VFA was identified and quantified from the chromatograph peak areas using calibration with external standards [22]. The rumen liquid NH3-N was measured according to Bremner and Keeney’s [1965] method [23], which calls for the use of a spectrophotometer (UV-6100, Mapada Instruments Co., Ltd., Shanghai, China). The microbial protein was then quantified using the purine derivative method [24]. ## 2.4.3. Ruminal GAA, Creatine, Enzyme Activity, and Antioxidant Capacity Related to GAA Metabolism GAA and creatine in rumen liquid were determined reference to the study by Wada et al. [ 25]. To this end, 1 mL of rumen liquid was aliquoted into another centrifuge tube, and 3 mL $5\%$ aqueous solution of sodium sulfosalicylate was added and mixed into the rumen liquid to precipitate the protein. The mixture was incubated at room temperature for 10 min and then centrifuged at 12,000× g for 10 min. Subsequently, the centrifuged sample mentioned above was filtered using a 0.22 mm syringe filter for the determination. The determination conditions were as follows: C18 weak acid cation exchange column (4.6 mm × 250 mm, 5 μm) was used; the flow rate was 0.6 mL/min; the column temperature was 30 °C; the detection wavelength was 210 nm; the elution mode was one-time linear elution; and the sample size was 10 μL. Thiobarbituric acid reactive substances (TBARS) assay measured MDA as a product of lipid peroxidation. The activity of enzymes SOD, CAT, and GSH-PX, and the contents of T-AOC, GSH, and ATP in rumen were measured using an assay kit (Jiancheng Biochemical Reagent Co., Nanjing, China) according to the manufacturer’s instructions. The GAMT and AGAT activities were measured in ruminal fluid of the lambs using a corresponding enzyme-linked immunosorbent assay kit (JinHaiKeYu Biochemical Reagent Co., Beijing, China). ## 2.4.4. Ruminal Microorganism DNA Extraction, PCR Amplification, and Sequencing A Fast DNA® soil DNA Kit (MP Biomedicals, Santa Ana, CA, USA) was used to extract the total microbial DNA from 60 rumen fluid samples according to the manufacturer’s instructions. DNA purity and concentration were detected with a NanoDrop2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington, DE, USA), and DNA integrity was assessed with $1\%$ agarose gel electrophoresis. The following amplification primers for 16 rRNA (V3 + V4) were used: 341F: 5′CCTAYGGGGBGCASCAG3′; 806R: 3′GGACTACNNGGGTATCTAAT5′. The PCR amplification of the 16S rRNA gene is shown in Table A2, and the PCR system is shown in Table A3. The PCR product was extracted from $2\%$ agarose gel and purified using an AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) according to the manufacturer’s instructions and quantified using a Quantus™ Fluorometer (Promega, Madison, WI, USA). Sequencing was conducted on a MiSeq PE300 platform (Illumina, San Diego, CA, USA) according to the standard protocols of Majorbio Bio-Pharm Technology Co., Ltd. (Shanghai, China). The raw reads were deposited into the NCBI Sequence Read Archive (SRA) database (PRJNA846357). ## 2.4.5. Processing of Sequencing Data Raw sequencing data were merged using the sliding window method and paired using FLASH (version 1.2.11, https://ccb.jhu.edu/software/FLASH/index.shtml (accessed on 27 September 2021)) on the basis of overlapping bases. The allowed mismatches of barcode and primer mismatch were 0 and 2, respectively. UPARSE software (version 7.0.1090, http://drive5.com/uparse/ (accessed on 9 October 2021)) was used to perform operational taxonomic unit (OTU) sequence clustering, and their relative abundances were used to calculate rarefaction curves and values of the Shannon Diversity Index using UPARSE version 7.1 (http://drive5.com/uparse/ (accessed on 8 November 2021)). The ribosomal database project (RDP) classifier (http://rdp.cme.msu.edu/ (accessed on 8 November 2021)) was used to classify and annotate each sequence through comparison to Silva (Release132, http://www.arb-silva.de/ (accessed on 8 November 2021)) with a comparison threshold of $70\%$ [26]. Alpha diversity analysis that included the Sobs, Shannon, Ace and Coverage indices at the OTU level was conducted using Mothur software (version 1.30.2, https://www.mothur.org/wiki/Download_mothur (accessed on 8 November 2021)). The beta diversity analysis and the principal coordinates analysis (PCoA) was analyzed at the OTU level with the distance algorithm of weighted normalized UniFrac. The differential bacteria were analyzed using the linear discriminant analysis effect size (LEfSe) software (http://huttenhower.sph.harvard.edu/galaxy/root?Tool_id¼lefse_Upload (accessed on 8 November 2021)). Bar plots and non-metric multidimensional scaling (NMDS) were generated using R software. ## 2.5. Statistical Analysis Data for each feeding stage were analyzed using the MIXED procedure of the Statistical Analysis System Institute [27]. Ruminal antioxidant capacity, rumen fermentation, and GAA metabolism were analyzed. The model was applied as follows:Yijk=μ+Gi+Fj+(G×F)ij+Rk+eijk where Yijk is the dependent variable, µ is the overall mean, Gi is the fixed effect of GAA products ($i = 3$: control, uncoated GAA, coated GAA), Fj is the fixed effect of total mixed ration type with different forage types (OH and OHWS), and G × F is the interaction of GAA and ration type. Rk is the random effect of animals ($k = 12$ per treatment) or pens ($k = 3$ per treatment), and eijk is the residual error term. The least squares means and standard errors of the means were calculated using the LSMEANS statement of the SAS software. Significance was declared at p ≤ 0.05, unless otherwise noted. ## 3.1. Rumen Fermentation At stage 1, interaction between forage type and GAA addition was found for the total VFA, acetate, and A:P (Table 1). UGAA and CGAA addition in the OH group increased the total VFA ($p \leq 0.05$), whereas only CGAA addition increased the total VFA in OHWS group ($p \leq 0.05$). The UGAA supplementation in the OH group increased the acetate proportion and decreased the A:P ($p \leq 0.05$), whereas there was no difference in the OHWS group ($p \leq 0.05$). Similarly, no difference was observed for rumen fermentation parameters with the two forage types ($p \leq 0.05$). Both UGAA and CGAA increased the content of total VFA ($p \leq 0.001$), A:P ($$p \leq 0.011$$), NH3-N ($$p \leq 0.025$$), and MCP ($$p \leq 0.015$$), but decreased the propionate proportion ($$p \leq 0.009$$) and pH value ($$p \leq 0.023$$). The addition of CGAA to the OH diet increased the butyrate proportion compared to the UGAA and control ($p \leq 0.05$). At stage 2, the significant Forage × GAA interaction was observed on total VFA, acetate, and A:P. Dietary UGAA and CGAA addition in the OH group increased the total VFA ($p \leq 0.05$) and A:P ($p \leq 0.05$), but this phenomenon did not occur in the OHWS group. However, UGAA and CGAA decreased the acetate proportion in the OHWS group, and there was no change in the OH group. Compared to the OHWS diet, the lambs fed the OH diet had lower total VFA ($$p \leq 0.018$$). The concentration of total VFA ($p \leq 0.001$) and percentage of butyrate ($$p \leq 0.008$$) and MCP ($$p \leq 0.011$$) were higher with the addition of UGAA or CGAA, and the pH value showed a tendency to decrease ($$p \leq 0.082$$). The other indicators did not alter in response to the addition of GAA. ## 3.2. Ruminal GAA, Creatine, ATP, and Related Metabolic Enzymes As shown in Table 2, the forage × GAA interaction was not significant for GAA, creatine, ATP, and related metabolic enzymes at stage 1 and stage 2. At stage 1, no difference was observed for ruminal GAA, creatine, GAMT and AGAT in the two forage types ($p \leq 0.05$). UGAA or CGAA addition increased GAA ($p \leq 0.001$), GAMT ($$p \leq 0.002$$), and ATP ($$p \leq 0.014$$) but decreased AGAT ($$p \leq 0.025$$) activity compared to the control. At stage 2, the concentration of GAA, creatine, ATP, and the activity of GAMT and AGAT did not differ between the two forage types ($p \leq 0.05$). Furthermore, regardless of the form of GAA added, the lambs presented greater rumen GAA ($p \leq 0.001$) and ATP ($$p \leq 0.001$$) than the control. No significant differences were observed among the treatments in the rumen creatine, or in the activity of GAMT and AGAT ($p \leq 0.05$). ## 3.3. Ruminal Microbiota The alpha diversity was not affected by forage type or GAA addition, except that the lambs in the OH group, in comparison with the OHWS group, presented lower sobs ($$p \leq 0.017$$), ace ($$p \leq 0.011$$), and chao ($$p \leq 0.010$$) in stage 2 (Supplementary Table S1). In stage 1, the effects of the interaction between forage type and GAA addition were found on the Bacteroidota (Table 3). Dietary CGAA in the OH group presented greater Bacteroidota than dietary CGAA ($p \leq 0.05$), whereas there was no difference in the OHWS group ($p \leq 0.05$). The relative abundance of Firmicutes, Bacteroidota, Synergistota, Patescibacteria, and Proteobacteria did not alter in response to either forage type or GAA addition. In stage 2, the effects of the interaction between forage type and GAA addition were found on the Firmicutes and Bacteroidota. Dietary UGAA and CGAA in the OH group presented greater Firmicutes compared with the control group ($p \leq 0.05$); dietary UGAA in the OH group presented lower Bacteroidota compared with the control group ($p \leq 0.05$), whereas there was no difference in the OHWS group ($p \leq 0.05$). Compared with the OHWS diet, the lambs fed the OH diet had lower ($$p \leq 0.039$$) Firmicutes. The relative abundance of microbiota did not alter in response to GAA addition ($p \leq 0.05$). It is worth mentioning that whatever form of GAA was added, the lambs fed the OHWS diet presented lower Patescibacteria levels ($p \leq 0.05$). At the genus level (Table 4), forage type and GAA addition interacted ($$p \leq 0.040$$) to affect the abundance of the NK4A214_group at stage 1. Dietary UGAA in the OH group presented a lower NK4A214_group compared with the control group ($p \leq 0.05$), whereas there was no difference in the OHWS group ($p \leq 0.05$). The forage type did not affect the microbial composition at the genus level ($p \leq 0.05$). Regardless of the form of GAA added, the population of Succiniclasticum decreased ($$p \leq 0.049$$) compared to the control. Furthermore, dietary CGAA in the OH group presented higher ($p \leq 0.05$) Prevotella abundance compared with dietary CGAA and the control. The relative abundances of norank_f_norank_o_Clostridia_UCG-014 decreased ($p \leq 0.05$) with UGAA addition in the OH group compared to the control. Interaction between forage type and GAA addition affected the abundance of Prevotella ($$p \leq 0.042$$), Ruminococcus ($$p \leq 0.027$$), and Succiniclasticum ($$p \leq 0.006$$) at stage 2; the relative abundances of Prevotella decreased with UGAA and CGAA addition ($p \leq 0.05$); the relative abundances of Succiniclasticum decreased and NK3A20 increased with UGAA addition compared to the control in the OH group ($p \leq 0.05$), whereas there was no difference in the OHWS group ($p \leq 0.05$). However, the abundances of Ruminococcus decreased with the addition of UGAA compared to the control and NK4A214_group increased with the UGAA and CGAA addition in OHWS group ($p \leq 0.05$), whereas there was no difference in the OH group ($p \leq 0.05$). The lambs in the OH group in comparison with the OHWS group presented lower NK4A214_group ($$p \leq 0.008$$) and NK3A20 ($$p \leq 0.038$$). Both forms of GAA resulted in greater NK4A214_group abundances ($$p \leq 0.040$$). The other genera did not alter in response to either forage type or GAA addition ($p \leq 0.05$). At stage 1, 13 bacterial taxa were identified by LEfSe as significantly enriched in the rumen, comprising 10 (f_Anaerovoracaceae, o_Peptostreptococcales-Tissierellales, g_unclassified_f_Prevotellaceae, f_Staphylococcaceae, g_Staphylococcus, o_Staphylococcales, g_Allorhizobium-Neorhizobium-Pararhizobium-Rhizobium, g_Eubacterium_saphenum_group, f_Enterococcaceae, and g_Enterococcus) in the control group with the OH diet, and 1 (g_Eubacterium_ventriosum_group) and 2 (g_norank_f_p-251-o5, f_p-251-o5) in the control and UGAA addition with the OHWS diet, respectively (Figure 1A). At stage 2, 29 bacterial taxa were identified by LEfSe as significantly enriched in the rumen, comprising 7 (f_Streptococcaceae, o_Lactobacillales, g_Streptococcus, g_Kandleria, g_UCG-001, g_unclassified_f_Prevotellaceae, and g_Lachnospiraceae_AC2044_group) and 4 (g_norank_f_Clostridium_methylpentosum_group, f_Clostridium_methylpentosum_group, o_Rhizobiales, and g_norank_f_Erysipelotrichaceae) in the control and with UGAA addition in the OH group, respectively, and 12 (o_Acidaminococcales, g_Succiniclasticum, f_Acidaminococcaceae, g_Lachnospiraceae_NK3A20_group, g_norank_f_Muribaculaceae, f_Muribaculaceae, g_norank_f_norank_o_Bradymonadales, c_Desulfuromonadia, o_Bradymonadales, f_norank_o_Bradymonadales, g_Erysipelotrichaceae_UCG008, g_Oribacterium) and 6 ((g_NK4A214, g_Ruminococcus_gauvreauii_group, g_Prevotellaceae_NK3B31_group, g_Moryella, g_Coprococcus, g_Lachnoclostridium) in the control and with CGAA addition in the OHWS group, respectively (Figure 1B). ## 3.4. Rumen Antioxidant Capacity As shown in Table 5, at stage 1, the Forage × GAA interaction effect was exerted on the SOD, CAT, and GSH-Px activities but not on the levels of T-AOC, GSH, and MDA. For the OH diet, the highest T-AOC content and SOD and CAT activities were observed with the CGAA addition, whereas for the OHWS diet, UGAA addition resulted in the maximum amount of T-AOC content and SOD and CAT activities. The forage type did not affect antioxidant capacity. GAA (UGAA and CGAA) supplementation in the rumen increased the T-AOC ($p \leq 0.001$), SOD ($p \leq 0.001$), CAT ($p \leq 0.001$), GSH-Px ($p \leq 0.001$), and GSH ($p \leq 0.001$), whereas the level of MDA ($p \leq 0.001$) decreased with the addition of GAA. At stage 2, an interaction effect was observed in the T-AOC, MDA levels and SOD, and GSH-Px activities. CGAA addition had the highest SOD activity, GSH-Px activity, and T-AOC content, and the lowest MDA content in the OH diet, but in the OHWS diet, UGAA addition achieved similar results. The type of forage did not affect the antioxidant capacity. Moreover, the T-AOC ($p \leq 0.001$), SOD ($p \leq 0.001$), CAT ($$p \leq 0.048$$), GSH-Px ($p \leq 0.001$), and GSH ($$p \leq 0.009$$) increased but the level of MDA ($p \leq 0.001$) decreased with GAA addition. ## 4. Discussion Oxidative stress is a dysregulation between the production of reactive oxygen species and the endogenous antioxidant defense mechanisms [28]. For ruminants, high-concentrate diets have been widely associated with oxidative stress by increasing the metabolic rate [29]. In general, SOD, CAT, and GSH-Px activity, T-AOC, GSH, and MDA contents in serum are usually measured to reflect whether or not oxidative stress has occurred in the body. However, it is unknown whether oxidative stress could be reflected by measuring the aforementioned indices in rumen liquid. In a previous study, both CAT and GPx4 activities were elevated and the MDA content was decreased in serum when dietary GAA addition was applied in growing lambs [5]. In a subsequent study with Holstein dairy cows, dietary GAA addition was found to promote the growth of rumen microorganisms [30]. However, it is not clear whether the above responses to GAA could be associated with the rumen fermentation characteristics and microbial stability of the environment inside the rumen and how their relationship with antioxidant capacity is directly measured in the rumen fluids. ## 4.1. Rumen Fermentation Total VFA concentration, pH value, NH3-N, and MCP levels are the main internal environmental indicators of rumen fermentation [8]. Among them, total VFA production was believed to account for over $70\%$ of the energy requirement [31]. In one of our previous studies with growing lambs, increasing the foxtail millet silage replacement of peanut vine hay in rations exhibited a lower pH value and greater total VFA production [32]. In the present study, regardless of what form of GAA was applied, the lambs in the OHWS group in comparison with those in the OH group presented greater total VFA production during the last 60 days of the feeding stage, suggesting that WS in comparison with OH might exhibit rumen digestibility. In one of our previous studies, the application of GAA was confirmed to improve growth performance and apparent total tract nutrient digestibility in lamb feeding practice [33]. In the present study, GAA feeding resulted in higher total VFA assessed at two stages. These results could be partly explained by the stimulation of nutrient degradation with dietary GAA supplementation in the rumen. Similarly, the addition of GAA has also been shown to increase rumen total VFA production in Holstein dairy cows [30]. The decrease in ruminal pH was commonly believed to be associated with an increase in the total VFA concentration. In the present study, the relatively low ruminal pH of 6.74 was observed for UGAA added to the OHWS diet group, and this pH was not low enough to have a negative impact on ruminal microorganisms [34]. Previous studies have reported that addition of GAA (0.6 or 0.9 g/kg basal diet) increased molar propionate proportion and A:P in Holstein dairy cows [30]. This phenomenon was not observed in the present study, and was probably a result of the differences in lamb species, ages, and basal diets. Researchers have also indicated that GAA can be used by microbes as an N source to synthesize their proteins [4], as observed in the present study in two stages. ## 4.2. Ruminal GAA, Creatine, ATP, and Related Metabolic Enzymes In the present study, rumen GAA, creatine, and ATP levels, as well as GAMT and AGAT activities, did not vary with forages. This was probably due to the same ratio of concentrate to forage in both TMRs, as replacing $36\%$ OH with WS had little effect on indicators related to energy metabolism in the rumen. A previous study noted that GAA in the diet was absorbed by the gut through the portal blood into the liver for creatine synthesis [35], and GAMT and AGAT were key enzymes in this process [36]. Decreased rumen AGAT activity was observed with the addition of GAA at stage 1 in the present study. This was consistent with the finding that AGAT can be feedback-inhibited by GAA [37]. Most metabolic studies on GAA have focused on the liver, blood, urea, and small intestinal segment mucosa [8,36,38]. The addition of GAA increased the rumen GAA content at both stages 1 and 2, confirming that GAA was degraded in the rumen and significantly correlated with the degradation rate (UGAA, $50.9\%$; CGAA, $15.8\%$). Furthermore, ATP level improved with GAA addition compared to the control. It also supports the theory that GAA can provide energy to microbes [4]. Interestingly, we did not observe a difference in ATP content between the two forms of GAA, suggesting that not all of the GAA degradation was used to provide energy, and would even be a waste, thus necessitating coating. ## 4.3. Ruminal Microbiota The alpha diversity analysis of lamb rumen flora in our experiment showed that the coverage of each group was higher than $99\%$ in two stages, indicating that the sequencing results truly reflected the species and structural diversity of the lamb rumen bacterial community. Forages did not affect alpha diversity in stage 1, which is consistent with previous research [39]. However, compared with the OHWS group, the sobs, ace, and chao index decreased with the OH diet in stage 2. These results indicate that rumen microbial richness varies with diet and host [40]. In the present study, no difference emerged in the alpha diversity with the addition of GAA, indicating that GAA did not affect the richness or evenness of lamb rumen microorganisms. In this study, at the phylum level, Firmicutes and Bacteroidota were the dominant phyla, which are known to be involved in carbohydrate and protein degradation [33]. The interaction between forage type and GAA addition was found to affect the abundance of Bacteroidota. We suspected that suitable GAA (CGAA) could promote Bacteroidota enrichment in the rumen, and a high concentration (UGAA) would affect the abundance of Bacteroidota. At stage 2, compared with the OHWS diet, the lambs fed the OH diet had a lower relative abundance of Firmicutes, which represented the predominant bacterium within the rumen, mainly comprising diverse fibrolytic and cellulolytic bacterial genera [41]. Similar to stage 1, dietary UGAA in the OH group presented a lower relative abundance of Bacteroidota, compared with the control group, which contributed to the release of energy from dietary fiber and starch [42]. At the genus level, Prevotella participates in the hydrolysis of proteins and the absorption of peptides in the rumen [43] and propionate production [44]. Prevotella enrichment helps to increase the antioxidant capacity in rumen fluid [45]. In the present study, the CGAA addition exhibited a higher relative abundance of Prevotella than the UGAA addition and the control with the OH diet. This corresponds to the highest propionate proportion. This also confirms that the addition of GAA can improve antioxidant capacity in rumen fluid. Furthermore, the addition of UGAA decreased the abundance of NK4A214_group, Succiniclasticum, and Clostridia_UCG-014 in the OH diet at stage 1. This suggests that the degradation of GAA in the rumen may lead to partial microbiota disorders. The same phenomenon was manifested in Ruminococcus and Succiniclasticum at stage 2. Higher relative abundances of NK4A214_group and NK3A20 were observed with the OHWS diet at stage 2. This may be because whole wheat silage contains higher starch than oaten hay. Zhang et al. [ 46] also found that NK4A214_group and NK3A20 enhanced starch and sucrose metabolism. Furthermore, NK4A214_group and NK3A20 are butyric acid-producing bacterium [47,48], thus explaining the corresponding increase in butyric acid with the addition of GAA. Apart from norank_f_p-251-o5 and f_p-251-o5 in the UGAA with the OHWS diet, no differential bacteria were identified in stage 1 by LEfSe analysis. However, at stage 2, the OH diet with the UGAA group was enriched with norank_f_Clostridium_methylpentosum_group, f_Clostridium_methylpentosum_group, o_Rhizobiales, and norank_f_Erysipelotrichaceae. Other researchers have reported that Clostridium_methylpentosum is a ring-shaped intestinal bacterium that ferments only methylpentoses and pentoses [49]. The norank_f_Erysipelotrichaceae was reported to relate to metabolic disorder and inflammation-related gastrointestinal diseases [50]. This indicates that the addition of CGAA may lead to disorder of the internal environment and even inflammatory reaction in rumen. In the present study, the NK4A214_group, Ruminococcus_gauvreauii_group, Prevotellaceae_NK3B31_group, Moryella, Coprococcus, and Lachnoclostridium were enriched in the group fed with the OHWS and CGAA diet. Previous research has shown that Moryella, Coprococcus, and Lachnoclostridium are butyric acid-producing bacteria [51,52]. Previous studies have shown that butyrate treatment of the colon increases glutathione content [53]. The increase in butyric acid-producing bacteria may also provide indirect evidence that GAA improves antioxidant capacity in this study. ## 4.4. Rumen Antioxidant Capacity A high concentrate diet in ruminants results in a massive release of bacterial endotoxins, which cause rumen epithelial cells to generate a certain amount of reactive oxygen species and lead to oxidative stress [54]. This phenomenon causes oxidative damage and decreases the activities of GSH-Px, CAT, and SOD [55]. SOD, CAT, and GSH-Px are intracellular antioxidant enzymes involved in the enzymatic antioxidant system against oxidative stress [56]. The SOD involved in the superoxide anion free radical (O2−) scavenging process in cells [57]. MDA is one of the meta-stable end products of lipid peroxidation [58]. Consistent with our hypothesis, the forage type did not affect antioxidant capacity due to the consistent forage-to-concentrate ratio. GAA has been reported to have a direct [59] or indirect antioxidant effect [60,61]. In our study, GAA addition increased SOD, CAT, and GSH-Px activities, as well as levels of T-AOC and GSH, but decreased MDA level at both stages 1 and 2, suggesting that GAA enhanced the antioxidant capacity in rumen. There are limited studies on the antioxidant properties of GAA in rumen fluid. We speculate that there are three possible reasons for this phenomenon. The content of creatine in rumen fluid increases numerically with the addition of GAA, as creatine has been proven to have the ability to remove O2- [60]. In addition, dietary GAA supplementation can save arginine, and L-Arginine alleviates oxidative stress by modulation of intestinal microbiota in intrauterine growth-retarded suckling lambs [61]. Another possible reason is that GAA increases the proportion of butyric acid in the rumen fluid, and butyrate improves the level of oxidative stress in intestinal mucosal cells [62]. It is interesting to find that OH diets had higher antioxidant capacity with CGAA addition, while OHWS diets had more significant antioxidant capacity with the addition of UGAA. This suggests that, in practice, we can choose the appropriate form of GAA according to the different types of forage. Examining these findings together, the authors of the present study speculated that GAA supplementation could improve antioxidant capacity and rumen fermentation via providing energy for rumen microorganisms since partial GAA was metabolized. However, GAA degradation would also cause rumen dysbacteriosis as noted by the ruminal microbiota analysis. The results obtained in the present study suggested that the antioxidant capacity of GAA could be reflected by measuring the relevant indicators in rumen fluids samples; the increase in antioxidant capacity may be related to the enrichment of butyric acid-producing bacteria. ## 5. Conclusions Our findings demonstrated that dietary GAA exhibited higher antioxidant capacity, total VFA, and microbial protein production in lambs fed with different forage types. The application of GAA as a feed additive has a bright application prospect of reducing oxidative stress and providing energy to support rumen fermentation in rapid-growing lambs. However, considering the rumen microbial stability, coated GAA is necessary to avoid rumen dysbacteriosis in feeding practice. ## References 1. Ostojic S.M.. Tackling guanidinoacetic acid for advanced cellular bioenergetics. 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--- title: Chemerin and Chemokine-like Receptor 1 Expression Are Associated with Hepatocellular Carcinoma Progression in European Patients authors: - Florian Weber - Kirsten Utpatel - Katja Evert - Oliver Treeck - Christa Buechler journal: Biomedicines year: 2023 pmcid: PMC10044805 doi: 10.3390/biomedicines11030737 license: CC BY 4.0 --- # Chemerin and Chemokine-like Receptor 1 Expression Are Associated with Hepatocellular Carcinoma Progression in European Patients ## Abstract The chemoattractant protein chemerin is protective in experimental hepatocellular carcinoma (HCC), and high expression in HCC tissues of Asian patients was related to a favorable prognosis. Studies from Asia found reduced expression of chemerin in HCC compared to para-tumor tissues while our previous analysis observed the opposite. Aim of this study was to correlate chemerin expression in HCC tissues with disease severity of European patients Hepatocyte chemerin protein expression was assessed by immunohistochemistry in HCC tissue of 383 patients, and was low in $24\%$, moderate in $49\%$ and high in $27\%$. High chemerin protein in the HCC tissues was related to the T stage, vessel invasion, histologic grade, Union for International Cancer Control (UICC) stage and tumor size. Chemokine-like receptor 1 (CMKLR1) is a functional chemerin receptor. CMKLR1 protein in hepatocytes was low expressed in HCC tissues of $36\%$, moderate in tissues of $32\%$ and high in $32\%$ of the HCCs. Tumor CMKLR1 was associated with the T stage, vessel invasion, histologic grade and UICC stage. Notably, sex-specific analysis revealed that associations of chemerin and CMKLR1 expression with HCC progression were significant in males but not in females. The tumor chemerin and CMKLR1 protein expression were not related to steatosis, inflammation and fibrosis grades. In summary, chemerin as well as CMKLR1 protein were related to disease severity of European HCC patients, and this was significant in males. This observation is in contrast to Asian patients where higher chemerin in the tumors was protective. Current analysis provides evidence for ethnicity and sex-related differences of tumor expressed chemerin and HCC severity. ## 1. Introduction Chemerin is a chemoattractant protein and is mainly produced by adipocytes and hepatocytes [1]. Serum chemerin protein is increased in obesity in accordance with its expression in fat tissues. Chemerin was initially identified to function as a chemoattractant for cells expressing chemokine-like receptor 1 (CMKLR1). Later on, a function of chemerin was demonstrated for the insulin response, adipogenesis, and blood pressure control [1,2,3]. There is increasing evidence that locally produced chemerin also has a role in different cancers. Most of the studies having reported an anti-tumorigenic function of chemerin also identified a downregulation of the chemerin protein in the tumors [4,5]. The first study showing anti-tumor properties of chemerin was in melanoma. Here, the chemerin-induced recruitment of natural killer cells was protective [6]. In experimental hepatocellular carcinoma (HCC), chemerin reduced tumor growth and metastasis by affecting the T-cell function. Chemerin did not change the proliferation of hepatocyte cell lines [7,8,9]. Chemerin, moreover, increased the expression and phosphatase activity of phosphatase and tensin homolog (PTEN) and thereby suppressed HCC metastasis [8]. Accordingly, chemerin protein was found to be low in HCC tissues. A study from China published in 2011 showed that chemerin protein analyzed by immunohistochemistry was reduced in HCC tissues for about $60\%$ of the patients [10]. Chemerin tumor protein positively correlated with immune cell infiltration. Patients with high intratumoral chemerin had a better prognosis [10]. The association of higher chemerin tumor with less disease severity and more favorable prognosis was confirmed by a second study that was also conducted in China. Notably, chemerin protein was also detected by immunoblot and was again reduced in the HCC tissues [9]. This was also described by a third study from China [8]. In contradiction with these findings from China, our previous analysis of chemerin protein levels by immunoblot in HCC tissues obtained from European patients detected higher proteins in HCC than the para-tumor tissues [8,9,10,11]. HCC-related upregulation of chemerin was influenced by disease etiology, and was observed in non-alcoholic fatty liver disease (NAFLD) and hepatitis B virus (HBV) infection, but not in hepatitis C virus (HCV)-positive patients. Tumor-localized chemerin protein was not related to the T stage in this small cohort of European patients [11]. The upregulation of chemerin in the HCC tissues of Europeans suggests a tumor-promoting function, but associations of hepatocyte-expressed chemerin and HCC progression in this population have not been studied so far. CMKLR1 is expressed by almost all the cell types analyzed [4,12,13,14,15]. G protein-coupled receptor 1 (GPR1) is the second chemerin receptor, but it was less well-studied [13,16]. CMKLR1 protein is expressed by primary human hepatocytes and various hepatocellular cell lines [8,17]. CMKLR1 protein was found to be reduced in HCC tissues in comparison to the tumor adjacent tissues of European patients with NAFLD. In patients with viral hepatitis, the tumor and para-tumor expression of CMKLR1 protein was similar [11]. A lower expression of CMKLR1 in the tumor tissues of NAFLD patients may prevent chemerin from exerting its anti-tumor effects. It has to be noted that chemerin is released by the cells as inactive prochemerin and cleavage of the carboxyl terminus by serine proteases results in activation [1]. An analysis of chemerin protein levels by commercial antibodies cannot discriminate the different chemerin isoforms, and it is unknown whether chemerin protein in HCC tissues is biologically active. HCC protective effects of chemerin are, however, attributed to the biologic active chemerin isoforms [1,4] Total chemerin protein levels in the tumors are, however, of diagnostic and prognostic value in HCC, at least in patients from China [8,9,10]. The current analysis sought to examine the relationships between hepatocyte-expressed chemerin in HCC and disease severity in a large cohort of patients from Europe. CMKLR1 is a functional chemerin receptor, and its protein expression in hepatocytes was analyzed in parallel. ## 2.1. Patients HCC tissues of 383 patients (315 males and 68 females) were obtained from the years 2000 to 2021. The patients are from the eastern part of Bavaria and about $2.5\%$ of the inhabitants of Germany are from Asian countries [18]. Mean age of the patients was 64.32 ± 11.48 years. Seven tissue microarrays (TMAs), in which up to 60 separate tissue cores per TMA were assembled, were prepared using standard techniques already described [19]. Experienced pathologists evaluated hematoxylin and eosin stained sections of HCC tissues and selected representative areas. One core was used from each of the tumors and included in the final TMA with about 60 specimens in each paraffin block. The TNM classification system was used to define pathological primary tumor extent (pT stage) and disease stage according to the Union for International Cancer Control (UICC) staging system. WHO guidelines were applied for histological tumor grading [20]. This was a retrospective study, which was conducted in accordance with the Declaration of Helsinki. Approval for this study was obtained by the Ethics Committee of the University Hospital of Regensburg. The Ethics Committee confirmed that an informed consent was not needed for this retrospective study (protocol code 22-2788-101; date of approval: 23 February 2022). ## 2.2. Immunohistochemistry The IHC-plusTM RARRES2/chemerin antibody (order number: LS-B13333) and the IHC-plusTM CHEMR23/CMKLR1 antibody (order number: LS-B12924) were obtained from Biozol (Eching, Germany) and diluted 1:100 fold for analysis. The IHC-plus™ antibodies are tested in immunohistochemistry against human formalin-fixed paraffin-embedded tissues and have an excellent specificity and sensitivity for detecting the target protein [21]. The staining protocol was established as is conducted for all antibodies in the Institute for Pathology by trying out different dilutions of the antibody and different pre-treatments on a control TMA with the most important normal human tissues until the optimal staining protocol with a balance between staining intensity of the desired protein and minimal background staining is found. For immunohistochemistry, 4 μm thick sections of the TMA blocks were deparaffinized, treated at 120 °C for 5 min, and incubated with Tris-EDTA buffer (pH 9). Endogenous peroxidase was blocked with peroxidase blocking solution (Dako, Glostrup, Denmark), and antibody incubation was performed. All antibodies were incubated for 30 min at room temperature. Staining was performed using the Dako EnVision™+ Detection System, Peroxidase/DAB+, Rabbit/Mouse (Dako, Glostrup, Denmark). After these incubations, the slides were counterstained with hematoxylin. Immunohistochemical staining was independently assessed by two expert pathologists (FW und KE); divergent results were discussed and a consensual score was reached. Both antibodies did not stain the tissues when the primary antibodies were not added [22]. Both antibodies showed unspecific nuclear staining of lymphocytes [22], and therefore, only cytoplasmic and membranous staining of hepatocytes was considered for the scoring. Nuclear staining was not included in the assessment. Low staining intensity was defined as no or barely visible membranous and/or cytoplasmic staining; for high staining intensity, either heterogeneous or patchy membranous and/or cytoplasmic staining comparable to that of bile ducts in normal liver tissue was considered, and medium staining intensity was defined as any staining intensity between the other two groups. A three-tiered scoring system was used for chemerin, with a score of 1 denoting low cytoplasmic and/or membranous staining, 2 denoting moderate staining, and 3 denoting high staining. A three-tiered score system was also used to define the level of CMKLR1 protein expression, with scores 1, 2, and 3 designating low, moderate, and high cytoplasmic and/or membranous staining, respectively. Immunohistochemical data traditionally are semi-quantified by pathologist visual scoring of the staining intensity [23]. Accordingly, Li et al. scored the staining intensity of chemerin in HCCs on a scale of 0–3 (0: no staining, 1: weak intensity, 2: moderate intensity, and 3: strongest intensity) [8] and Lin et al. scored the chemerin staining as 0 to 3+ [10]. A separate study counted the staining-positive cells and measured chemerin density by Image-Pro Plus v6.2 software [9]. Whether nuclear staining has not been observed or was not quantified is not discussed in these manuscripts [8,9,10]. Here, cytoplasmic and membrane staining but not nuclear staining of hepatocytes was evaluated, which is not possible with automated measurements. The control group consisted of non-neoplastic liver tissue from patients suffering from HCC. ## 2.3. Histological Scores Histological assessment of tumoral steatosis, grade of inflammation, and liver fibrosis was performed by expert liver pathologists (K.U. and K.E.). For grade of tumoral steatosis, the percentage of tumoral fat vacuoles in relation to total tumor volume was stated. Steatosis grade ranged from $0\%$ to $80\%$. Intratumoral inflammation was graded on a 4-tiered scale from 0–3, where 0 refers to no inflammation and 3 describes severe inflammation. Fibrosis of non-tumoral liver parenchyma in the surgical specimen was graded according the Ishak fibrosis score on a 7-tiered scale from 0–6, where 0 stands for no fibrosis and 6 is equal to complete cirrhosis [24]. ## 2.4. Statistics Data are given as mean value ± standard deviation (SD). Statistical tests used were Mann–Whitney U Test (to test for differences between two independent groups), and Kruskal–Wallis Test (to test for differences between three independent groups) (SPSS Statistics 26.0 program). The p-values were corrected for multiple comparisons by Bonferroni. A p-value < 0.05 was considered significant. ## 3.1. Chemerin Expression in HCC Tissues Chemerin protein was analyzed by immunohistochemistry and was found to be expressed low in $24\%$, moderate in $49\%$, and high in $27\%$ of the 383 HCC tissues analyzed (Figure 1). Stratification of the patients into low, moderate, and high chemerin-expressing tumors showed that the T stage was significantly higher in the latter group in comparison to the low and moderate chemerin group. Blood vessel invasion was increased in HCCs with high compared to low chemerin expression. Histologic grading and tumor size were significantly higher in patients with moderate or high chemerin tumor protein compared to those with low expression. The Union for International Cancer Control (UICC) stage was higher in patients with moderate and high chemerin expression compared to patients with low expression levels. Lymph node invasion and age did not differ between the three groups (Table 1). Notably, chemerin protein was not related to steatosis score, inflammation, or fibrosis grade (Table 1). CMKLR1 expression increased in parallel with chemerin (Table 1). ## 3.2. Sex-Specific Differences of Chemerin Expression in HCC Tissues The chemerin staining scores of the tumors of females and males did not differ ($$p \leq 0.156$$). The T stage ($$p \leq 0.961$$), lymph node invasion ($$p \leq 0.919$$), and vessel invasion ($$p \leq 0.975$$) were similar between sexes. Tumors of females were 8.51 ± 6.09 cm and of males 5.95 ± 4.41 cm, and so, they were larger in females ($$p \leq 0.001$$). A sex-specific analysis showed that the chemerin protein in the HCC tissues of females was not related to the T stage, lymph node invasion, vessel invasion, grading, tumor size, or UICC score (Table 2). Chemerin protein in the HCC tissues of the female patients was not related to the steatosis score, inflammation, or fibrosis grade (Table 2). The age of the patients did not differ between HCCs with low, moderate, and high chemerin (Table 2). As was shown for the whole cohort, CMKLR1 protein increased in parallel with chemerin (Table 2). In the male patients the T stage was significantly higher in the high chemerin group in comparison to the low and moderate chemerin group. Blood vessel invasion and tumor size were increased in HCCs with a high compared to low chemerin expression. Histologic grading was significantly higher in patients with moderate or high chemerin tumor protein compared to HCCs with low expression. The UICC score was significantly increased in the patients with high chemerin compared to those with moderate and low expression in the tumors. Lymph node invasion did not differ between the three groups (Table 3). Notably, chemerin protein in the HCC tissues of males was not related to age, steatosis score, inflammation, or fibrosis grade (Table 3). CMKLR1 protein scores increased in parallel with the chemerin scores (Table 3). ## 3.3. CMKLR1 Expression in HCC Tissues Notably, hepatocyte-expressed CMKLR1 was mostly found in the cytoplasm. The CMKLR1 protein of 382 patients could be judged and CMKLR1 was lowly expressed in $36\%$, moderately expressed in $32\%$ and highly expressed in $32\%$ of the HCCs (Figure 2). Comparison of the patients with low, moderate, and high tumor CMKLR1 proteins revealed that the T stage and blood vessel invasion were significantly increased in the high versus the low CMKLR1 group. Histologic grading was significantly increased in the high CMKLR1 group in comparison to the low and to the moderate group. The UICC stage was highest in the patients with high CMKLR1 in comparison to the two other groups (Table 4). CMKLR1 protein was not related to tumor size, steatosis score, inflammation, or fibrosis grade. Age was not a confounding factor (Table 4). Chemerin protein expression increased with higher CMKLR1 protein (Table 4). ## 3.4. Sex-Specific Differences of CMKLR1 Expression in HCC Tissues The CMKLR1 protein expression scores did not differ between males and females ($$p \leq 0.077$$). Sex-specific analysis showed that the CMKLR1 protein in the HCC tissues of females was not related to the T stage, lymph node invasion, vessel invasion, grading, tumor size, or UICC score (Table 5). The CMKLR1 protein in the HCC tissues of the female patients was not related to age, steatosis score, inflammation, or fibrosis grade (Table 5). The chemerin protein was higher in the high CMKLR1 compared to the low CMKLR1 group (Table 5). Comparison of the male patients with low, moderate, and high tumor CMKLR1 protein revealed that the T stage, lymph node invasion, and vessel invasion were significantly increased in the high versus the low CMKLR1 group. Histologic grading and the UICC stage were significantly increased in the high CMKLR1 group in comparison to the low and to the moderate group (Table 6). The CMKLR1 protein was not related to tumor size, steatosis grade, fibrosis grade, or age (Table 6). Patients with high CMKLR1 had more inflammation than those with moderate CMKLR1 (Table 6). Chemerin protein expression was elevated in the moderate and high CMKLR1 groups in comparison to the low CMKLR1 group (Table 6). Disease etiology was documented for 63 patients, and 19 patients had HBV and 44 patients had HCV. However, chemerin ($$p \leq 0.188$$) and CMKLR1 protein in the tumors ($$p \leq 0.600$$) did not differ between the two groups. ## 4. Discussion The current study showed that chemerin and CMKLR1 protein expression in the tumors of European HCC patients are associated with disease progression. This is in contrast to Chinese patients, where the tumor chemerin protein levels were found to decline with disease severity [8,9,10]. Sex-specific analysis, which has not been performed in the Asian studies, suggests that strong associations of chemerin and CMKLR1 protein with HCC stages exist in males but not females. As far as we are aware, there have never been reports of opposing associations between the expression of a protein and the severity of HCC in Asian and European patients. The three studies from Asia consistently described that chemerin is reduced in HCC tissues in comparison to the peritumoral tissue [8,9,10]. In contrast, it was shown in our previous analysis that chemerin protein expression is increased in the HCC tissues of European patients. In comparison to tumor-adjacent tissues, chemerin protein was found to be induced in the tumors of European HCC patients with NAFLD and HBV, but not in HCV-related HCC [11]. Associations of the tumor chemerin expression with the T stages were not identified in these relatively small cohorts [11]. In Asian patients, significant negative correlations of chemerin with tumor size and histological grade have been detected [10]. A second analysis could, however, not observe the associations of chemerin protein with the tumor size, tumor character, such as single nodule or multiple nodule, or TNM stage [8]. In our European cohort, chemerin protein in the HCC tissues was associated with tumor stage, grading, tumor size, and vessel invasion, and thus, UICC staging. These relations could be verified in the male patients. Whether equivalent associations exist in females requires further study. In the 68 females of our cohort, no such relations were observed. There are much fewer females than males in our cohort in accordance with the two to four fold higher HCC incidence of males [25] and the study group may be too small to show significance. In contrast to what has been published [25], females had larger tumors than males, and this is specific to our study group. In Asian patients, macrophage numbers were higher in HCC tissues with increased chemerin expression [8]. The dendritic cell and natural killer cell counts were positively related to chemerin protein [10]. Associations of tumoral inflammation and chemerin protein expression were not identified in the current cohort. Chemerin is a chemoattractant for macrophages, dendritic cells, and natural killer cells. To function as an immune cell attractant, chemerin has to be C-terminally processed [1]. Positive correlations of chemerin protein with macrophage, dendritic cell, and natural killer cell numbers suggest that these active isoforms are available in the HCC tissues of Chinese patients. In the HCC tissues of European patients, chemerin protein did not correlate with the inflammation score and thus seems to be inactive. Commercial antibodies cannot discriminate between the different chemerin isoforms, and which of the chemerin variants are abundant in HCC tissues has not been evaluated so far. Currently, there is no explanation for the discordant findings between European and Asian patients. Genetic mutations and signatures vary between patients with different disease etiologies, and ethnic factors also play a role here [26]. Thus, Sal-like protein 4 (SALL4) was found re-expressed in tissues of about $50\%$ of Chinese HCC patients, and serum levels were increased. In these patients, serum SALL4 levels were related to tumor recurrence and survival [27]. SALL4 upregulation was, however, very rare in Western HCC patients [28]. The underlying liver disease etiologies of HCC differ between Western and Asian countries [26]. Chronic viral infections and NAFLD are risk factors for HCC, and the epidemiology of HCC varies with geographic location. In Asia, nearly $80\%$ of liver cancers are caused by chronic HBV infection, $3\%$ by HCV, and $1\%$ by NAFLD [29]. In Germany, $20\%$ of HCCs are related to chronic HCV, $25\%$ to chronic HBV, and $22\%$ to NAFLD [30]. A detailed comparison of hepatic chemerin expression between patients with different causes for chronic liver injury has not been performed so far. Interestingly, HBV infection was found to lower the chemerin protein in HCC tissues of Asian patients and had the opposite effect in European patients [8,11]. This shows a contrary regulation of chemerin protein in tumor tissues of HBV-infected Chinese and European patients. Differences in disease etiology of Chinese and European HCC patients alone can, therefore, not explain the opposite association of HCC-expressed chemerin protein with disease severity. Chemerin protein in the HCC tissues of Chinese patients was not changed with sex and age [10], and associations of the protein levels with sex and age were not identified in the patient samples studied here. Interestingly, associations of chemerin expression in the HCC tissues with disease progression seem to be less pronounced in females, and no significant relations were found in the cohort studied here. In the Chinese cohort, the chemerin tumor protein was not related to cirrhosis [10], which is in accordance with the current findings in the European patients. Moreover, tumoral steatosis was not associated with altered chemerin levels in the Western patients. Liver steatosis is caused by HCV infection and alcohol abuse and is commonly observed in obesity [31,32]. Data regarding chemerin expression in the steatotic human liver are not concordant [1], and yet to be identified factors besides disease etiology seem to have a role herein. Studies from Europe and Japan have shown that serum chemerin is low in patients with liver cirrhosis and that it correlates with hepatic dysfunction [33,34,35]. Although this suggests reduced hepatic chemerin protein expression in the cirrhotic liver, chemerin protein expression in the HCC tissues was not related to fibrosis scores. Liver dysfunction thus may impair chemerin release from the liver and/or fat tissues into the blood and/or enhance its elimination from the body. Considering the close association of serum chemerin with hepatic dysfunction, it is not unexpected that studies measuring circulating chemerin in HCC have revealed discordant results. In the European patients, serum chemerin did not differ between HCC and controls [36]. A study from Japan could not find significant correlations of serum chemerin levels with HCC prognosis [35]. A study from China described a nearly 20-fold lower chemerin concentration in the blood of HCC patients compared with healthy subjects [9]. It is well-known that serum chemerin declines in patients with liver cirrhosis, and HCC occurs most often in patients with cirrhosis [33,34,37]. Future research has to compare serum chemerin levels between patients with and without HCC stratified for liver disease severity. Until now, studies on the hepatic expression of CMKLR1 have been sparse. CMKLR1 protein was found to be positively associated with the T stage, grading, vessel invasion and, UICC stage. Again, these associations were significant in males but not in females. The CMKLR1 protein did not change with age and sex, and moreover, it was not related to tumoral steatosis, inflammation, and liver fibrosis. In males, high CMKLR1 protein was related to more inflammation. This difference existed between HCCs with moderate and high but not between low and high CMKLR1 expression. The corresponding p value was rather high, arguing against a strong association of CMKLR1 and inflammation in the HCC tissues of males. In hepatocytes, CMKLR1 was mostly localized in the cytoplasm. Notably, this also applies to PTEN [38], which was shown to interact with CMKLR1 [8]. Membrane–cytoplasmic staining of CMKLR1 was described in mice livers [39]. The CMKLR1 antibody distributed by a different company showed cytoplasmic and nuclear staining in kidney carcinoma and stomach carcinoma [40]. Nuclear staining was also observed in the current experiments but was regarded as unspecific and not quantified. It is possible that CMKLR1 localizes to the nucleus in different tumor cells, but further experiments have to prove this assumption. Nuclear staining of chemerin is considered unspecific, and there are no studies that have shown that chemerin translocates to the nucleus as far as we know. The limitations of the current study are that survival was not documented and that the disease etiology of most patients was unknown. Although positive associations of chemerin and CMKLR1 protein levels with markers of HCC severity suggest that higher levels of these proteins are linked to a worse prognosis, this has to be experimentally proven. Further limitations are the descriptive nature of this retrospective study, that only immunohistochemistry was used, and that chemerin as well as CMKLR1 expression was semi-quantified by a pathologist visual scoring of the staining intensity. The ethnicity of the patients with HCC was not recorded. The patients enrolled in the study are from the eastern part of Bavaria. About $2.5\%$ of the German population are of Asian descent, and data of these patients could not be removed [18]. The studies on Asian HCC patients published so far suggested a protective role of chemerin in HCC [8,9], but the issue of whether chemerin in human HCC tissues is biologically active has not been evaluated. Future research has to consider chemerin activity and to compare the chemerin isoform composition of HCC tissues obtained from Western and Asian patients. Furthermore, the molecular mechanisms underlying the conflictive results of studies on Asian and European patients regarding the association of chemerin with HCC progression remain to be investigated. ## 5. Conclusions High chemerin and CMKLR1 proteins in European patients with HCC are related to adverse clinical parameters such as the T stage, vessel invasion, grading, and UICC stage, indicating a tumor-promoting effect of this receptor and its ligand. In Asian patients, the high chemerin expression in tumors was protective. Preliminary analysis, moreover, indicates that the association of chemerin and CMKLR1 with HCC progression was less evident in female patients. The current findings suggest that ethnicity and sex play a role in HCC pathophysiology and have to be considered in the development of diagnostic and therapeutic approaches. ## References 1. Buechler C., Feder S., Haberl E.M., Aslanidis C.. Chemerin Isoforms and Activity in Obesity. Int. J. Mol. Sci. (2019.0) 20. 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--- title: Coffee Consumption and the Risk of Metabolic Syndrome in the ‘Seguimiento Universidad de Navarra’ Project authors: - María J. Corbi-Cobo-Losey - Miguel Á. Martinez-Gonzalez - Anne K. Gribble - Alejandro Fernandez-Montero - Adela M. Navarro - Ligia J. Domínguez - Maira Bes-Rastrollo - Estefanía Toledo journal: Antioxidants year: 2023 pmcid: PMC10044807 doi: 10.3390/antiox12030686 license: CC BY 4.0 --- # Coffee Consumption and the Risk of Metabolic Syndrome in the ‘Seguimiento Universidad de Navarra’ Project ## Abstract [1] Background: Metabolic Syndrome (MetS) affects over a third of the United States population, and has similar prevalence in Europe. Dietary approaches to prevention are important. Coffee consumption has been inversely associated with mortality and chronic disease; however, its relation to the risk of MetS is unclear. We aimed to investigate the association between coffee consumption and incident MetS in the ‘Seguimiento Universidad de Navarra’ cohort. [ 2] Methods: From the SUN project, we included 10,253 participants initially free of MetS. Coffee consumption was assessed at baseline, and the development of MetS was assessed after 6 years of follow-up. All data were self-reported by participants. MetS was defined according to the Harmonizing Definition. We used multivariable logistic regression models to estimate odds ratios and $95\%$ confidence intervals for incident MetS according to four categories of coffee consumption: <1 cup/month; ≥1 cup/month to <1 cup/day; ≥1 cup/day to <4 cups/day; ≥4 cups/day. [ 3] Results: 398 participants developed MetS. Coffee consumption of ≥1 to <4 cups/day was associated with significantly lower odds of developing MetS (multivariable adjusted OR = 0.71, $95\%$ CI (0.50–0.99)) as compared to consumption of <1 cup/month. [ 4] Conclusions: In a Mediterranean cohort, moderate coffee consumption may be associated with a lower risk of MetS. ## 1. Introduction Metabolic syndrome (MetS) is a confluence of cardiovascular risk factors incorporating insulin resistance, dyslipidaemia, obesity, and hypertension. MetS is estimated to affect approximately one quarter of the global population [1] despite definitional discrepancies. The most widely accepted definition is the Harmonizing Definition provided by the International Diabetes Federation (IDF) Task Force on Epidemiology and Prevention and the American Heart Association/National Heart, Lung, and Blood Institute (AHA/NHLBI) [1]. Three out of five MetS criteria must be met in order to make a diagnosis. In the United States, MetS prevalence increased by almost $30\%$ in just over 30 years [2]. European studies have described the prevalence to be $23.9\%$ in men and $24.6\%$ in women [3]. As for Spain, the prevalence of MetS was estimated at $22.7\%$ based on 2008–2010 data using the Harmonized Definition [4]. This high prevalence is troubling, as the presence of MetS is associated with an increased risk of new onset cardiovascular disease (CVD) and all-cause mortality [5]. The presence of MetS doubles the risk of CVD over 5–10 years and increases the risk of type 2 diabetes by fivefold or more over a lifetime [1]. It is therefore of clinical relevance to work towards its prevention [1]. Diet and lifestyle changes are an important tool in the prevention of chronic disease. The Mediterranean diet is a pattern of food consumption that has proven its potential to protect cardiovascular health [6] as well as protect against MetS [7]. It differs from other dietary patterns because of its comparatively higher consumption of fats (mainly olive oil) and moderate wine intake [8]. Coffee is one of the most widely consumed beverages around the world and in Europe it is consumed at a rate of approximately 8.3 kg per year per capita [9]. Spain ranks 23rd among countries with the highest per capita coffee consumption [10]. Coffee contains a plethora of micronutrients that have been associated with cardiometabolic health [11], and its consumption causes short-term physiological changes which also affect the cardiovascular system. Coffee consumption and coffee micronutrient components have been suggested to improve glucose metabolism, lower inflammation, and decrease liver damage in animal models [12,13]. Further evidence suggests that it improves endothelial function, aids in loss of fat mass and is associated with favorable plasma biomarkers of metabolic and inflammatory pathways [14,15,16]. A recent metanalysis of randomized controlled trials by Ramli et al. investigated the effects of coffee consumption on anthropometric measurements, glycemic indices, lipid profiles, and blood pressure and found all of these parameters were improved by green coffee extract supplementation [17]. It is of great interest to continue to investigate the relationship between coffee consumption and the development of MetS, especially given that there is little prospective epidemiological evidence on the association between coffee consumption and MetS incidence [18]. There are only three published prospective cohort studies on the relationship between coffee and MetS [19,20,21]. Two of these did not find any significant association between coffee consumption and MetS [19,21]. The other pointed towards a protective effect of coffee consumption on MetS, but its results were not statistically significant [20]. These previous studies were heterogenous in several aspects, including the average baseline age (13–60 years), the average follow-up time (9–23 years), the way that they assessed coffee consumption, and the adjustment for important confounders such as dietary factors and physical activity levels. None of the mentioned studies adjusted for the overall dietary pattern, which may have introduced substantial confounding as coffee drinking habits have been associated with other dietary habits [22]. There are two main varieties of coffee: Arabica and Robusta. The former is more acidic and is predominantly produced in Latin America [23]; the latter is stronger but less acidic and is mainly found in West Africa and Southeast Asia and used in soluble coffee production [24]. During the roasting process, the Maillard reaction leads to the oxidative polymerization and degradation of phenolic compounds [25]. This same process reduces the quantity of carbohydrates, proteins, chlorogenic acid, and free amino acids but increases the lipid and mineral content as well as the caffeine and trigonelline content [26]. The method of coffee preparation has its own role in determining the concentrations of bioactive substances such as diterpenes [27], caffeine, or polyphenols [28]. For example, Moeenfard et al. found that boiled coffee showed the highest diterpene esters concentration, whereas filtered and instant brews showed the lowest concentrations [27]. Our aim was to address the association between coffee consumption and MetS in a prospective cohort of middle-aged Spanish adults and to evaluate the role of the overall dietary pattern in this association. ## 2.1. Study Population The sample for this study was taken from the SUN project. SUN is a cohort of university graduates with open enrollment since December 1999. Participants complete an extensive questionnaire when joining the study and are asked to complete follow-up questionnaires every two years thereafter. Further details on the design of the SUN cohort can be found elsewhere [29]. At the time of data analysis for our study, a total of 22,894 participants had been recruited to the cohort. Participants were excluded for various reasons (Figure 1): 990 who had been followed for less than the minimum of 6 years for possible outcome assessment; 165 who had died prior to outcome assessment; 5137 who had not completed the 6- or 8-year follow-up questionnaire; 4312 who already had at least one component of MetS at enrollment; 1130 who had an energy intake outside of the predefined limits for realistic energy intake [30]; 627 who left more than 15 items blank in the food-frequency questionnaire; 68 who had previous cardiovascular disease; 211 who had previously suffered from cancer, and, 1 whose increased waist circumference during pregnancy confounded the assessment of MetS criteria. The remaining 10,253 participants were included in the study. ## 2.2. Coffee Consumption A semi-quantitative food-frequency questionnaire (FFQ) was used to assess diet [31,32,33]. This previously validated questionnaire included questions on both caffeinated and decaffeinated coffee (cup size 50 cm3). Participants were asked to estimate the average number of cups consumed according to nine categories: ‘never/seldom’, ‘1–3 per month’, ‘1 per week’, ‘2–4 per week’, ‘5–6 per week’, ‘1 per day’, ‘2–3 per day’, ‘4–6 per day’, and ‘6+ per day’. We categorized coffee consumption into four variables (<1 cup/month, ≥1 cup/month to <1 cup/day, ≥1 cup/day to <4 cups/day, ≥4 cups/day). ## 2.3. Metabolic Syndrome Data related to the components of metabolic syndrome were assessed on the third and fourth questionnaires, distributed approximately six and eight years after entry into the cohort. Participants self-reported recent measurements of waist circumference, blood pressure, HDL and LDL cholesterol, and fasting glucose, as well as diagnoses of hypertension and diabetes and any medicines they were using. MetS was defined according to the Harmonizing Definition from the IDF and AHA/NHLBI. That is, it was defined as the presence of three of the following five criteria [1]: (a) central adiposity (waist circumference ≥94 cm for men and ≥80 cm for women, as defined for the European population); (b) elevated triglycerides (≥150 mg/dL or undergoing treatment for hypertriglyceridemia); (c) reduced HDL cholesterol (<40mg/dL for men and <50 mg/dL for women or undergoing treatment for reduced HDL cholesterol); (d) high blood pressure (systolic ≥130 mmHg, diastolic ≥85 mmHg, or undergoing pharmacological treatment for hypertension); and (e) elevated fasting glucose (≥100 mg/dL or undergoing pharmacological treatment for hyperglycemia). Participants were provided with a measuring tape and a set of instructions on how to measure their waist circumference [34]. Each component of MetS was addressed as a dichotomous yes/no variable. The resulting ascertainment of MetS has been previously validated in our cohort [35]. MetS was primarily assessed based on data from the 6-year follow-up questionnaire. In the case of missing information, we used the 8-year follow-up questionnaire to replace missing data. ## 2.4. Other Covariates The SUN project uses a thorough baseline questionnaire to assess all of the individual characteristics of the participants. These include socio-demographic factors such as age, sex, and years at university; health–lifestyle factors such as smoking habits, siesta habits and hours of TV per day; and clinical history, including previous diagnoses of depression and family history of diabetes. Anthropometric variables of height and weight are assessed from which the body mass index (BMI) is then calculated. The average daily energy intake (kcal) and average daily alcohol intake (g/day) were derived from the FFQ based on Spanish food composition tables [36,37]. Important to our study, the baseline questionnaire included a 17-item questionnaire on physical activity, from which total leisure time physical activity (METs–h/week) was derived [38]. The questionnaire also asked whether the participants added sugar to beverages. Furthermore, the FFQ allowed for adherence to the Mediterranean diet to be assessed in accordance with the Mediterranean diet scale proposed by Trichopoulou et al. [ 39], which was modified to exclude the alcohol component (range: 0–8 points). Participants received one point for consuming more than the sex-specific median in each of the six traits typical of the traditional Mediterranean diet: vegetables, legumes, fruit and nuts, whole grain cereals, fish, and ratio of monounsaturated:saturated fats. One point was assigned for consuming less than the median in either of the two components considered contrary to the traditional Mediterranean diet: meat and dairy products. Coffee consumption is not included in this Mediterranean diet score. ## 2.5. Statistical Analysis To address the association between coffee consumption and the risk of MetS, we used non-conditional logistical regression models. We fitted a crude model without adjusting for covariables and then another model adjusted for age and sex. Subsequently, we fitted an additional model adjusting for all other covariates: energy intake (continuous), adherence to a Mediterranean diet (continuous), alcohol intake (three categories), BMI (continuous), physical activity (quartiles), hours of watching TV (continuous), smoking (three categories), pack-years of smoking (continuous), previous depression (yes/no), years of university (continuous), hours of siesta (over or under half an hour), added sugar in drinks (yes/no), and family history of diabetes (yes/no). We addressed the goodness of fit (calibration) of the final model with the Hosmer–Lemeshow test [40]. We assessed the interactions between coffee consumption and sex, BMI (<25 kg/m2/≥25 kg/m2), age (continuous) and adherence to the Mediterranean diet (continuous) with the likelihood ratio test. We repeated our analyses separately for caffeinated and decaffeinated coffee consumption with the aforementioned adjustments. In addition, the results for caffeinated coffee consumption were adjusted for decaffeinated coffee consumption and vice versa. We deemed a p value below 0.05 to be statistically significant. ## 3. Results Our analyses included 10,253 participants. Of these, 398 developed MetS. The baseline characteristics of participants according to the categories of coffee consumption are described in Table 1. The participants with higher coffee consumption were older and also included a moderately greater percentage of smokers, a moderately higher average BMI, and a higher total energy intake, as well as a better score of adherence to the Mediterranean diet. Participants who consumed ≥4 cups of coffee a day were more likely to add sugar to their beverages compared to those who consumed <1 cup per month. Table 2 shows the odds ratios (OR) ($95\%$ confidence interval) for the risk of developing MetS by categories of coffee consumption. In comparison with the control category (<1 cup of coffee per month), participants who consumed one to less than four cups of coffee per day showed significantly lower odds of developing MetS (OR ≥ 1 cup/d to <4 cups/d 0.71, $95\%$ CI (0.50–0.99)). The p for trend was not statistically significant, nor was the continuous association between one more cup of coffee per day and the risk of MetS. We observed no significant interactions of coffee consumption with age, sex, BMI, or adherence to the Mediterranean diet. When we separated caffeinated coffee consumption from decaffeinated coffee consumption, the point estimates across categories of caffeinated and decaffeinated coffee consumption were similar to what had been observed for total coffee consumption; however, the associations were slightly attenuated, and they were no longer significant. These results are shown in Table 3 for caffeinated beverages and Table 4 for non-caffeinated beverages. ## 4. Discussion Our results suggest that moderate coffee consumption (≥1 cup/d to <4 cups/d) may be associated with a decreased risk of developing MetS in a multivariable model. We did not find any remarkable difference between caffeinated and decaffeinated coffee consumption. Although no single category yielded statistically significant results, there was a trend towards an overall protective effect once covariates were accounted for. Previously published prospective studies on the association between coffee consumption and the development of MetS did not report significant results. A comparison of the results is difficult due to a high degree of heterogeneity: not only did they use different definitions for MetS, but they also used a vastly different categorization of coffee consumption. The Tromsø study [21], similar to our study, categorized coffee consumption in categories of cups/day; however, the ARIC study [20] grouped coffee consumption by quintiles, and the Amsterdam study [19] did not give details about their categorization of average long-term coffee consumption. It is important to note that our study was the only study to suggest an association that remained statistically significant after multivariate adjustment and was also the study that adjusted for the most covariables. Most importantly, we were able to limit confounding by adjusting for physical activity levels and overall dietary pattern. A fundamental difference between the previous studies was their use of differing as well as modified definitions of MetS, leading to altered inclusion criteria and eventual inconsistencies in the data. The Tromsø and Amsterdam studies used modified versions of the NCEP ATP III definition [41], whereas the ARIC study used the American Heart Association guidelines [42]. Furthermore, the previous studies looked at data from the United States, the Netherlands, and Norway, whereas our cohort is from Spain. This spread of geographical locations is relevant for our study question as coffee consumption patterns - including the type of coffee bean and the method of preparation - may be different in the United States and Northern Europe as compared to Spain. For example, filtered, boiled, and instant coffee brewing methods predominate in Norway [43,44], whereas espresso and percolator coffee predominate in Spain [45]. It has been found that different brewing methods yield different results in terms of the extraction of antioxidants, caffeine, and micronutrients [46,47,48]. The preparation method and the type of coffee may impact the different compounds found within coffee. For example, diterpenes are largely removed during the filtering process [27] and are more present in the arabica bean variety [49]. The roasting process and the method of preparation may also be important for disease prevention, but we were not able to differentiate between roasting or brewing methods. In addition, the average baseline age differed greatly between the study samples of the previous studies. The ARIC study participants were middle-aged (mean age 53.6 years), whereas those included in the Amsterdam Study were much younger (27 years old at baseline). The median age of participants in the Trømso study was 30–39 years, so this latter study more closely resembles our own contribution. Inconsistent results across different age groups may suggest a critical time window for coffee consumption in MetS prevention. There are several mechanisms that may explain the potential protective effect of coffee consumption on MetS incidence. Primarily, coffee consumption has been inversely associated with chronic inflammation, which is an underlying condition of MetS [16,50]. A significant association has been found between caffeine and reduced inflammation in animal studies [51]. Moreover, specific components found in coffee have been linked to beneficial biological processes. So, melanoidins have been shown to have both antioxidant and anti-inflammatory properties [52], chlorogenic acids have been shown to increase the bioavailability of NO and inhibit the activity of the angiotensin-converting enzyme [53], and diterpenes, especially cafestol and kahweol, have also been associated with the downregulation of inflammatory mediators [54]. Interestingly, coffee has previously been associated with hypertension, one of the MetS components, due to caffeine’s ability to increase blood pressure [55]. However, this effect was found for much higher concentrations of caffeine than those found in regular coffee consumption, which is also affected by extraction methods and methods of administration [56]. There is additional controversy as caffeine could actually alleviate hypertension by binding to the A1 adenosine receptors [57]. Meanwhile other studies have shown a quick adaptative response against the pressor effect as well as a tolerance among regular consumers of coffee [58]. In any case, coffee contains compounds besides caffeine. For example, it is rich in anti-hypertensive minerals (vitamin E, niacin, potassium, and magnesium) [59]. These other compounds could counteract the potential hypertensive effect of caffeine and could even be responsible for a beneficial effect of coffee consumption in terms of hypertension prevention. Coffee consumption has previously been linked to reduced risk of type 2 diabetes [60] and improved glucose metabolism as it reduces the area under the glucose curve and increases the insulin response [61]. High coffee consumption has been associated with increased insulin secretion, insulin sensitivity, and β-cell function [62], in part due to caffeine [61] and in part due to coffee’s effects on adiponectin [63]. As trigonelline has been found to reduce glucose and insulin levels at higher concentrations than those normally found in coffee, it is coherent to think that the long-term trigonelline intake from coffee may also exert a beneficial role [64]. Coffee consumption has also been associated with a lower risk of developing obesity [65] and with favorable changes in BMI and waist circumference over time [66,67]. It is possible that coffee may contribute to weight control through its capacity to reduce fatigue. This could lead to an increase in total energy expenditure. Caffeine has been observed to increase energy expenditure and therefore lead to weight loss by increasing thermogenesis [68]. The polyphenol content could be responsible for the reduced risk of dietary-induced obesity by increasing energy expenditure through their regulation of the expression of lipogenic enzymes [69]. The strengths of our study include its prospective design and our ability to account for a large range of potential confounders in our analyses. Nevertheless, we acknowledge that our results did not show any evidence of a linear trend and were not consistent with the previous literature. Dose–response trends and consistency are usually included among the causality criteria in observational studies, and they were not met in this study. Therefore, our findings can only be considered as suggestive of a potential protection, and need to be replicated in independent cohorts with good control of confounding. We recognize several limitations of our study. First, coffee consumption was self-reported rather than quantified by objective means. This method of data collection was necessary due to the large size of the SUN cohort. It may be preferable to the time-limited methods of direct observation or food diary completion because it allows participants to report ‘usual consumption over the last year’. Importantly, the FFQ used by the SUN Project has previously been validated [31,32,33]. A second limitation is that participants’ coffee consumption was only reported at the beginning of the study, and we were not able to account for possible changes over time. Nevertheless, it has been suggested that the coffee consumption habit remains relatively constant in adulthood [70]. Third, we did not differentiate between coffee bean varieties or the processing or brewing methods. In Spain, the most frequent type of preparation has been reported to be unfiltered coffee (espresso or percolator) [45]. Fourth, information for the assessment of MetS criteria was self-reported, though this too has been previously validated in our cohort [35]. Fifth, it could be argued that the incidence of MetS that we observed ($4.15\%$) was lower than the expected incidence among the general population. This reduction is likely because of the selection process (excluding all baseline criteria of MetS) as well as the low BMI, younger age, and higher educational level of our cohorts’ participants. In this way, our sample was not representative of the general population. Finally, due to the observational nature of our study, confounding cannot completely be ruled out even though we adjusted our results for a wide range of confounders. ## 5. Conclusions In conclusion, moderate regular coffee consumption might reduce the risk of developing MetS, but further evidence is needed. There are physiological mechanisms that support a relationship between coffee and reduced risk of MetS. However, the currently available cohort studies are scarce. Our study is the first to find some statistically significant associations, though further prospective studies are warranted to confirm these results. ## References 1. Alberti K.G., Eckel R.H., Grundy S.M., Zimmet P.Z., Cleeman J.I., Donato K.A., Fruchart J.-C., James W.P.T., Loria C.M., Smith S.C.. 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--- title: Flavonoid Extract from Seed Residues of Hippophae rhamnoides ssp. sinensis Protects against Alcohol-Induced Intestinal Barrier Dysfunction by Regulating the Nrf2 Pathway authors: - Juan Wei - Jinmei Zhao - Tingting Su - Sha Li - Wenjun Sheng - Lidan Feng - Yang Bi journal: Antioxidants year: 2023 pmcid: PMC10044812 doi: 10.3390/antiox12030562 license: CC BY 4.0 --- # Flavonoid Extract from Seed Residues of Hippophae rhamnoides ssp. sinensis Protects against Alcohol-Induced Intestinal Barrier Dysfunction by Regulating the Nrf2 Pathway ## Abstract Alcohol has been demonstrated to disrupt intestinal barrier integrity. Some flavonoid compounds that exert antioxidant activity have a protective effect on intestinal barrier function. As an important medicinal and edible plant, sea buckthorn (Hippophae) seeds are rich in flavonoids, but their protective effect on the intestinal barrier has not been reported. In our research, 76 kinds of flavonoids were identified in Hippophae rhamnoides ssp. sinensis seed residue flavonoids (HRSF) by ultra-performance liquid chromatography–tandem mass spectrometry. Kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, kaempferol-3-O-robinoside-7-O-rhamnoside, isorhamnetin-3-O-2G-rhamnosylrutinoside, quercetin-3-O-rutinoside, (−)-epigallocatechin, and B type of procyanidin were the most abundant substances, accounting for $15.276\%$, $15.128\%$, $18.328\%$, $10.904\%$, $4.596\%$, $5.082\%$, and $10.079\%$ of all identified flavonoids, respectively. Meanwhile, pre-treatment with HRSF was able to prevent alcohol-induced disruption of intestinal barrier integrity through elevating the transepithelial monolayer resistance value, inhibiting the flux of fluorescein isothiocyanate-dextran, and upregulating the mRNA and protein level of TJs (occludin and ZO-1). Furthermore, it was also able to reverse alcohol-induced oxidative stress through suppressing the accumulation of reactive oxygen species and malondialdehyde, improving the glutathione level and superoxide dismutase activity. Finally, the results showed that HRSF pre-treatment effectively elevated the erythroid-related factor 2 mRNA and protein level compared with the alcohol-alone treatment group. Our research was the first to demonstrate that HRSF could prevent alcohol-induced intestinal barrier dysfunction through regulating the Nrf2-mediated pathway in order to attenuate oxidative stress and enhance TJ expression. ## 1. Introduction Alcoholic beverages are widely consumed all over the world, resulting in numerous gastrointestinal and liver disorders [1]. Most alcohol is absorbed in the proximal small intestine by simple diffusion after oral administration because of its good water solubility, moderate lipid solubility, and small molecular size [2]. Therefore, the intestine is the primary target of alcohol [3]. The intestinal epithelium, which is a continuous monolayer, is the first barrier of the intestine preventing against the invasion of pathogenic antigens and toxins [4]. Tight junctions (TJs) form between epithelial cells to control the selective permeability of the intestinal epithelium [5]. TJs are composed of various molecular components, including transmembrane proteins (claudins and occludin), peripheral membrane proteins (zonulin occludins, ZOs), tricellulin, and junctional adhesion molecules (JAMs) [6]. Studies have identified that alcohol could disrupt the epithelial TJs, leading to abnormal intestinal leakage of bacterial endotoxins and macromolecules [2]. Moreover, cellular oxidative stress is also an important factor in intestinal barrier dysfunction caused by alcohol [7]. A certain range of alcohol (2.5–$15\%$) can increase the permeability of Caco-2 monolayers, which is a typical model of the intestinal barrier, via activating the production of reactive oxygen species (ROS), changing the expression of TJ proteins, and causing oxidation of the microtubule cytoskeleton [8,9]. Flavonoids are important secondary metabolites in plant-based foods, being known for their antioxidant activities [10]. It has been shown that some flavonoids also exert beneficial effects on intestinal epithelial barrier function. Flavonoid-enriched extracts from orange peel could attenuate the alcohol-induced permeability of the Caco-2 cell monolayer by increasing the expression of TJ proteins, including ZO-1, occludin, and claudin 4 [11]. Meanwhile, naringenin, which is the major flavonoid from citrus fruits, was able to modulate TJ protein expression and improve barrier integrity in Caco-2 monolayers, as well as mitigate colitis in mice, by improving the TJ barrier [12,13]. Furthermore, quercetin and morin were also reported to reverse the decreasing of the trans-epithelial electrical resistance (TEER) value and the increasing of membrane permeability that was induced by high-glucose treatment in the Caco-2 cell monolayer [14]. Since natural extracts from plants that are rich in flavonoids generally have antioxidant and intestinal epithelial barrier protective activities, they could be considered as effective strategies to alleviate alcohol-induced intestinal barrier dysfunction. Sea buckthorn (Hippophae), which is a small tree or deciduous shrub, belongs to the Elaeagnaceae family and is mainly distributed in Europe and Asia [15]. China is the largest sea buckthorn producer in the world [16]. As a medicinal and edible plant, all parts of sea buckthorn, including its leaves, berries, and stems, contain abundant phenolic compounds that are mainly composed of phenolic acids and flavonoids [17,18,19]. Notably, flavonoids occupied over $90\%$ of the content of the total phenolic compounds in sea buckthorn berries, including the seeds [18]. Sea buckthorn seeds are rich in flavonoids and are mainly found in the forms of flavonoid glycoside, isorhamnetin-3-O-sophroside-7-O-rhamnosid, isorhamnetin-3-O-rutinoside, and quercetin-3-O-rutinoside, being the most abundant glycosides; meanwhile, querceitin-3-O-glucoside-7-O-rhamnosides; isorhamnetin-3-O-glucoside-7-O-rhamnosides; quercetin-3-O-sophroside-7-Orhamnosides; kaempherol-3-O-sophroside-7-orhamnosides; isorhamnetin-3-O-glucoside; and their free forms including kaempherol, quercetin, and isorhamnetin were also found in significant amounts [20]. Antioxidant effects are the most important function of the flavonoid-enriched seed extract from sea buckthorn; moreover, it also exerts hypolipidaemic, hypoglycaemic, anti-obesity, anti-hypertriglyceridemia, and anti-hypertensive effects [21,22,23,24]. It could be supposed that sea buckthorn seed extract may show an intestinal epithelial barrier protective effect due to its high flavonoid contents and antioxidant activity. Until now, sea buckthorn seeds have mainly been used to extract seed oil, while the remaining seed residue has not been effectively utilized. A few studies have reported some flavonoid compounds of sea buckthorn seed, but the composition of its flavonoids has not been comprehensively analyzed. Although antioxidant effects are the most important function of sea buckthorn, it is only demonstrated by 2,2′-azino-bis(3-ethylbenzothiazoline-6-sulfonic acid) radical scavenging activity assay [21], with the bioavailability, absorption, and metabolism of seed extract in cells not being taken into consideration. Thus, the antioxidant activity of sea buckthorn seed extract needs to be tested at the cell level. Meanwhile, no research on the intestinal barrier protection effect of sea buckthorn seed flavonoids has been reported until now. There are seven species and eleven subspecies of sea buckthorn that have been recognized worldwide [25]. Hippophae rhamnoides ssp. sinensis is the wild sea buckthorn subspecies of China with the broadest distribution and maximum production [16]. In this study, flavonoids of Hippophae rhamnoides ssp. sinensis seed residues were extracted by aqueous ethanol and purified by macroporous resin. The composition and content of Hippophae rhamnoides ssp. sinensis seed residue flavonoid extract (HRSF) was analyzed by ultra-performance liquid chromatography combined with tandem mass spectrometry equipped with an electrospray ionization (UPLC-ESI-MS/MS) system. Then, the protective effect of HRSF against alcohol-induced intestinal barrier interruption was investigated for the first time by estimating the change of TEER value and flux of fluorescein isothiocyanate (FITC)-dextran in Caco-2 monolayers. Meanwhile, the impact of HRSF on intestinal barrier integrity was also investigated by testing the TJs mRNA and protein level, including ZO-1 and occludin. Moreover, the antioxidant activity of HRSF, which may be responsible for its intestinal protective function, was illustrated by estimating the production of ROS, malondialdehyde (MDA), and glutathione (GSH), as well as the enzyme activity of superoxide dismutase (SOD). Finally, the regulation of the nuclear factor erythroid-related factor 2 (Nrf2) gene, which is the key factor responding to resisting the oxidative system, was explored in order to elucidate the possible protective mechanism of HRSF against alcohol-induced intestinal barrier dysfunction. ## 2.1. Chemicals Fluorescein isothiocyanate (FITC)-dextran and 2,7-dichlorodihydrofluorescein diacetate (DCFH-DA) was provided by Sigma-Aldrich (St Louis, MO, USA). TRIpure total RNA extraction reagent (Trizol) and Cell Counting Kit-8 (CCK8) were purchased from BioTeke Corpotation (Beijing, China). Assay kits of Whole Cell Lysis (WLA019), MDA (TBA method) (WLA048), GSH (WLA105), SOD (WLA110), rapid preparation for SDS-PAGE gel (WLA013), electrochemilu-minescence (ECL) detection reagents, goat anti-rabbit IgG-HRP antibody, occludin antibody, ZO-1 antibody, Nrf2 antibody, and β-actin antibody were purchased from Wanleibio Co., Ltd. (Shenyang, China). All the standards were purchased from MedChemExpress (Monmouth Junction, NJ, USA). ## 2.2. Materials Sea buckthorn (Hippophae rhamnoides ssp. sinensis) seed residues obtained after extracting seed oil were provided by Gansu Gannong Biotechnology Co., Ltd. (Lanzhou, China). ## 2.3. Extraction, Identification, and Quantification of Phenolic Components The extraction of Hippophae rhamnoides ssp. sinensis seed residue flavonoids followed our previously published method [19]. Briefly, seed residues were extracted by aqueous ethanol (ethanol/water, 2:1, v/v) with the assistance of ultra-sonication; the extraction was then freeze-dried. The dried power was purified by AB-8 macroporous resin and freeze-dried again. The product was collected and stored at −20 °C, namely, the Hippophae rhamnoides ssp. sinensis seed residue flavonoids (HRSF). Its total phenolic content (TPC) was tested according to the Folin–Ciocâlteu method and expressed as milligram gallic acid equivalent per gram of HRSF weight (mg GA equiv./g HRSF); meanwhile, its total flavonoid content (TFC) was estimated by the NaNO2-AlCl3-NaOH method and expressed as milligram rutin equivalent per gram of HRSF weight (mg RT equiv./g HRSF). The composition and content of flavonoid compounds in HRSF was analyzed by the UPLC-ESI-MS/MS system (UPLC, ExionLC™ AD; MS, Applied Biosystems 6500 Triple Quadrupole) (SCIEX, Framingham, MA, USA) according to the method of Chen et al. [ 26]. A total of 196 kinds of flavonoid compounds were detected (Table S1). The conditions were as follows: UPLC: column, Waters ACQUITY UPLC HSS T3 C18 (100 mm × 2.1 mm i.d. 1.8 µm); solvent system, water with $0.05\%$ formic acid (A), acetonitrile with $0.05\%$ formic acid (B). The gradient elution program was set as follows: 0–1 min, 10–$20\%$ B; 1–9 min, 20–$70\%$ B; 9–12.5 min, 70–$95\%$ B; 12.5–13.5 min, $95\%$ B; 13.5–13.6 min, 95–$10\%$ B; 13.6–15 min, $10\%$ B. The flow rate was set at 0.35 mL/min, and the temperature was set at 40 °C. The injection volume was 2 μL. ESI-MS/MS: quadrupole-linear ion trap mass spectrometer (QTRAP)® 6500 + LC-MS/MS System, equipped with an ESI Turbo Ion-Spray interface, operating in positive and negative ion mode and controlled by Analyst 1.6.3 software (Sciex). The ESI source operation parameters were as follows: ion source, ESI+/−; source temperature, 550 °C; ion spray voltage: (IS) 5500 V (Positive), −4500 V (Negative); curtain gas (CUR) was set at 35 psi. Flavonoids were analyzed using scheduled multiple reaction monitoring (MRM). Data acquisitions were performed using Analyst 1.6.3 software (Sciex). Multiquant 3.0.3 software (Sciex) was used to quantify all compounds. The result was expressed as mg/g HRSF. ## 2.4. Cell Culture Caco-2 cells (Bioleaf Company, Shanghai, China) were cultured in Dulbecco’s modified Eagle’s medium (DMEM) (Servicebio Technology Co., Ltd., Wuhan, China) with 25 mmol/L glucose, 4 mmol/L L-glutamine, 1 mmol/L sodium pyruvate, and phenol red, supplemented with 100 U/mL penicillin, 100 μg/mL streptomycin, and $10\%$ fetal bovine serum (FBS) (Zhejiang Tianhang Biotechnology Co., Ltd., Huzhou, China) at 37 °C with $5\%$ CO2. ## 2.5. Analysis of Cell Cytotoxicity The CCK8 assay was used to analyze the cytotoxicity of HRSF [27]. Caco-2 cells were maintained for 21 days, then treated with HRSF (2.5 μg/mL–320 μg/mL, concentrated as microgram gallic acid equivalent per milliliter) for 24 h. The absorbance was assessed after incubation with CCK8 for 3 h at 450 nm by an iMARK microplate reader (Bio-Rad Laboratories, Inc., Hercules, CA, USA). Cell viability was assessed as the percentage of the mean value normalized to untreated cells. ## 2.6. Measurement of TEER Value and Paracellular Permeability The TEER assay was applied to estimate intestinal barrier integrity [11,27]. Briefly, Caco-2 cells were planted in the apical chamber of a 12-well transwell plate. After 21 days, cells were pre-treated with HRSF (5, 10 and 20 μg/mL) in serum-free medium for 1 h prior to treatment with $7.5\%$ alcohol for 24 h in a protective group. Meanwhile, wells treated only with alcohol or only with HRSF were used as the alcohol-induced damage group or the HRSF-alone treatment group, respectively. Cells without alcohol and HRSF served as the control group. The medium was removed, the apical chamber and basolateral chamber were filled with 37 °C Hank’s Balanced Salt Solution to wash the cells twice, and then the mixture was incubated at 37 °C with Hank’s Balanced Salt Solution for 30 min. Finally, TEER was estimated with an epithelial volt-ohmmeter ERS-2 (Merck Millipore, Burlington, MA, USA). Resistance values (Ω·cm2) were calculated by multiplying the area of the membrane filter. The results were expressed as TEER (% of control) = (TEERtreatment/TEERcontrol) × $100\%$. The flux of the FITC-dextran assay was used to estimate the paracellular permeability [27]. Briefly, cells were incubated for 21 days in a transwell plate, then treated as described above. The medium was removed, and cells were washed twice with Hank’s Balanced Salt Solution, as above. The apical chamber was filled with FITC-dextran solution (1 mg/mL), and the basolateral had Hank’s Balanced Salt Solution added; then, the mixture was incubated for 4 h. Finally, the liquid in the basal chamber was collected, and the fluorescence was measured with a SynergyTM HT multi-mode microplate reader (BioTek, Winooski, VT, USA) with an excitation of 490 nm and emission of 520 nm. The content of fluorescence was expressed in relative fluorescence units (RFU). The results were expressed as cumulative transport of FITC-dextran (% control) = (RFUtreatment/RFUcontrol) × $100\%$. ## 2.7. Quantitative Reverse-Transcription Polymerase Chain Reaction (qRT-PCR) Analysis of mRNA Level Following the method of Wei et al. [ 28], respective treatments were given to Caco-2 cells after 21 days culture as described for TEER measurement. The RNA was obtained using TRIpure total RNA extraction reagent (Trizol). Briefly, cells were lysed in 1 mL TRIpure for 5 min, then mixed with 200 μL chloroform and stand still for 3 min. The aqueous phase was mixed with isopropanol, then incubated at −20 °C overnight. The mixture was centrifuged at 4 °C at 10,000× g, and the aqueous phase was precipitated with ethanol, whereas total RNA was dissolved with 30 μL RNase-free ddH2O. Then, the first-strand cDNA was synthesized by the PrimeScript RT Reagent Kit, and qRT-PCR was continued by using the SYBR Premix EX TaqTM II kit (Thermo Fisher, Waltham, MA, USA) in an ExicyclerTM 96 fluorescence quantitative instrument (Bioneer Corporation, Daejeon, Republic of Korea). Gene expressions of occludin, ZO-1, and Nrf2 were normalized to β-actin. The results were calculated using the 2−ΔΔCT method. The primers shown in Table S2 were synthesized by the Genscript Biotech Corporation (Nanjing, China). ## 2.8. Western Blot Analysis of Protein Expression Cells were treated as described in Section 2.6. The whole protein was obtained by the Cell Lysis Kit. Briefly, 600 μL lysis buffer supplemented with 6 μL phenylmethylsulfonyl fluoride (PMSF) was added to cells from one cell culture dish, and then the mixture was centrifuged at 10,000× g after incubation on ice for 30 min; the supernatant was the whole cell protein. A total of 40 μg protein of each group was electrophoresed in $10\%$ polyacrylamide gel and transfected onto a polyvinylidene difluoride membrane (Merck Millipore, Burlington, MA, USA). The membrane was blocked in $5\%$ non-fat milk for 1 h and incubated overnight with primary antibodies of occludin, ZO-1, Nrf2, and β-actin at 4 °C. Then, subsequently, it was washed and incubated with goat anti-rabbit IgG-HRP secondary antibody. The protein bands were developed with ECL detection reagents. The optical density was estimated through Gel-Pro-Analyzer software and normalized to β-actin, expressed as fold-over basal change comparative to the control group. ## 2.9. Measurement of ROS Caco-2 cells were treated as described above. Following this, the cells of each group were collected and stained with DCFH-DA (15 μmol/L) for 20 min. Fluorescence intensity was recorded by flow cytometry (NovoCyte TM, ACEA Biosciences, San Diego, CA, USA) with the excitation of 485 nm and emission of 535 nm. The result was analyzed using NovoExpress software. ## 2.10. Estimation of MDA, GSH Content, and SOD Activity Cells were cultured and treated as described above, then resuspended with phosphate-buffered saline, lysed ultrasonically in an ice bath, and centrifuged. Protein in the supernatant was quantified with the BCA Protein Assay Kit (WLA004) following the manufacturer’s instructions. Then, the content of MDA and GSH were estimated by the MDA and GSH Assay Kit, expressed as nmol per milligram of cell protein (nmol/mg prot). Meanwhile, the activity of SOD was analyzed by the SOD Assay Kit, expressed as active unit per milligram of cell protein (U/mg prot). ## 2.11. Data Analysis Results were calculated from the mean ± standard deviation of at least three independent experiments. The statistical analysis was performed with the software of SPSS Statistics 26.0 (IBM, Armonk, NY, USA). The analysis of significant differences was expressed by Duncan’s multiple range tests ($p \leq 0.05$ means statistically significant difference). ## 3.1. The Flavonoid Composition and Content of HRSF The results showed that the TPC and TFC of HRSF were 327.759 mg GA equiv./g HRSF and 277.356 mg RT equiv./g HRSF, respectively (Table 1). Thus, it could be inferred that flavonoids were the predominant polyphenols in sea buckthorn seeds, which was also demonstrated through the research of Gong et al. [ 21]. The HPLC-ESI-MS/MS analysis identified 76 compounds of flavonoids in HRSF, belonging to the subclasses of flavonols, flavanols, procyanidins, flavanones, chalcones, flavanonols, isoflavanones, flavones, and other flavonoids derivatives (Table 1, Figure 1). ## 3.1.1. Flavonols A total of 24 compounds of flavonols were identified in Hippophae rhamnoides ssp. sinensis seed residues; the total content was up to 36.014 mg/g HRSF, which was the highest content subclass of flavonoids, accounting for $73.249\%$ of all identified flavonoids (Table 1, Figure 1). Most of the flavonols in HRSF mainly existed as glycoside forms of kaempferol, isorhamnetin, and quercetin, which was consistent with a previous report by Arimboor and Arumughan [20]. The predominant compounds were robinin (kaempferol-3-O-robinoside-7-O-rhamnoside), nicotiflorin (kaempferol-3-O-rutinosid), and narcissin (isorhamnetin-3-O-rutinoside), up to the content levels of 9.011, 7.511, and 7.438 mg/g HRSF, accounting for $18.328\%$, $15.276\%$, and $15.128\%$ of all the identified flavonoids, respectively (Table 1, Figure 1). Arimboor and Arumughan [20] also reported that isorhamnetin-3-O-rutinoside was the major flavonoid of Hippophae rhamnoides L. seeds from India. Kaempferol-3-O-rutinosid and kaempferol-3-O-robinoside-7-O-rhamnoside were identified for the first time in sea buckthorn seeds. Typhaneoside (isorhamnetin 3-O-2G-rhamnosylrutinoside) was also the major flavonol glycoside in HRSF, with a concentration of 5.361 mg/g HRSF (Table 1, Figure 1), which was the first time it was identified in sea buckthorn seeds. A significant amount of rutin (quercetin-3-O-rutinoside) was also found in HRSF, up to the content of 2.26 mg/g (Table 1, Figure 1), which was in accordance with a previous report [20]. Meanwhile, kaempferol-3-neohesperidoside, astragalin (kaempferol-3-β-D-glucopyranoside), baimaside (quercetin-3-sophoroside), isorhamnetin-3-O-glucoside, and tiliroside also presented high content levels in Hippophae rhamnoides ssp. sinensis seeds, at the amounts of 0.570, 0.620, 0.412, 0.525, and 0.178 mg/g HRSF, respectively. Moreover, the free forms of flavonol glycosides, including isorhamnetin, quercetin, kaempferol, and myricetin, were also identified at the levels of 0.726, 0.547, 0.263, and 0.196 mg/g HRSF, respectively. Isorhamnetin, quercetin, and kaempferol were also reported at high levels in $70\%$ methanol extract of Hippophae rhamnoides L. seeds from Xinjiang Province, China [29]. However, myricetin was identified for the first time in sea buckthorn seeds. Furthermore, isorhamnetin-3-O-neohespeidoside, quercimeritrin, spiraeoside, afzelin, kaempferitrin, miquelianin, avicularin, laricitrin, 2′′-o-galloylhyperin, and quercitrin were also found in HRSF, but at relatively low levels (less than 0.1 mg/g HRSF). ## 3.1.2. Procyanidins Procyanidins are an important subclass of flavonoids in sea buckthorn seed and are only composed of B-type procyanidin [30]. In our research, procyanidin B2 was used as the standard for quantification of the procyanidins—the result showed that the B type of procyanidins presented a significantly high level in sea buckthorn seeds at the level of 4.956 mg/g HRSF and occupied $10.079\%$ of all the identified flavonoids (Table 1, Figure 1). ## 3.1.3. Flavanols Flavanol compounds formed the third abundant subclass of flavonoids in Hippophae rhamnoides ssp. sinensis seeds, accounting for $8.691\%$ of all the identified flavonoids in HRSF (Table 1, Figure 1). ( −)-Epigallocatechin was the most abundant compound of this subclass, at the amount of 2.853 mg/g HRSF (Table 1, Figure 1). ( −)-Gallocatechin, (−)-catechin, (−)-epicatechin, and (−)-catechin gallate were also present at high levels at 0.763, 0.271, 0.231, and 0.155 mg/g HRSF, respectively (Table 1, Figure 1). Except for (−)-catechin gallate, which was identified firstly in sea buckthorn seed in our research, (−)-epigallocatechin, (−)-gallocatechin, (−)-catechin, and (−)-epicatechin were also reported in a previous research in the water-acetone extract of Hippophae rhamnoides ssp. sinensis seeds from Shanxi Province, China [31]. ## 3.1.4. Flavanones and Flavanonols Nine flavanone compounds were found in HRSF, and most of them were in the form of glycosides. Naringenin-7-glucoside, hesperidin, and poncirin were the major flavanones, which were firstly identified in sea buckthorn, presenting at the levels of 0.321, 0.132, and 0.121 mg/g HRSF, respectively. Moreover, isosakuranin, eriodictyol, eriocitrin, pinocembrin, narirutin, and sophoraflavanone G were found at a negligible amount (Table 1, Figure 1). Furthermore, four kinds of flavanonols were firstly found in sea buckthorn seed in our results, namely, dihydrokaempferol, dihydromyricetin, astilbin, and taxifolin 7-O-rhamnoside in the amounts of 0.085, 0.104, 0.062, and 0.087 mg/g HRSF, respectively (Table 1, Figure 1). ## 3.1.5. Chalcones, Isoflavanones, Flavones, and Other Flavonoids Six kinds of chalcones were found in HRSF, among them, phlorizin and naringenin chalcone were at high levels at 0.651 and 0.185 mg/g HRSF, respectively. In addition, four kinds of isoflavanones were identified in HRSF as well, and genistin was the highest compound of this subclass, at a concentration of 0.126 mg/g HRSF. Fourteen flavone compounds were also detected, but all at negligible levels. Furthermore, some other kinds of flavonoids were found at a relatively high level, such as mangiferin, theaflavin, and methylnissolin-3-O-glucoside at the concentrations of 0.588, 0.644, and 0.231 mg/g HRSF, respectively. Generally speaking, flavonols were the predominant subclass of flavonoids in HRSF, and most of them were presented in the form of glycosides. Kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, kaempferol-3-O-robinoside-7-O-rhamnoside, isorhamnetin-3-O-2G-rhamnosylrutinoside, and quercetin-3-O-rutinoside were the most abundant substances among them. Moreover, (−)-epigallocatechin belonging to the flavone subclass and B type of procyanidin also presented at a significantly high level. ## 3.2. HRSF Alleviated the Alcohol-Induced Decreasing of TEER and Increasing of Paracellular Permeability of the Caco-2 Monolayer HRSF showed no cytotoxicity to Caco-2 cells between 0 and 40 μg/mL. However, it showed significant cytotoxicity above 80 μg/mL, at which cell viability dropped to $55.65\%$ (Figure 2A). Therefore, the maximum concentration of HRSF in subsequent experiments should not exceed 40 μg/mL. TEER value and paracellular marker FITC-dextran are two common indicators of intestinal barrier integrity and could be used to test membrane permeability [27]. Our results showed that TEER value was decreased by $53.44\%$ and FITC-dextran transportation was increased by $47.24\%$ of alcohol treatment alone, demonstrating that alcohol could cause intestinal barrier dysfunction of the ileum-like Caco-2 monolayer model (Figure 2B,C). Pre-incubation with 5 μg/mL HRSF before treatment with alcohol could increase the TEER by $38.20\%$ and decrease the FITC-dextran diffusion by $31.75\%$ compared with the alcohol alone treatment group. Furthermore, 20 μg/mL HRSF could restore the TEER decrease and FITC-dextran transportation caused by alcohol to the level of control group. It should be noted that the HRSF alone treatment had no significant effect on TEER and FITC-dextran diffusion, and thus we could infer that HRSF significantly prevented the impairments of the intestinal barrier induced by alcohol. This result was consistent with previous finding that some flavonoid extracts or compounds exert protective effects on intestinal barrier integrity against alcohol administration. For example, flavonoid extracts from orange peel prevented alcohol-induced decreasing of the TEER value, as well as the increasing of FITC-dextran diffusion in the Caco-2 monolayer [11]. Luteolin, a dietary flavonoid, also effectively prevents alcohol-induced intestinal barrier injury by increasing TEER value and reducing FITC-dextran transportation [32]. Olejnik et al. [ 33] reported that blackcurrant fruit extract mainly composed of flavonoid glycosides, such as kaempferol-3-O-rutinosid, isorhamnetin-3-O-rutinoside, and quercetin-3-O-rutinoside, which were also the predominant components in HRSF, could restore the TEER decrease induced by proinflammatory mediators. Thus, we can infer that flavonoid glycosides may play important roles in the intestinal barrier protection effect of HRSF. ## 3.3. HRSF Ameliorated the Alcohol-Induced Downregulation of TJ mRNA and Protein in Caco-2 Cells The intestinal barrier is formed mainly by TJs, which are multi-protein complexes that link adjacent epithelial cells [34]. Occludin is a tetraspan transmembrane protein, crucial in maintaining the structural integrity and barrier function of TJs [35]. ZO-1 is a cytoskeletal linker protein that provides a link between the transmembrane proteins such as occludin and the cytoskeletal actin [35]. Our results revealed that alcohol treatment markedly decreased mRNA expression of occludin and ZO-1 by $69\%$ and $62\%$ compared with the control group, respectively (Figure 3A,D). Meanwhile, it also reduced the protein levels of occludin and ZO-1 by $77\%$ and $79\%$, respectively (Figure 3B,C,E,F). This was also observed in a previous report, finding that $7.5\%$ alcohol could inhibit both the mRNA and protein expression of TJs [11]. Pre-incubation with HRSF prevented the mRNA low-regulation of TJs induced by alcohol in a dose-dependent manner, and 20 μg/mL HRSF increased the mRNA level of occludin and ZO-1 by 2.2- and 1.9-fold, respectively, compared with alcohol treatment alone. In parallel, HRSF have a similar impact on the protein expressions of TJs—20 μg/mL HRSF increased the protein level of occludin and ZO-1 by 2.6- and 2.9-fold in the alcohol group, respectively. Furthermore, individual treatment of HRSF showed no effect on the TJ mRNA and protein level (Figure 3). The above results indicated that HRSF could attenuate the intestinal barrier dysfunction induced by alcohol through upregulating occludin and ZO-1 expression both at transcriptional and translational levels. A similar result was also found in ethanol extract rich in polyphenols from Alnus japonica bark, showing a protective effect on the intestinal epithelium in mice with dextran sodium sulfate (DSS)-induced colitis and in HT-29 and Caco-2 cells [36]. Some other flavonoids, such as luteolin, puerarin, and flavonoid-rich propolis extracts, were also found to exert similar effects on occludin and ZO-1, which were downregulated by alcohol [32,37,38]. More importantly, the abundant flavonoids in HRSF, including quercetin-3-O-rutinoside and procyanidin B, have been found to prevent the decreasing of TJ proteins induced by dextran sulfate sodium or inflammatory agents [39,40]. Thus, quercetin-3-O-rutinoside and procyanidin B in HRSF may play key roles in ameliorating the inhibiting effects of alcohol on occludin and ZO-1 expression. ## 3.4. HRSF Attenuated the Alcohol-Induced Generation of ROS and MDA in Caco-2 Cells The gastrointestinal tract is an important source of ROS production in the body [41]. Although the intestinal epithelial barrier has a protective function, factors such as food, drugs, and exogenous chemicals will also lead to the production of excessive ROS, which will induce oxidative stress in the intestine [41]. Many researchers found that oxidative stress plays a preeminent role in the disruption of the intestinal barrier [42]. Meanwhile, cellular oxidative stress triggered by alcohol was considered to be involved in alcohol-induced intestinal barrier dysfunction [7]. Banan et al. [ 43] reported that 2.5–$15\%$ alcohol could disrupt intestinal barrier integrity and increase paracellular permeability of the Caco-2 monolayer by stimulating excessive production of ROS. The same phenomenon was observed in our results—the total amount of ROS in alcohol-stimulated cells was 1.9 times higher than the control. Furthermore, pretreatment with HRSF was able to significantly reduce the alcohol-induced ROS accumulation in a dose-dependent manner, and HRSF treatment alone had no influence on the ROS production (Figure 4A). Excessive generation of ROS induced lipid peroxidation, thus causing a large amount of MDA formation, which could react with protein and nucleic acid to make it lose its function [44]. In our results, the MDA production of Caco-2 cell treatment with alcohol was 3.6-fold compared with the control group (Figure 4B), which was in accordance with a previous report that alcohol could promote MDA accumulation [45]. Similar to the effect on ROS production, the MDA content of the HRSF pretreatment group decreased in a dose-dependent manner, reducing to $55.6\%$ in the alcohol alone treatment group at the concentration of 20 μg/mL. Since studies have validated that the accumulation of ROS and MDA could cause intestinal barrier hyperpermeability and disruption [46], the inhibition of ROS and MDA production might partially be the mechanism of HRSF to exert a protective effect on alcohol-induced intestinal barrier dysfunction. Procyanidin B2, which is a major component of HRSF, was reported to attenuate the oxidative stress induced by tert-butyl hydroperoxide in Caco-2 cells through reducing the generation of intracellular ROS [47]. Thus, we could infer that procyanidin B2 plays an important role in the inhibiting effect of HRSF on the alcohol-induced generation of ROS and MDA. ## 3.5. HRSF Restored the Alcohol-Induced Inhibition of the Antioxidant Defense System in Caco-2 Cells The organism has a complex antioxidant defense system that relies on endogenous non-enzymatic and enzymatic antioxidants in response to oxidative stress [48]. GSH is a tripeptide that can directly scavenge ROS without enzymatic help [49]. SOD is the first detoxification enzyme—it catalyzes the dismutation of superoxide anion to O2 and H2O2, which could be removed by the other enzymatic antioxidant systems [50]. Firm evidence has demonstrated that the administration of alcohol could decrease the antioxidant defense level [51]. In our experiment, $7.5\%$ alcohol treatment decreased the level of GSH by $58.1\%$ and suppressed the activity of SOD by $59.9\%$ compared with the control (Figure 5). Meanwhile, the levels of GSH were increased by $19.1\%$, $43.2\%$, and $79.1\%$ in the alcohol treatment group for pre-incubation with 5, 10, and 20 μg/mL HRSF (Figure 5A). In parallel, it also reversed the inhibiting effect of alcohol on SOD activity by $19.5\%$, $57.4\%$, and $82.9\%$, respectively (Figure 5B). However, no significant difference of GSH level and SOD activity was observed between cells challenged exclusively with HRSF and the control cells (Figure 5). Thus, it was obvious that HRSF could restore the redox homeostasis, which was inhibited by alcohol through increasing the GSH content and SOD activity in a dose-dependent manner. ## 3.6. HRSF Prevented Alcohol-Induced Inhibition of Nrf2 in Caco-2 Cells Nrf2 is a critical nuclear transcription regulator of cells for resisting oxidative stress [52]. It is usually located in the cytoplasm by combining with its negative mediator Keap1 (Kelch-like ECH-associated protein 1) in an inactive state. However, upon stimulation, Nrf2 is activated through dissociating from Keap1 and enters into the nucleus. Then, the activated Nrf2 binds to the antioxidant response element (ARE), which induces the downstream activation of the endogenous antioxidant system [53]. Evidence has shown that Nrf2 plays a key role in maintaining the integrity of the intestinal barrier through alleviating oxidative stress and regulating TJs expression [54]. To elucidate the protection mechanism of HRSF on the intestinal barrier against alcohol, the mRNA and protein levels of Nrf2 were examined. As shown in Figure 6, the mRNA and protein levels of Nrf2 were decreased by $51\%$ and $59\%$ in the alcohol treatment group compared with the control. A previous report also found a similar effect of alcohol on Nrf2 [32]. However, this inhibition induced by alcohol was mitigated by pre-incubation with HRSF in a dose-dependent manner. A 20 μg/mL HRSF pre-incubation before alcohol treatment was able to reverse the mRNA and protein expression of Nrf2 to nearly $80\%$ of the control group. Additionally, the HRSF-alone treatment did not have a significant effect (Figure 6). Previous studies demonstrated that some flavonoids usually show a protective effect on the intestinal barrier through scavenging of free radicals, but more recent studies indicated that they may also act as indirect antioxidants by triggering the endogenous antioxidant system to balance cellular redox homeostasis [32,55]. Evidence has confirmed that alcohol administration could disrupt the intestinal barrier by stimulating the generation of ROS, suppressing the antioxidant activity and causing the TJ dysfunction [2]. Considering that endogenous antioxidants and TJ expression were regulated by Nrf2 activation [54], and combined with the influence of HRSF on ROS, MDA, GSH content, SOD activity, and TJs expression, we could infer that HRSF could prevent alcohol-induced decreasing of TEER and increasing of paracellular permeability by activating the Nrf2-mediated pathway. Consistent with this, a dietary flavonoid, luteolin, also prevented alcohol-induced intestinal barrier dysfunction by activating the Nrf2-ARE indirect antioxidant system [32]. ## 4. Conclusions In summary, HRSF extracted from sea buckthorn seed residues were mainly com-posed of flavonoids. Flavonols, in the form of glycosides, were the predominant subclass. Kaempferol-3-O-rutinoside, isorhamnetin-3-O-rutinoside, kaempferol-3-O-robinoside-7-O-rhamnoside, isorhamnet-in-3-O-2G-rhamnosylrutinoside, and quercetin-3-O-rutinoside were the most abundant substances among them. Moreover, (−)-epigallocatechin belonging to the flavone subclass and B type of procyanidin were also presented at a significantly high amount. Meanwhile, HRSF had a dramatic protective effect against alcohol-induced intestinal barrier dysfunction, which was mainly achieved through alleviating oxidative stress and enhancing TJ expression. 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--- title: LCN2 secreted by tissue-infiltrating neutrophils induces the ferroptosis and wasting of adipose and muscle tissues in lung cancer cachexia authors: - Dong Wang - Xiaohui Li - Defeng Jiao - Ying Cai - Liting Qian - Yiqing Shen - Yichen Lu - Yonggang Zhou - Binqing Fu - Rui Sun - Zhigang Tian - Xiaohu Zheng - Haiming Wei journal: Journal of Hematology & Oncology year: 2023 pmcid: PMC10044814 doi: 10.1186/s13045-023-01429-1 license: CC BY 4.0 --- # LCN2 secreted by tissue-infiltrating neutrophils induces the ferroptosis and wasting of adipose and muscle tissues in lung cancer cachexia ## Abstract ### Background Cancer cachexia is a deadly wasting syndrome that accompanies various diseases (including ~ $50\%$ of cancers). Clinical studies have established that cachexia is not a nutritional deficiency and is linked to expression of certain proteins (e.g., interleukin-6 and C-reactive protein), but much remains unknown about this often fatal syndrome. ### Methods First, cachexia was created in experimental mouse models of lung cancer. Samples of human lung cancer were used to identify the association between the serum lipocalin 2 (LCN2) level and cachexia progression. Then, mouse models with LCN2 blockade or LCN2 overexpression were used to ascertain the role of LCN2 upon ferroptosis and cachexia. Furthermore, antibody depletion of tissue-infiltrating neutrophils (TI-Neu), as well as myeloid-specific-knockout of Lcn2, were undertaken to reveal if LCN2 secreted by TI-Neu caused cachexia. Finally, chemical inhibition of ferroptosis was conducted to illustrate the effect of ferroptosis upon tissue wasting. ### Results Protein expression of LCN2 was higher in the wasting adipose tissue and muscle tissues of experimental mouse models of lung cancer cachexia. Moreover, evaluation of lung cancer patients revealed an association between the serum LCN2 level and cachexia progression. Inhibition of LCN2 expression reduced cachexia symptoms significantly and inhibited tissue wasting in vivo. Strikingly, we discovered a significant increase in the number of TI-Neu in wasting tissues, and that these innate immune cells secreted high levels of LCN2. Antibody depletion of TI-Neu, as well as myeloid-specific-knockout of Lcn2, prevented ferroptosis and tissue wasting in experimental models of lung cancer cachexia. Chemical inhibition of ferroptosis alleviated tissue wasting significantly and also prolonged the survival of cachectic mice. ### Conclusions Our study provides new insights into how LCN2-induced ferroptosis functionally impacts tissue wasting. We identified LCN2 as a potential target in the treatment of cancer cachexia. ### Supplementary Information The online version contains supplementary material available at 10.1186/s13045-023-01429-1. ## Background Around $50\%$ of all cancer-related deaths worldwide can be attributed to a wasting condition known as “cachexia”. Cachexia is a complex syndrome that causes ongoing loss of adipose tissue and muscle, and which cannot be reversed with nutritional supplementation [1–3]. The progressive wasting that occurs in cancer cachexia has been suggested to be mediated by circulating factors [3] (e.g., proinflammatory cytokines, hormones, and metal ions), which can originate from various tissues and have different functions [3]. It has also been suggested that interventions for cachexia could be developed by targeting inflammatory processes (e.g., interleukin [IL]-6), although these efforts have not achieved the desired results [4, 5]. Thus, advances in the basic understanding of cachexia and effective targeting strategies are needed. The protein lipocalin 2 (LCN2) (also known as neutrophil gelatinase-associated lipocalin, siderocalin, or 24p3) functions as a mediator in several diseases associated with cachexia, including cancer, pneumonia, and kidney disease [6–9]. LCN2 has been demonstrated in mechanistic studies to function as an iron-regulatory protein under physiological and inflammatory conditions. In prokaryotes, LCN2 inhibits bacterial siderophores from acquiring iron, thus inhibiting bacterial growth [10]. In mammals, a study using a mouse model of leptomeningeal metastasis showed that cancer cells use lipocalin 2 to collect iron [6]. However, LCN2 in the hypothalamus interacts with the melanocortin-4 receptor, which has been shown to mediate anorexia and promote the loss of lean (skeletal muscle) and fat (adipose tissue) mass in cancer cachexia [11, 12]. Ferroptosis is a form of cell death. It is driven by iron-dependent phospholipid peroxidation and regulated by multiple metabolic and signaling pathways [13, 14]. Since its discovery in 2012, diverse injuries to many organs and various malignant lesions have been pathogenically associated with ferroptosis [13]. In recent years, multiple studies have reported links between ferroptosis and cancer progression. Egolf et al. [ 15] reported that knocking out the gene of an epigenetic regulator, myeloid/lymphoid or mixed-lineage leukemia 4, leads to ferroptosis inhibition and the development of pre-cancerous skin lesions in mice. Ma et al. [ 16] showed that cholesterol in the tumor microenvironment leads to increased cluster of differentiation (CD)36 expression in CD8+ T cells and causes these cells to take up polyunsaturated fatty acids and to initiate ferroptosis. Liao et al. [ 17] reported that CD8+ T cells orchestrate tumor ferroptosis via acyl-coa synthetase long chain family member-4. Although a link between cachexia and ferroptosis has not been reported, many therapy-resistant cancer cells (especially those prone to metastasis) are highly susceptible to ferroptosis [18]. Accordingly, it has been proposed that ferroptosis could be regulated by pharmacologic means to treat drug-resistant cancers [19]. In the present study, we found that the ferroptosis of adipose tissue cells caused tissue wasting in experimental models of lung cancer cachexia. Mechanistically, we demonstrated that wasting tissue had an increased number of tissue-infiltrating neutrophils (TI-Neu), and showed that these cells promoted ferroptosis and tissue wasting through LCN2 secretion. Moreover, we showed that the chemical inhibition of ferroptosis inhibited tissue wasting in experimental models of lung cancer cachexia. ## Mice 6–10 week-old male C57BL/6 (Cat# T002040), BALB/cJGpt-Foxn1nu/Gpt mice (Cat# D000521) were obtained from GemPharmatech (Nanjing, China). The Lcn2f/+ (Cat# NM-CKO-00134) and LysMcre mice (Cat# NM-KI-18018) were obtained from ShangHai Model Organisms. Lcn2f/f mice were crossed with LysMcre mice to obtain Lcn2f/f;LysMcre mice. All mice used were housed under specific pathogen-free conditions. All procedures involving experimental animals were approved by the Ethics Committee of the University of Science and Technology of China and were performed in accordance with the National Guidelines for Animal Usage in Research. ## Cell lines The Lewis lung carcinoma (LLC) and 3T3-L1 cell lines were obtained from The Cell Bank of Type Culture Collection of the Chinese Academy of Sciences (Cat# TCM 7 and Cat#SCSP-5038, respectively). Both cell lines were cultured in DMEM supplemented with $10\%$ FBS (HyClone), $1\%$ streptomycin and penicillin, and were maintained at 37 °C and $5\%$ CO2. The cell lines were routinely tested for mycoplasma using the TransDetect PCR mycoplasma detection kit (Transgen, Cat#FM311). 2 days after the 3T3-L1 cells had reached $70\%$ confluence, the cells were treated with 1 μM dexamethasone (Selleck, Cat#S1322), 5 μg/mL insulin (Novoprotein, Cat#P05019), 0.5 μM isobutylmethylxanthine (IBMX) (Selleck, Cat#S5836), and 1 μM rosiglitazone (Selleck, Cat#S2556). Another 2 days later, the cells were treated with 5 μg/mL insulin and 1 μM rosiglitazone. Starting on day 6, the cells were cultured in DMEM supplemented with $10\%$ FBS, $1\%$ streptomycin and penicillin, and treated with 200 ng/mL recombinant mouse LCN2 protein (Novoprotein, Cat#P11672) for 24 h. ## Human samples Serum samples from healthy donors and from lung cancer patients with and without cachexia were obtained from the First Affiliated Hospital of the University of Science and Technology of China. Patients were diagnosed with cachexia if they had lost > $5\%$ of their body weight over the past 6 months or had a body mass index (BMI) < 20, plus had three of the following criteria: anorexia, decreased muscle strength, fatigue, low fat-free mass index, or abnormal biochemistry results, including increased levels of inflammatory markers (e.g., CRP and IL-6), anemia, or low serum albumin. All samples were collected with the informed consent of the patients, and the experiments were approved by the Ethics Committee of the University of Science and Technology of China (2020-research-36). Details relating to the patients’ cancer type and cachexia are listed in Additional file 4: Table S5. ## Tumor models LLC-induced cachexia model was established as previous study described [20]. In brief, the male C57BL/6 mice were subcutaneously inoculated with LLC cells (5 × 106 per mouse). Mice were randomly divided into treatment groups while ensuring that the average body weight in each group was roughly the same. Mice which showed significant loss (> $20\%$) in body weight were defined as having cachexia. Then, they were euthanized and WAT and muscle tissues were harvested to further confirm the cachexia symptom. We comprehensively compared the cachexia phenotype of mice at 1,2,3 weeks and finally determined the analysis of samples on day 21. For lung cancer patient-derived xenograft (PDX)-induced cachexia, whether or not cachexia occurs is dependent upon the source of the tissue from the tumor patient. We established based on the 3#-Ade PDX of a lung adenocarcinoma. For specific steps and methods, PDX tumors in cold Dulbecco’s Modified Eagle’s Medium (DMEM) were minced into 30–50 mm3 fragments, and each fragment was subcutaneously transplanted into the dorsal flank of 6- to 10-week-old male BALB/cJGpt-Foxn1nu/Gpt mice. Body weight of these tumor-bearing mice were monitored regularly. Mice who showed significant loss (> $20\%$) in body weight were defined as having cachexia. Then, they were euthanized and WAT and muscle tissues were harvested to further confirm the cachexia symptom. ## Animal treatment protocol To deplete neutrophils, the male C57BL/6 mice were subcutaneously inoculated with LLC cells (5 × 106 per mouse) on day 0 and then intraperitoneally injected on days − 1, 1, 4, 6, 8, 11, 13, 15, 17, and 20 with a 100 μg dose of an anti-Ly6G (Bio X Cell, Cat# BE0075-1; RRID:AB_1107721) or an isotype IgG (Bio X Cell, Cat# BP0090, RRID: AB_2891360). To deplete LCN2, male C57BL/6 mice were subcutaneously inoculated with LLC cells (5 × 106 per mouse) on day 0 and then were intraperitoneally injected on days 4, 7, 11, 14, 17, and 20 with a 50 μg dose of an anti-LCN2 (Novus Biologicals, Cat# AF1857, RRID:AB_355022) or an isotype IgG (Bio X Cell, Cat# BP0090, RRID: AB_2891360). For deferoxamine (DFO) therapy, male C57BL/6 mice were subcutaneously inoculated with LLC cells (5 × 106 per mouse) on day 0 and then intraperitoneally injected with 15 mg/kg DFO (Selleck, Cat#S5742) on days 4, 7, 10, 13, 16, and 19. For liproxtatin-1 therapy, male C57BL/6 mice were subcutaneously inoculated with LLC cells (5 × 106 per mouse) on day 0 and then intraperitoneally injected with 10 mg/kg liproxtatin-1 (Selleck, Cat#S7699) daily between days 1 and 20. ## Lentivirus production and delivery The pCDH-CMV-MCS-EF1-Lcn2 (Sangon Biotech) or pCDH-CMV-MCS-EF1 vectors (YouBio, Cat# VT1479) were extracted using the Endo-Free Plasmid DNA Mini Kit II (Omega, Cat#D6950) and co-transfected with the pRSV-Rev (YouBio, Cat# VT1445), pLP/VSVG (YouBio, Cat# VT1491), and pNL-GFP-RRE (YouBio) plasmids into 293 T cells using the lipofectamine (Invitrogen, Cat#11668019) transfection method. Supernatants containing the LCN2-expressing or control lentiviruses were collected 48 and 72 h later and centrifuged at 50,000 × g for 2 h at 4 °C to purify the virus. For overexpression of Lcn2, 2 × 109 plaque-forming units (PFUs) of LCN2-expressing lentivirus were injected intravenously into C57BL/6 mice once per week. ## Flow cytometry Leukocytes were isolated from the epididymal and inguinal white adipose tissue (eWAT and iWAT, respectively), gastrocnemius skeletal muscle (Gast), bone marrow, and blood, as previously described [21, 22]. For intracellular staining, leukocytes were incubated with PMA (50 ng/mL), ionomycin (1 mg/mL), and monensin (10 ng/mL) for 4 h at 37 °C and $5\%$ CO2, followed by staining for surface markers for 30 min at 4 °C. Cells were fixed and then permeabilized with the Foxp3/Transcription Factor Staining Buffer and incubated with fluorescent antibodies for 30 min at 4 °C. Cells were acquired by flow cytometry (LSR II). For analysis of human blood samples, blood from cachectic cancer patients was centrifuged and lysed using red blood cell lysis buffer. Cells were then stained for surface markers for 30 min at 4 °C. Cells were fixed and then permeabilized with the Foxp3/Transcription Factor Staining Buffer and incubated with the fluorescent antibodies for 30 min at 4 °C. Cells were acquired by flow cytometry (LSR II). Antibodies and related materials used in this study are listed in Additional file 1: Table S1. Data analysis was performed using FlowJo 10 software. ## Isolation of adipocytes, neutrophils, and macrophages eWAT samples from mice were cut into pieces and digested in DMEM with collagenase I (1 mg/mL) while shaking at 220 rpm for 30 min at 37 °C. The suspensions were filtered through sieves, and the filtrates were centrifuged at 500 × g for 5 min to separate the suspended adipocytes from pelleted leukocytes. Neutrophils were purified using the Neutrophil Isolation Kit (Miltenyi Biotec, Cat#130-097-658). Macrophages were purified using a PE-F$\frac{4}{80}$ antibody (eBioscience, Cat#12-4801-80; RRID:AB_465922) and anti-PE Microbeads (Miltenyi Biotec, Cat#130-048-801). ## ELISA The concentrations of human IL-6, human CRP, human LCN2, and mouse LCN2 in cell culture supernatants and serum were measured by ELISA, according to the manufacturers’ instructions; the kits used are listed in Additional file 1: Table S1. ## Histological analysis Tissues were fixed overnight with $10\%$ neutral-buffered formalin, dehydrated, embedded in paraffin, and sectioned. The 4 μm slices were then stained with hematoxylin and eosin (H&E). ## Immunofluorescence Tissues were collected as described above. Tissues were fixed overnight with $10\%$ neutral-buffered formalin, dehydrated, embedded in paraffin, and sectioned into 4 μm slices. The slides were dewaxed, rehydrated, and subjected to heat-induced epitope retrieval, followed by incubation with $5\%$ goat serum for 1 h at room temperature to block non-specific antibody binding. Next, the sections were incubated with a primary anti-LCN2/NGAL antibody (Abcam, Cat# ab216462) overnight at 4 °C and then with an Alexa-Fluor-546-conjugated goat anti-rabbit IgG (5 μg/mL; Invitrogen, Cat# A-11010, RRID:AB_2534077) secondary antibody. Nuclear staining was performed using DAPI (5 min incubation). The stained sections were imaged using the LSM 880 Confocal Microscope (Zeiss, Jena, Germany) and analyzed with Image J software. ## Iron assay The concentrations of iron (Fe2+) in eWAT, iWAT, and Gast tissues were measured using the Iron Assay Kit (Sigma Aldrich, Cat#MAK025). eWAT, iWAT, and Gast tissues were homogenized in Iron Assay buffer by centrifugation at 16,000 × g for 10 min at 4 °C. The samples were then incubated with an iron reducer in a 96-well plate for 30 min at room temperature, and finally with an iron probe for 60 min at room temperature. Absorbance at 593 nm was measured using a microplate reader. ## Lipid reactive oxygen species (ROS) analysis Adipocyte lipid ROS production was measured using the Lipid Peroxidation Assay Kit (Abcam, Cat#ab243377). Adipocytes purified from eWAT or iWAT were stained with the Lipid Peroxidation Sensor for 30 min at 37 °C and analyzed immediately by flow cytometry (LSR II). The oxidized and non-oxidized lipids were detected on the FITC and PE channels, respectively. The FITC to PE mean fluorescence intensity (MFI) ratio was calculated for each sample. Data analysis was performed using FlowJo 10 software. ## The malondialdehyde (MDA) lipid peroxidation assay Lipid peroxidation was analyzed using the MDA Assay Kit (Sigma Aldrich, Cat#MAK085). Briefly, eWAT, iWAT, and Gast tissues were homogenized in MDA Lysis Buffer by centrifugation at 13,000 × g for 10 min. MDA in epididymal adipose tissue was mixed with TBA to generate the MDA-TBA adduct. Absorbance at 532 nm was measured using a microplate reader. ## Quantitative (q)PCR assays RNA was extracted from purified cells or frozen tissue samples using the TRIzol reagent (Invitrogen, Cat#15596018), glycogen (Thermofisher, Cat#AM9515), and sodium acetate (Thermofisher, Cat#R1181). RNA was reverse transcribed into cDNA using the M-MLV Reverse Transcriptase (Invitrogen, Cat#28025013). qPCR was then performed using the SYBR Green Premix Pro Taq HS qPCR Kit (Accurate biology, Cat#AG11701) on a LightCycler 96 instrument (Roche). PCR was performed using the 2 × TransTaq-T PCR SuperMix (Transgen, Cat#AS122). Relative mRNA levels were calculated using the 2−ΔΔCt method and normalized to actin mRNA levels. Target gene primers are shown in Additional file 1: Table S2. ## RNA sequencing (seq) Total RNA was extracted from the eWAT of cachectic and control mice using the miRNeasy Mini Kit and treated with DEPC-treated water. A total amount of 1 µg RNA per sample was used as input material for the RNA sample preparations. RNA libraries were prepared for sequencing using the NEBNext UltraTM RNA Library Prep Kit (Illumina). Clustering of the index-coded samples was performed on a cBot Cluster Generation System using TruSeq PE Cluster Kit v3-cBot-HS (Illumina), according to the manufacturer’s instructions. After cluster generation, the library preparations were sequenced on an Illumina Novaseq platform and 150 bp paired-end reads were generated. Raw data (raw reads in fastq format) were firstly processed through in-house perl scripts. Clean data were obtained by removing reads containing adapter and poly-N sequences, and low-quality reads. The reference genome and gene model annotation files were downloaded from the genome website directly. The reference genome index was built using Hisat2 software, and paired-end clean reads were aligned to the reference genome. FeatureCounts software was used to count the read numbers mapped to each gene. Fragments per kilobase of exon model per million reads mapped (FPKM) were calculated for each gene based on the length of the gene and the read counts mapped to this gene. Differential expression analysis of two groups was performed using the DESeq2 R package (1.16.1). The resulting P-values were adjusted using the Benjamini and Hochberg’s approach to control for the false discovery rate. Genes with a DESeq2-derived adjusted P-value < 0.05 and log2fold change (FC) ≥ 2 were labelled as differentially expressed genes (DEGs; listed in Additional file 5: Table S6). The DEG heatmap was analyzed using MEV software Gene Ontology (GO) enrichment analysis of DEGs was implemented using the clusterProfiler R package. GO terms with a corrected P-value < 0.05 were considered significantly enriched DEGs. We used clusterProfiler R package to test the statistical enrichment of DEGs in various KEGG enrichment pathways. For the Gene Set Enrichment Analysis (GSEA), the genes were ranked according to the degree of differential expression in the two samples being compared. The predefined Gene Sets were then tested to see if they were enriched at the top or bottom of the list. The RNA-seq data generated in this study were deposited in the GEO database repository and can be accessed using with the accession number GSE188479. ## Serum protein array The relative expression levels of human serum proteins in lung cancer patients with and without cachexia and in healthy donors were measured using the G-Series Human Cytokine Antibody Array 440 (RayBiotech). The relative expression levels of mouse serum proteins in cachectic and control mice were determined using the Quantibody® Mouse Cytokine Antibody Array 4000 (RayBiotech). Fluorescence signals were visualized in the Cy3 channel of a laser scanner. Data were extracted using the GAL file from www.RayBiotech.com/Gal-Files.html. Heatmaps of differentially expressed proteins (DEPs) were analyzed using MEV software. ## Western blotting Purified adipocytes, neutrophils, and macrophages were lysed in RIPA buffer (Beyotime, Cat#P0013B) containing protease inhibitors on ice for 30 min, followed by centrifugation at 16,000 × g for 10 min at 4 °C. Protein concentrations in the supernatants were measured using the BCA Protein Assay Kit (Thermofisher, Cat#23225), and 50 μg aliquots of proteins were incubated at 95 °C for 10 min and separated on SDS-PAGE gels. The proteins were transferred to PVDF membranes, which were blocked in a solution of $5\%$ milk in TBS buffer containing $0.1\%$ Tween-20 for 1 h at room temperature. The membranes were then incubated with primary antibodies overnight at 4 °C and subsequently with HRP-conjugated secondary antibodies for 1 h at room temperature. The protein bands were detected by chemiluminescence autoradiography. The antibodies used are listed in Additional file 1: Table S1. ## Statistical analysis All statistical analyses were performed using GraphPad Prism 5.0 (GraphPad Software, La Jolla, CA, USA). Results are presented as the mean ± standard error of the mean (SEM) and were compared using unpaired t-tests, one-way ANOVA, or two-way ANOVA. Mouse survival was estimated using the Kaplan–Meier method and compared using the log-rank test. Patient sample data were compared using the Wilcoxon signed rank test and Spearman’s rho was calculated as indicated. ## The LCN2 level is increased in wasting tissues in lung cancer cachexia We wished to identify the drivers of lung cancer cachexia in humans. Hence, we used a proteomics approach to evaluate the profiles of serum protein factors in cachectic lung cancer patients vs. non-cachectic lung cancer patients or healthy controls. This approach revealed aberrantly higher LCN2 levels in lung cancer patients with cachexia than in non-cachectic lung cancer patients or healthy controls (Fig. 1A, B). Levels of proinflammatory cytokines (e.g., IL-6 and CRP) were also higher in the serum of cachectic patients than in the other two groups, thereby indicating that cachexia was associated with a highly inflammatory environment (Fig. 1B and Additional file 1: Fig. S1A). Consistent with our data on the profiles of protein factors, ELISA results showed that serum concentrations of LCN2 were higher in cachectic lung cancer patients than in non-cachectic lung cancer patients or healthy controls (Fig. 1C). Moreover, we detected a negative correlation between the serum LCN2 concentration and the BMI and serum albumin level (Fig. 1D). We also detected a positive correlation between the serum LCN2 concentration and the IL-6 level and CRP level (Fig. 1D).Fig. 1LCN2 is highly expressed in wasting tissues in lung cancer cachexia. A. Volcano plot of differentially expressed proteins (DEPs) between the sera of cancer patients with cachexia and healthy controls, and non-cachectic lung cancer patients. $$n = 3$$ for each group. B. Heatmap showing the normalized levels of serum inflammatory proteins in lung cancer patients with cachexia vs. the levels in lung cancer patients without cachexia vs healthy controls. $$n = 3$$ for each group. C. Serum LCN2 concentrations in lung cancer patients with ($$n = 33$$) or without ($$n = 37$$) cachexia and in healthy controls ($$n = 32$$). D. Negative correlation between the LCN2 level, the body mass index (BMI), and the serum albumin concentration; and positive correlation between the LCN2 level and the serum concentrations of IL-6 and CRP. The Spearman correlation coefficient (r) and P-value are shown. E. Representative images of PDX-induced cachectic model mice and control mice. F. Representative images of iWAT, eWAT, and Gast. G. Serum LCN2 concentrations in mice with PDX-induced cachexia ($$n = 7$$) and control mice ($$n = 7$$). H. The Lnc2 mRNA levels in eWAT, iWAT, and Gast tissues for the indicated mice, assessed by qPCR. $$n = 5$$ per group. I–O. Experiments using a classic model of murine cachexia. Mice were inoculated with murine lung cancer cells (the LLC cell line) and monitored for up to 21 days. I. Representative images of cachectic and control mice. J. Weight of eWAT, iWAT, and Gast in these mouse groups. $$n = 6$$ per group. K. qPCR determination of Lcn2 mRNA levels in eWAT, iWAT and Gast. $$n = 5$$–8 per group. L. Representative H&E staining of iWAT, eWAT, and Gast. Scale bars, 100 μm. M. Volcano plot of DEPs in the sera of cachectic and control mice. N. Heatmap showing the normalized expression of the indicated inflammatory proteins in mice with cachexia compared with control mice. O. Monitoring of serum LCN2 concentrations for up to 21 days in mice bearing LLC tumors. $$n = 6$$ per group. Data are shown as the mean ± SEM. Statistical analyses were performed using one-way ANOVA (C, J, K, O) or unpaired Student’s t-tests (G, H) To pursue the basis of these clinical observations experimentally, we followed a previously reported [23] approach to establish a lung cancer PDX mouse model of cachexia (Fig. 1E, F). The tumor-tissue source of the PDX cachexia model was from a patient with lung adenocarcinoma (number: 3#-Ade) and all mice were able to develop cachexia. First, our ELISA data showed that the serum concentration of LCN2 was significantly higher in cachexia model mice than in control mice (i.e., mice without PDX-based induction of cachexia) (Fig. 1G). In addition, after sacrificing the mice, a qPCR analysis showed significantly increased Lcn2 expression in the tissues (eWAT, iWAT, and Gast) of cachexia model mice (Fig. 1H); eWAT, iWAT, and Gast undergo atrophy in cachexia [24, 25]. Subsequently, we induced a classic experimental model of murine cachexia [20] based on the subcutaneous inoculation of mice with LLC cells. As expected, $100\%$ of LLC tumor-bearing mice developed cachexia symptoms. Over the 3-week observation period, tumor-bearing (i.e., cachectic) mice experienced significant weight loss vs. control mice (Fig. 1I). Upon sacrificing the mice, we detected wasting of eWAT, iWAT, and Gast in cachectic mice (Fig. 1J and Additional file 1: Fig. S1B). We also detected dystrophic pathological changes in the iWAT (Fig. 1L, top), eWAT (Fig. 1L, middle), and Gast (Fig. 1L, bottom) of tumor-bearing mice during cachexia progression. A proteomics-based analysis of the profiles of serum protein factors in lung cancer cachectic mice showed significantly higher LCN2 levels in cachectic mice than in control animals (Fig. 1M, N). ELISA results further supported the significantly increased serum LCN2 concentrations in cachectic mice compared with those in control mice (Fig. 1O). In addition, we observed aberrantly increased LCN2 (mRNA and protein) levels during cachexia progression in the eWAT, iWAT, and Gast of cachectic mice (Fig. 1K and Additional file 1: Fig. S1E), as well as stronger LCN2 protein signals (immunofluorescence staining) in the iWAT and Gast of cachectic mice (Additional file 1: Fig. S1C, D). Collectively, these findings implied that the pathological changes which affected the wasting tissues of lung cancer cachexia models were related to the increased LCN2 level observed in lung cancer patients suffering from cachexia. ## Ferroptosis occurs in wasting tissues in lung cancer cachexia To assess the pathological transformations that occur in the microenvironment of local wasting tissue during lung cancer cachexia, we assessed changes in the wasting tissues of LLC-implanted cancer cachectic mice at the transcriptomic level. Among the 3,624 DEGs identified in our comparison of the eWAT from cachectic mice and control mice, 1770 showed upregulated expression and 1,854 had downregulated expression in cachectic mice (Fig. 2A). Enrichment analysis using the KEGG database revealed that the DEGs with upregulated expression were enriched in pathways such as “fatty acid degradation”, “inflammation”, and “ferroptosis” (Fig. 2B).Fig. 2Ferroptosis occurs in the wasting tissues of mice with lung cancer cachexia. A. Mice were inoculated with LLC cells to induce the murine cachexia, and eWAT tissues from cachectic and control mice were harvested on day 21 and analyzed by RNA-seq; this panel shows a heatmap of differentially expressed genes (DEGs) in the eWAT tissue. B. KEGG pathway enrichment analysis. C. Heatmap analysis showing the normalized expression of ferroptosis-related genes. D. Gene set enrichment analysis (GSEA) showing that metabolic processes involving reactive oxygen species were enriched in the cachectic mice compared with controls. E. qPCR analysis of the indicated ferroptosis gene mRNA levels in the eWAT, iWAT, and Gast. $$n = 6$$ per group. F. Chemiluminescence analysis of Fe2+ concentrations in the eWAT, iWAT, and Gast. $$n = 5$$–6 per group. G. Chemiluminescence analysis of MDA concentration in the eWAT, iWAT, and Gast. $$n = 5$$–6 per group. Data are shown as the mean ± SEM. Statistical analyses were performed using unpaired Student’s t-tests (E, F, G) Ferroptosis is a form of cell death that results from iron-dependent accumulation of lipid peroxide and ROS production [26, 27]. GSEA showed enrichment of the ROS signature (Fig. 2D). Pursuing ferroptosis-related genes specifically, RNA-seq data and follow-up qPCR analysis showed that expression of Ncoa4, Slc39a8, Slc3a2, Pcbp2, Slc39a14, and Sat1 [28] was significantly higher in the wasting eWAT, iWAT, and Gast of cachectic mice than in the equivalent tissues in control mice (Fig. 2C, E). Ptgs2 expression [29] was significantly higher in the wasting eWAT of tumor-bearing mice than in the eWAT of control animals (Fig. 2C). Tissues were harvested 21 days after tumor inoculation. We also assessed the known biochemical features of ferroptosis [26]. Levels of Fe2+, MDA, and lipid ROS were significantly higher in wasting eWAT, iWAT, and Gast than in the equivalent normal tissues (Fig. 2F, G and Additional file 1: Fig. S2B). Finally, immunofluorescence and flow-cytometry analyses of fresh adipocytes purified from wasting eWAT and normal eWAT showed that wasting eWAT exhibited increased lipid peroxidation (Additional file 1: Fig. S2C) and had a higher proportion of adipocytes (Additional file 1: Fig. S2A) than those in the eWAT of controls. We also examined a lung cancer PDX-induced mouse model of cachexia, and found that Fe2+ and MDA levels were significantly higher in wasting eWAT, iWAT, and Gast than in normal tissues (Additional file 1: Fig. S2D, E). We also detected significantly higher expression of ferroptosis-related genes in the wasting eWAT, iWAT, and Gast of cachectic mice than in the equivalent normal tissues of control animals (Additional file 1: Fig. S2F). These results suggested that, in lung cancer cachexia, wasting tissues undergo ferroptosis. ## Exogenous LCN2 induces ferroptosis and tissue wasting in mice To investigate the potential impact of the LCN2 level on ferroptosis, we conducted in vitro experiments with 3T3-L1 adipocytes [30]. Briefly, addition of the recombinant murine Lcn2 protein to 3T3-L1 cells increased the extent of cell death, lipid peroxidation (Fig. 3A and Additional file 1: Fig. S3E), and expression of ferroptosis-related genes (Ncoa4, Slc3a2, Slc39a8, Slc39a14, Pcbp2, Ptgs2, and Sat1) significantly (Fig. 3B and Additional file 1: Fig. S3H). We also treated 3T3-L1 cells with a ferroptosis inhibitor, liproxstatin-1 (after treatment with recombinant Lcn2 protein), and found that it alleviated the increase in MDA or lipid ROS levels triggered by Lcn2 (Additional file 1: Fig. S3F, G). These results indicated that LCN2 could induce ferroptosis in cultured adipocytes. Fig. 3LCN2 overexpression promotes tissue ferroptosis and wasting. A, B. 3T3-L1 cells were treated with LCN2 for 24 h. A. Representative immunofluorescence staining of lipid peroxidation. Scale bars, 50 μm. B. qPCR analysis of mRNA levels of the indicated ferroptosis-related genes in 3T3-L1 cells. $$n = 3$$ per group. C–J. Mice were injected intravenously with the control or LCN2-overexpressing lentivirus, and tissues were harvested on day 21. C. qPCR measurements of Lcn2 mRNA levels in the eWAT, iWAT, and Gast. $$n = 5$$–6 per group. D. MDA concentrations in the eWAT, iWAT, and Gast, assessed using chemiluminescence. $$n = 5$$–6 per group. E. ELISA evaluation of serum LCN2 concentrations. $$n = 6$$ per group. F. Fe2+ concentrations in eWAT, iWAT, and Gast, assessed by chemiluminescence. $$n = 5$$–6 per group. G. Representative images of mice injected with the control or LCN2-overexpressing lentivirus. Body weights of mice injected with the control or LCN2-overexpressing lentivirus. $$n = 5$$ per group. H. Representative image of the eWAT, iWAT, and Gast from mice injected with the control or LCN2-overexpressing lentivirus. I. Weights of the eWAT, iWAT, and Gast isolated from mice injected with the control or LCN2-overexpressing lentivirus. $$n = 5$$ per group. J. Representative H&E staining of the iWAT, eWAT, and Gast from mice injected with the control or LCN2-overexpressing lentivirus. Scale bars, 100 μm. Data are shown as the mean ± SEM. Statistical analyses were performed using the unpaired Student’s t-tests (B–E, F, G, I) Next, we evaluated the potential functional contributions of LCN2 to ferroptosis and tissue wasting in vivo by developing mice overexpressing LCN2. To achieve this, we injected (i.v.) wild-type C57BL/6 mice with 2 × 109 PFUs of an LCN2-expressing lentivirus [31]. mRNA expression of Lcn2 was increased significantly in the eWAT, iWAT, and Gast of mice injected with the LCN2-expressing lentivirus compared with that in the tissues of mice injected with the empty control lentivirus (Fig. 3C); protein expression of LCN2 was also increased significantly, considerably exceeding the serum concentration of endogenous LCN2 (Fig. 3E). In addition, the concentrations of MDA (Fig. 3D) and Fe2+ (Fig. 3F) were significantly higher in the eWAT, iWAT, and Gast of LCN2-overexpressing mice than in those of control mice. Moreover, expression of ferroptosis-related genes was increased significantly in the eWAT, iWAT, and Gast of LCN2-overexpressing mice than in those of control mice (Additional file 1: Fig. S3D). LCN2-overexpressing mice also showed a significant reduction in body weight (Fig. 3G), as well as wasting of adipose tissues (eWAT and iWAT) and skeletal muscle (Gast) (Fig. 3H, I). Histology revealed the clearly aberrant morphology of the iWAT and eWAT of LCN2-overexpressing mice (Fig. 3J); the skeletal muscle of these mice was also pathologically altered markedly (Fig. 3J). In addition, the eWAT and iWAT of mice injected with the LCN2-expressing lentivirus expressed higher levels of thermogenic and lipolytic genes (Additional file 1: Fig. S3A, B), whereas the Gast of these mice expressed higher levels of genes involved in protein degradation (Additional file 1: Fig. S3C). Collectively, our results from these narrowly focused experimental models revealed that LCN2: (i) induced ferroptosis in adipocytes; (ii) contributed specifically and functionally to the observed wasting of WAT and muscle observed initially in cachexia model mice. ## TI-Neu cells are a major source of LCN2 in wasting tissues in lung cancer cachexia Next, we investigated the source of LCN2 in wasting tissues. Notably, LCN2 is a neutrophil gelatinase-associated lipocalin, and has been reported to act as a pleiotropic mediator in several inflammatory and metabolic diseases [32]. The eWAT, iWAT, and Gast of LLC lung cancer cachectic mice had significantly higher numbers of TI-Neu cells (Fig. 4A and Additional file 1:Fig. S4B, E), macrophages, and myeloid-derived suppressor cells (MDSCs) that the equivalent tissues of control mice (Fig. 4A and Additional file 1:sFig.4C, D, E).Fig. 4TI-Neu cells are a major source of LCN2 in wasting tissues in lung cancer cachexia. A. Mice were inoculated with LLC cells to induce cachexia. Proportions of neutrophils, macrophages, and MDSCs in the eWAT, iWAT, and Gast were assessed. $$n = 6$$ per group. B. Proportions of neutrophils, macrophages, and MDSCs in the eWAT, iWAT, and Gast of PDX-induced cachectic model mice and non-model control mice. $$n = 6$$ per group. C. MFI values for LCN2 in the TI-Neu cells, macrophages, and MDSCs of the eWAT, iWAT, and Gast. $$n = 6$$ per group. D. Flow cytometry LCN2 MFI values in the TI-Neu cells, macrophages, and MDSCs of the eWAT, iWAT, and Gast of PDX-induced cachectic model mice and control mice. $$n = 6$$ per group. E–F. Mice were inoculated with LLC cells and the tissues were harvested on day 21. Adipocytes, TI-Neu cells, and macrophages were purified from the eWAT. E. Western blotting for LCN2 in purified adipocytes, TI-Neu cells, and macrophages. F. LCN2 concentrations in the supernatants of purified adipocytes, TI-Neu cells, and macrophages (cultured in medium for 18 h). Adi, adipocyte. Mφ, macrophage. G. Proportions and absolute numbers of neutrophils in the peripheral blood of lung cancer patients with ($$n = 34$$) or without ($$n = 34$$) cachexia, and healthy controls ($$n = 32$$). H. MFI values for LCN2 in neutrophils, monocytes, NK cells, T cells, and B cells obtained from the peripheral blood of lung cancer patients with cachexia ($$n = 26$$). I. Negative correlation between the number of neutrophils and BMI, and positive correlation between the number of neutrophils and the LCN2 level. Data are shown as the mean ± SEM. Statistical analyses were performed using one-way ANOVA (F–H) or unpaired Student’s t-tests (A–D) We also conducted flow-cytometry analyses of the eWAT, iWAT, and Gast from LLC lung cancer cachectic model mice: TI-Neu cells, macrophages, and MDSCs all expressed LCN2 protein. Moreover, the LCN2 level was higher in the TI-Neu cells derived from the wasting eWAT, iWAT, and Gast of cachectic mice than from control animals. In contrast, the LCN2 levels in eWAT-, iWAT-, and Gast-derived MDSCs and macrophages did not differ between the experimental lung cancer cachexia mice and controls (Fig. 4C). Notably, the LCN2 level was very low in the lymphocytes (e.g., T, B, and natural killer [NK] cells) of cachectic mice and control mice (Additional file 1: Fig. S4G). Furthermore, neutrophils were the largest population of LCN2-positive immune cells (LCN2 + CD45 + cells) (Additional file 1: Fig. S4H). Co-localization of LCN2 and neutrophils (MPO +) in the adipose tissue of cachectic mice was observed (Additional file 1: Fig. S4I), indicating that TI-Neu cells were the major source of LCN2. To garner additional support for the conclusions inferred from the LLC cancer cachexia model, we examined a lung cancer PDX-induced mouse model of cachexia. The two models elicited similar results (Fig. 4B, D). LCN2 secretion from bone-marrow neutrophils has been reported to mediate appetite suppression during pancreatic cancer cachexia [12]. Similarly, we found that the LCN2 levels in neutrophils derived from the bone marrow and blood were higher in cachectic mice than in control animals (Additional file 1: Fig. S4F). Our evaluation of cachectic mice also showed that the MFI of LCN2 was much higher in WAT neutrophils than in neutrophils originating from the bone marrow or blood (Additional file 1: Fig. S4F). Next, we measured the LCN2 levels in adipocytes, TI-Neu cells, and macrophages purified from the wasting adipose tissue of cachectic mice. The LCN2 level was markedly higher in the TI-Neu cells than in the adipocytes or macrophages of wasting eWAT (Fig. 4E). Then, we cultured these three cell types individually. ELISA of the culture supernatants showed that the LCN2 level was obviously increased in TI-Neu cells but not in the other two cell types (Fig. 4F). Analysis of the peripheral blood from lung cancer cachectic patients revealed two main phenomena. First, the proportion and absolute number of circulating neutrophils were higher in cancer patients with cachexia than in cancer patients not suffering from cachexia or in healthy controls (Fig. 4G). Second, the neutrophils of cancer patients with cachexia had significantly higher LCN2 levels than other immune cells (monocytes, T cells, NK cells, and B cells) (Fig. 4H). Furthermore, the number of neutrophils in the blood of cancer patients suffering from cachexia correlated positively with their LCN2 level, but negatively with their BMI (Fig. 4I). Taken together, these results showed that TI-Neu cells from wasting tissues were a major source of LCN2. ## Neutrophil depletion prevents ferroptosis and tissue wasting in lung cancer cachexia To determine if elimination of TI-Neu cells from mice with lung cancer cachexia could alleviate tissue wasting and ferroptosis, we treated model mice with a neutrophil-depleting, anti-Ly6G antibody (Additional file 1: Fig. S5A). As expected, treatment with the anti-Ly6G antibody reduced the number of neutrophils in the blood and adipose tissue of model mice significantly compared with treatment with the control (IgG) (Additional file 1: Fig. S5B–E). Strikingly, the body weight of neutrophil-depleted lung cancer cachectic model mice was significantly higher than that of IgG control-treated animals; there was no significant difference between the body weight of control mice and neutrophil-depleted cachexia model animals (Fig. 5A).Fig. 5Depletion of TI-Neu cells prevents tissue ferroptosis and wasting in lung cancer cachexia. Mice were inoculated subcutaneously with LLC cells to induce cachexia and were treated with 100 μg anti-Ly6G antibody (or IgG control) three times per week between days 1 and 20; the tissues were harvested on day 21. A. Representative images (left) and body weights (right) of the lung cancer cachexia model mice. $$n = 5$$ per group. B. Representative images (left) and weights (right) of the eWAT. $$n = 5$$ per group. C. Representative images (left) and weights (right) of the iWAT. $$n = 5$$ per group. D. Representative H&E staining of the iWAT, eWAT, and Gast. Scale bars, 100 μm. E. qPCR analysis of Lcn2 mRNA levels in the eWAT, iWAT, and Gast. $$n = 5$$ per group. F. Serum LCN2 concentrations. $$n = 5$$ per group. G. MDA concentrations in the eWAT. $$n = 5$$–6 per group. H. MDA concentrations in the iWAT and Gast. $$n = 6$$ per group. Data are shown as the mean ± SEM. Statistical analyses were performed using the one-way ANOVA (A–C, E–H) Neutrophil depletion alleviated the wasting of eWAT and iWAT significantly (Fig. 5B, C). Moreover, histology showed that neutrophil depletion alleviated the pathological morphology of iWAT, eWAT, and Gast observed in cachexia model mice (Fig. 5D) and also reduced the LCN2 levels in the eWAT, iWAT, Gast, and serum significantly (Fig. 5E, F). Neutrophil depletion reduced the MDA concentrations in iWAT, eWAT, and Gast significantly (Fig. 5G, H) and alleviated the increase in Fe2+ concentrations in iWAT and Gast compared with those in IgG control-treated mice (Additional file 1: Fig. S5F). Next, we measured mRNA expression of ferroptosis-related genes. The increase in expression of these genes was alleviated significantly in the eWAT, iWAT, and Gast of anti-Ly6G-antibody-treated mice compared with those in IgG control-treated animals (Additional file 1: Fig. S5G). Collectively, these results showed that the targeted elimination of neutrophils alleviated tissue wasting in lung cancer cachexia model mice significantly. ## LCN2 knockout reduces ferroptosis and tissue wasting in lung cancer cachexia Next, we examined the impact of knocking out LCN2 by generating myeloid-specific Lcn2 knockout (Lcn2f/fLysMcre) mice (Additional file 1: Fig. S6A) and inducing lung cancer cachexia in these mice and their control (Lcn2f/+LysMcre) littermates. Induction of lung cancer cachexia led to the expected increase in protein expression of LCN2 in the serum of Lcn2f/+LysMcre mice, but not in Lcn2f/fLysMcre mice (Additional file 1: Fig. S6B). As expected, Lcn2f/+LysMcre, but not Lcn2f/fLysMcre mice, exhibited weight loss (Fig. 6A), significant wasting of eWAT, iWAT, and Gast (Fig. 6B–D), and pathological morphology of eWAT, iWAT, and Gast (Fig. 6E). Examination of the biochemical features of ferroptosis showed that Fe2+ and MDA concentrations were increased significantly in the eWAT, iWAT, and Gast of Lcn2f/+LysMcre, but not in those of Lcn2f/fLysMcre mice (Fig. 6F, G). Moreover, analyses of the lipid ROS of fresh adipocytes purified from eWAT showed lipid ROS levels to be increased significantly in Lcn2f/+LysMcre mice, but not in Lcn2f/fLysMcre mice (Additional file 1: Fig. S6H). Taken together, these findings suggested that myeloid-derived LCN2 contributed specifically to the ferroptotic tissue wasting observed in lung cancer cachexia model mice. Fig. 6LCN2 knockout reduces tissue ferroptosis and wasting in lung cancer cachexia. A–G. Lcn2f/+;LysMcre and Lcn2f/f;LysMcre mice were injected with LLC cells to induce the lung cancer cachexia model, and the tissues were harvested on day 21. A. Representative images and body weights of Lcn2f/+;LysMcre and Lcn2f/f;LysMcre lung cancer cachexia model mice and their respective controls. Representative images of eWAT, iWAT (B), and Gast (C) from the Lcn2f/+;LysMcre and Lcn2f/f;LysMcre lung cancer cachexia model mice and controls. $$n = 5$$–6 per group. D. Weights of the eWAT, iWAT, and Gast of Lcn2f/+;LysMcre and Lcn2f/f;LysMcre lung cancer cachexia model mice and controls. $$n = 5$$–6 per group. E. Representative images of the iWAT, eWAT, and Gast of Lcn2f/+;LysMcre and Lcn2f/f;LysMcre lung cancer cachexia model mice and controls. H&E staining; scale bars, 100 μm. Analysis of (F) Fe2+ concentration, (G) MDA concentration in the eWAT, iWAT, and Gast of Lcn2f/+;LysMcre and Lcn2f/f;LysMcre lung cancer cachexia model mice and controls. $$n = 5$$–6 per group. H–M. Lung cancer cachexia model mice were injected with 50 μg IgG or anti-LCN2 antibody every 3 days between days 4 and 20, and the tissue samples were harvested on day 21. H. Representative images (left) and body weights (right) of control mice, neutrophil-depleted lung cancer cachexia model mice, and control IgG-injected lung cancer cachexia model mice. $$n = 5$$ per group. I. Representative images (left) and weights (right) of the eWAT. $$n = 5$$ per group. J. Representative images (left) and weights (right) of the iWAT. $$n = 5$$ per group. K. Fe2+ ($$n = 5$$–6 per group) concentrations in the eWAT, iWAT, and Gast. L. MDA ($$n = 6$$ per group) concentrations in the eWAT, iWAT, and Gast. M. Kaplan–Meier analysis of mouse survival ($$n = 10$$ per group), with comparisons performed using the log-rank test. Statistical analyses were performed using the one-way ANOVA (A, D, F, G, H–L) or the log-rank test (O). Data are shown as the mean ± SEM Given our findings regarding LCN2 knockout, next we explored if treating mice with an anti-LCN2 antibody to achieve targeted elimination of LCN2 conferred similarly protective effects in lung cancer cachexia model mice (Additional file 1: Fig. S6C). Upon administration of the anti-LCN2 antibody (or IgG control; both 50 μg per mouse), we observed the expected reductions in eWAT, iWAT, and serum LCN2 level (Additional file 1: Fig. S6D–E). Furthermore, treatment with the anti-LCN2 antibody alleviated the pathogenic phenotypes of lung cancer cachexia model mice, including the reduction in body weight (Fig. 6H) and eWAT/iWAT wasting (Fig. 6I, J). Histology showed that treatment with anti-LCN2 antibody also alleviated the pathological morphology of iWAT and Gast of cachexia model mice (Additional file 1: Fig. S6F, G). Biochemical analysis showed that lung cancer cachexia model mice treated with anti-LCN2 antibody had significantly lower Fe2+ and MDA concentrations in their eWAT, iWAT, and Gast than those in controls (Fig. 6K, L). Moreover, anti-LCN2 antibody-treated mice lived significantly longer (Fig. 6M). Collectively, the findings from studies on myeloid-specific knockout and therapy with anti-LCN2 antibody demonstrated that targeted elimination of LCN2 alleviated ferroptosis and tissue wasting significantly in mice with lung cancer cachexia. ## Chemical inhibition of ferroptosis reduces tissue wasting in lung cancer cachexia Until this point, we had focused on the nature of the aberrantly high LCN2 level in lung cancer cachexia model mice and LCN2-mediated induction of ferroptosis in wasting tissues. We were also interested in whether disrupting ferroptosis could protect mice directly from developing lung cancer cachexia. To this end, we first treated lung cancer cachectic model mice with a ferroptosis inhibitor: liproxstatin-1 (10 mg per kg body weight, delivered by intraperitoneal injection daily between days 1 and 20). Histology showed that liproxstatin-1 treatment alleviated the pathological morphology of the iWAT, eWAT, and Gast of cachexia model mice (Fig. 7A). Liproxstatin-1 treatment also alleviated the loss in body weight of cachectic mice (Additional file 1: Fig. S7D). As expected, liproxstatin-1 treatment reduced the Fe2+ concentration in eWAT, iWAT, and Gast (Fig. 7B). Liproxstatin-1 treatment also led to a significant reduction in the MDA and lipid ROS levels in eWAT and iWAT (Fig. 7C, D), as well as a significant reduction in expression of ferroptosis-related genes (Ncoa4, Slc39a8, Slc3a2, Slc39a14, Pcbp2, and Sat1) in eWAT, iWAT, and Gast (Additional file 1: Fig. S7C).Fig. 7Chemical inhibition of ferroptosis reduces tissue wasting in lung cancer cachexia. A–D. Lung cancer cachexia model mice were treated with the ferroptosis inhibitor, liproxtatin-1 (10 mg per kg body weight, delivered by intraperitoneal injection daily between days 1 and 20), and the tissues were harvested on day 21. A. Representative H&E staining of the iWAT, eWAT, and Gast. Scale bars, 100 μm. B. Fe2+ concentration in the iWAT, eWAT, and Gast. $$n = 5$$–6 per group. C. Flow cytometry analyses of the relative lipid ROS levels in the adipocytes of the eWAT and iWAT. A PE channel was used to detect non-oxidized lipids and a FITC channel was used to detect oxidized lipids. The FITC to PE MFI ratio was calculated as the relative lipid ROS value. $$n = 5$$–6 per group. D. Chemiluminescence analysis of the MDA concentration in the iWAT, eWAT, and Gast. $$n = 6$$ per group. E–L. Lung cancer cachexia model mice were treated with the ferroptosis inhibitor, DFO (15 mg per kg body weight, delivered by intraperitoneal injection every 3 days between days 4 and 19), and the tissues were harvested on day 21. ( E, F) Concentrations of (E) Fe2+ and (F) MDA in the eWAT. $$n = 5$$ per group. ( G, H) Flow cytometry analyses of (G) relative lipid ROS and (H) the percentage of 7-AAD+ adipocytes in the eWAT. $$n = 5$$ per group. I. Representative images (left) and body weights (right) of the different groups of mice. $$n = 5$$ per group. J. Representative images (left) and weights (right) of the eWAT and iWAT. $$n = 5$$ per group. K. Representative H&E staining of the iWAT. Scale bars, 100 μm. L. Kaplan–Meier analysis of mouse survival ($$n = 10$$ per group), with comparisons performed using the log-rank test. Statistical analyses were performed using the one-way ANOVA (B–I, J). Data are shown as the mean ± SEM We also treated lung cancer cachexia model mice with an alternative ferroptosis inhibitor: DFO (15 mg per kg body weight, delivered by intraperitoneal injection) [33] (Additional file 1: Fig. S7A). Beyond the expected reduction in the Fe2+ concentration in eWAT (Fig. 7E), DFO treatment induced four phenotypes in lung cancer cachexia model mice: (i) reduced MDA and lipid ROS levels in eWAT (Fig. 7F, G); (ii) alleviation of weight loss and wasting of eWAT and iWAT (Fig. 7I, J); (iii) alleviation of the pathological morphology of iWAT (Fig. 7K); (iv) reduced expression of ferroptosis-related genes (Ncoa4, Slc39a8, Slc3a2, Slc39a14, Pcbp2, and Sat1) (Additional file 1: Fig. S7B). These four phenotypes were very similar to those of mice subjected to myeloid-specific LCN2 knockout or treatment with anti-LCN2 antibody. DFO treatment prolonged the survival of lung cancer cachexia model mice significantly (Fig. 7L) and reduced the percentage of dead (7AAD+) adipocytes in their eWAT significantly (Fig. 7H). The results of experiments on DFO treatment showed that disrupting ferroptosis protected mice from cachexia directly. Thus, the antibody-based disruption of LCN2-induced ferroptosis or chemical inhibition of ferroptosis represent potentially promising strategies for treating the tissue wasting and multiple other deleterious aspects of cachexia. ## Discussion and conclusions The incidence of cachexia among cancer patients is relatively high, especially those with cancer of the pancreas, gastrointestinal tract, colon, or lung. Cachexia symptoms can appear early, even if the primary tumor is localized. These systemic changes affect many peripheral tissues that are not proximal to the tumor (e.g., the muscles essential for breathing, moving, chewing, and swallowing food) and are often detrimental to the host [34]. Furthermore, weakened muscle and adipose tissue reduce the tolerance of cancer cachexia patients to anti-tumor therapies. For instance, weakening of the heart muscles and diaphragm muscles often leads to premature death from heart failure or respiratory failure [2, 35]. Frustratingly, efficacious treatment for cancer cachexia is lacking, despite > 100 clinical trials of mediators developed to treat this condition [2]. In addition, many reported cachexia mediators target tumor metastasis-related cachexia but very few target cachexia arising from localized tumors or early tumors. This scenario is suboptimal given that the mediators of cachexia may differ between metastatic tumors and localized primary tumors [2, 36]. We established, by experimental means, that the LCN2-induced ferroptosis of tissue parenchymal (e.g., adipose) cells is one of the causes of tissue wasting in mice with non-metastatic lung cancer cachexia. Ferroptosis has been demonstrated to induce organ injury and various degenerative changes in diverse diseases [19]. For example, there is strong evidence that ferroptosis contributes to ischemia–reperfusion injury, including stroke and ischemic disease of the heart, liver, and kidney [37]. DFO (Desferal®) is a drug approved for the treatment of acute iron overdose and chronic iron overload resulting from repeated blood transfusions. It is an iron-chelating agent that binds excess free iron and forms a stable complex that inhibits ferroptosis [38]. We found that the DFO-mediated inhibition of ferroptosis significantly and reduced wasting of adipose tissue in a mouse model of lung cancer cachexia. In addition, DFO treatment lengthened the survival of these mice significantly. LCN2 is a mediator implicated in several diseases: cachexia, cancer, pneumonia, and kidney disease [6–9]. There are two forms of LCN2 under physiological conditions. It is now clear that the function of the iron-free form of LCN2 is distinct from that of the iron-loaded from [39]. Meier et al. [ 40] reported that iron-loaded LCN2 promoted the growth and progression of renal cell carcinoma, whereas iron-free LCN2 exerted anti-tumoral activity; however, the mechanistic details of cancer-related LCN2 signaling are not known. Iron-free LCN2 has been used as a marker for renal regeneration [41]. Incidentally, the exogenous LCN2 used in our study was the iron-free form. Therefore, further studies assessing the impact of the iron-loaded form of LCN2 on ferroptosis are needed to deepen our understanding of the diverse functions of LCN2. LCN2-related signaling varies among diseases affecting different organs. Liu et al. [ 42] reported that the stress-responsive transcription factor nuclear protein-1 transactivates LCN2 in pancreatic cancer cells which, in turn, induce ferroptosis resistance. Yao et al. [ 43] reported that an leukemia inhibitory factor receptor (LIFR)/nuclear factor-kappa B (NF-κB)/LCN2 axis controls liver tumorigenesis and vulnerability to ferroptosis. They showed that loss of LIFR activates NF-κB signaling, thereby leading to upregulation of expression of the iron-sequestering cytokine LCN2 which, in turn, depletes iron and renders liver cells insensitive to ferroptosis inducers. LCN2 has been reported to mediate appetite suppression during pancreatic cancer cachexia [12]. We found that LCN2 overexpression in healthy mice or LCN2 depletion in LLC tumor-bearing mice did not influence the food intake of animals significantly. We established that TI-Neu-derived LCN2 induced the ferroptosis of adipocytes directly, leading to the wasting of adipose tissues. A therapeutic antibody targeting LCN2 prolonged the survival of tumor-bearing mice effectively and prevented wasting of adipose tissue and muscle in these animals. In addition, LCN2 expression was upregulated in lung cancer patients and was associated with cachexia progression. Collectively, these findings indicate that LCN2 may represent a valuable target in the treatment of cachexia caused by lung cancer and other types of cancer. Wang and colleagues suggested that LCN2 knockdown protected a lipopolysaccharide- induced model of acute respiratory distress syndrome via inhibition of ferroptosis-related inflammation and oxidative stress by inhibiting the mitogen-activated protein kinase/extracellular signal-regulated kinase (MAPK/ERK) pathway [44]. Huang et al. [ 45] found that signal transducer and activator of transcription-3 (STAT3)-mediated lysosomal cell death promoted ferroptosis in PDAC. They found the MEK-ERK pathway promotes STAT3 activation in ferroptosis, and that STAT3 contributes to erastin-induced ferroptosis. Wang and colleagues [46] reported the crosstalk of Lcn2/Janus kinase 2 (JAK2)-STAT3 in neurotoxic microglia and astrocytes. We also found the JAK-STAT pathway to be enriched in the eWAT of cachectic mice (Fig. 2B). Thus, LCN2 may induce ferroptosis by activating the STAT3 pathway. ## Supplementary Information Additional file 1. Fig. S1: LCN2 levels are increased in the wasting tissues in murine lung cancer cachexia. Fig. S2: Ferroptosis occurs in wasting tissues of mice with PDX-induced lung cancer cachexia. Fig. S3: LCN2 promotes tissue ferroptosis and wasting. Fig. S4: Lung cancer cachectic mice have an increased number of myeloid cells and exhibit higher LCN2 expression in wasting adipose tissues. Fig. S5: Depletion of neutrophils alleviates tissue ferroptosis and wasting in lung cancer cachexia. 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--- title: Preparation of Vanillin-Taurine Antioxidant Compound, Characterization, and Evaluation for Improving the Post-Harvest Quality of Litchi authors: - Hafiz Umer Javed - Ruofan Liu - Cuijin Li - Sixia Zhong - Jiechang Lai - Murtaza Hasan - Xugang Shu - Li-Yan Zeng journal: Antioxidants year: 2023 pmcid: PMC10044817 doi: 10.3390/antiox12030618 license: CC BY 4.0 --- # Preparation of Vanillin-Taurine Antioxidant Compound, Characterization, and Evaluation for Improving the Post-Harvest Quality of Litchi ## Abstract Litchi’s post-harvest pericarp browning is one of the main constraints that drastically affect its visual attributes and market potential. Therefore, the vanillin-taurine Schiff base (VTSB) compound prepared from natural compounds of vanillin and taurine exhibited higher DPPH-radical-scavenging invitro antioxidant activity than vanillin. VTSB first-time report to mitigate the postharvest browning of litchi fruit. In this study, litchi fruits were dipped in 0.3 mM (based on pre-experiment) VTSB solution and stored at 25 ± 1 °C for six days to examine their effects on browning and postharvest quality. Fruit treated with VTSB had lower levels of browning degree (BD), browning index (BI), weight loss, soluble quinone (SQ), relative electrolyte leakage (REL), and malondialdehyde (MDA) than control fruit. Additionally, total anthocyanins and phenolic concentrations, Total soluble solids (TSS), and 2,2-diphenyl-1-picrylhydrazyl-free radical scavenging activity (DPPH-RSA) were preserved higher in VTSB-treated litchi fruit. The levels of Ascorbate peroxidase (APX), Superoxide dismutase (SOD), and Catalase (CAT) were higher in treated fruit, whereas polyphenol oxidase (PPO) and Peroxidase (POD) were decreased during the postharvest period. This study suggested that VTSB would be very useful for different post-harvest problems in the fruit and vegetable industry. ## 1. Introduction Litchi is an economical fruit crop with pinkish to bright red skin and edible aril having high juice content and a pleasing taste [1]. Its non-climatic nature prevents it from further ripening after harvest, thus litchi pericarp quickly turns brown within one–two days of being stored at room temperature which downgrades cosmetic quality and reduces marketability. The pericarp browning in harvested litchi fruit has also been associated with higher water loss, susceptibility chilling injury, rapid post-harvest senescence, an energy imbalance, fungal decay, and higher reactive oxygen species (ROS) generation [2,3,4]. It has been reported that oxidation of the phenolics in the litchi pericarp markedly leads to the formation of quinones, and direct polymerization into brownish pigments linked with accelerated anthocyanin degradation which consequently promotes the post-harvest browning [5]. PPO and POD enzymes are known to be key markers that have a significant function in enzymatic browning during storage due to the oxidation reaction of phenolics [5,6,7]. In order to overcome the major constraint of enzymatic browning in the litchi industry, it has been reported that the application of SO2 at a commercial scale significantly reduced the rapid oxidation reaction of phenolics that are responsible for litchi pericarp browning. Nevertheless, SO2 fumigation has some adverse side effects, including altered fruit flavor and health risks (carcinogenic effects) for packhouse employees and customers, as well as, negative consequences on quality and safety [8]. Accordingly, there is a dire need for developing food-safe technology for the quality preservation of litchi fruit during the post-harvest supply chain. Therefore, a natural, safe, eco-friendly, and antioxidant product needs to be explored that preserves the fruit’s quality while being secure for both customers and workers in the packaging industry. In the last two decades, several natural compounds with high antioxidant capacity such as apple polyphenols [4], α-Lipoic acid [9], salicylic acid [10], melatonin [11], tea seed oil [12], oxalic acid [13], and Aloe vera gel coating [14] have been found effective for the alleviation of pericarp browning and maintaining the storage quality of litchi fruit by reducing ROS and preserving higher antioxidant systems [12,13]. Therefore, it is essential to develop a natural antioxidant product that has engaged researchers in order to protect agricultural products from environmental and safety hazards. Plant phenols are well-known natural compounds with a phenolic hydroxy structure, such as vanillin (4-hydroxy-3-methoxy benzaldehyde), which showed significant anti-oxidative properties, and thus resulted in a safe food additive [15,16,17]. Vanillin was reported to keep the activity of defense-related enzymes constant, including POD, PPO, and phenylalanine ammonia-lyase (PAL) in tomato fruit [18], it protected the grapes from postharvest rot caused by yeast, mold, and Botrytis cinerea, and extended the post-storage quality [19,20]. On the other hand, an amino acid derived Schiff base attracted more attention due to its special biological activities including antioxidant, and antibacterial [21,22,23]. Taurine is a naturally occurring amino acid that is widely distributed in human tissues and organs and was used as a supplementation or additive in food. Moreover, taurine also has higher antioxidant activity and is utilized as a functional agent in medicine [24,25]. Notably, there is an aldehyde group inside vanillin that could form a Schiff base with taurine. Therefore, both vanillin and taurine were combined by the Schiff base reaction for producing a superior antioxidant product, which helps to preserve the fruit without any environmental or health concerns. Expectedly, the preliminary in vitro anti-oxidant evaluation of VTSB through the DPPH-RSA test showed a positive result. Currently, there is sporadic research work on employing plant phenols as applications regarding fruit preservation, and to the best of our knowledge, vanillin-taurine Schiff base has not been tested to preserve the post-harvest fruit quality, while VTSB has antioxidant potential and environmental protection, which fulfill the requirement for food safety. In this study, the VTSB was utilized to overcome the main post-harvest browning in the litchi pericarp and to explore the enzymatic mechanism. The newly synthesized compound will be helpful for managing browning in litchi fruit and preserving its quality by substituting traditional antibrowning treatments with VTSB compounds. ## 2.1. General Information for Preparation of Schiff Compounds All reagents including taurine and vanillin were purchased from commercial providers (Shanghai Macklin Biochemical Technology; Shanghai, China) and employed without additional purification. 1H NMR spectra were acquired on a Bruker spectrometer (Avance NEO 600 MHz; Bruker, Switzerland) operating at 400 MHz D2O, and chemical shifts were reported in ppm. ## 2.2. Preparation of VTSB (E)-3-((4-Hydroxy-3-methoxybenzylidene)amino) Propane-1-Sulfonate Compound For synthesizing the VTSB compounds, 250.5 g (2 mol) of taurine and 128 g (3.2 M) of NaOH were added to 300 mL of methanol under 40 °C till the mixture was clear. Then, 300 mL of methanol containing 304.3 g (2 M) of vanillin was added gradually into the reaction mixture and heated to reflux and the eventual yellow solid started to be produced sequentially. After 30 min of continuous refluxing and stirring, the reaction was completed. It was then allowed to cool to room temperature. The yellow solid substance was separated by filtering (Figure 1), yielding 472 g (yielded in $80\%$), which was structurally identified as VTSB by 1H NMR. ## 2.3. In Vitro Antioxidant Activity Evaluation The DPPH radical scavenging test is a rapid technique for invitro screening and evaluation of antioxidants. DPPH radical-scavenging test was performed following a protocol of Hai-Yun and Shuo-sheng, 2022 with minor modifications. The antioxidant activity was measured on UV-Vis at 517 nm, and the result was calculated using the formula below:Radical-scavenging activity (%) = [1 − (As − Ag)/Ae] × 100 Ae: Absorbance of DPPH solution in EtOH at 517 nm. Ag: Absorbance of the sample solutions in EtOH at 517 nm. As: Absorbance of DPPH and sample solution in EtOH at 517 nm. ## 2.4. Litchi Sampling Litchi fruits (Litchi chinensis Sonn. cv. “ Guiwei”) were hand-harvested at commercial maturity from a commercial orchard “Zhou Huang Bai” in Baiyun, Guangzhou, Guangdong, China. The well-known and traditional Guiwei (GW) cultivar is distinguished by its pink-red color. The litchi fruit was first sorted and graded at the orchard before being transferred to the Zhongkai University of Agriculture and Engineering within 2 h. ## Experimental Treatments The litchi fruits were re-sorted at the workstation and divided into two groups after being examined for uniformity in size, shape, and color, as well as free from any blemishes and disease signs. In total, 500 fruits were used in this experiment, which included two treatments (25 fruits per treatment, each with 3 replicates) and four storage periods. The dipping duration for both treatments was five min: one group was treated with 0.3 mM VTSB compounds, while the second was dipped in distilled water. The final concentration (0.3 mM) was determined based on a preliminary study that employed 0.3, 0.6, and 0.8 mM. Litchi fruit was treated and then allowed to air dry for 60 min at room temperature. After that, 25 treated fruits were packed in polypropylene bags with 6 holes and stored for 0, 2, 4, and 6 days at ambient conditions (25 ± 2 °C; RH 80–$90\%$). ## 2.5. Browning Index (BI), Browning Degree (BD), Weight Loss Based on Sivakumar and Korsten’s [2] scale descriptions, the browning of litchi skin was visually evaluated. The scale has five categories: 1 (no browning), 2 (1–2 brown spots), 3 ($25\%$), 4 ($50\%$), and 5 (75–$100\%$). Lin et al. [ 26] adopted the methodology to determine the litchi pericarp’s BD. The first step was to ground a 1 g sample of litchi skin in an extraction solution made up of 4 mL of methanol ($60\%$), phosphate buffer (0.1 M; pH 6.8), and polyvinylpyrrolidone ($2\%$). The homogenized sample was centrifuged at 15,000× g for 15 min to extract the supernatant. Finally, the supernatant measured the BD at 450 nm and was expressed as OD450 g−1 fresh weight (FW). The weight of the litchi fruit was determined using an electronic balance after drying from treatments, prior to storage, and at the end of each storage period. The weight loss was calculated using the formula (WL % = W1 − W2/W1 × 100) and expressed as a percentage. ## 2.6. H2O2 and O2•− Contents The Velikova and Loreto method was slightly modified to measure H2O2 concentration [27]. The peel sample (1 g) was centrifuged at 12,000× g for 15 min after extraction with $0.1\%$ TCA (3 mL). Afterward, 0.5 mL of the filtrate was diluted with 1 mL of KI (1 M) and 10 mM L−1 of phosphate buffer. At 390 nm, the solution absorbance was measured and reported as μmolkg-1. Yang et al. [ 28] methodology was followed with a few minor adjustments to establish the O2•− production rate. Briefly, 1 g of the pericarp from a litchi fruit was well mixed with a solution containing 50 mM of phosphate buffer (3 mL; 7.8 pH) and 1 percent of polyvinylpyrrolidone. The assay was then centrifuged (10,000× g) for 15 min at 4 °C. Monitoring NO2 generation from hydroxylamine in the existence of O2•− and comparing the results with a standard curve were used to compute the production of O2•− content, and calculated as nmol min−1 kg−1. ## 2.7. Relative Electrolyte Leakage (REL), Malondialdehyde (MDA) Content REL was measured according to the protocol adopted by Chen et al. [ 29]. Peel discs of the same size were removed from 10 litchis, mixed with 20 mL of deionized water, and then left at room temperature (25 °C) for 30 min. Afterward, the electric conductivity (EC) meter was employed to measure the initial reading (Lt). After the initial reading, the sample was heated in a water bath for 15 min. The last value (Lo) was taken after the heated solution had cooled, and the following equation was used to calculate REL as a percentage. REL(%) = Lt/Lo × 100 With minor modifications, MDA was estimated according to Zhang et al. [ 30] methodology. Litchi peel was properly homogenized in trichloroacetic acid (5 mL) and centrifuged (10,000× g; 20 min). Then, 2 mL of supernatant was collected and used to react with 2 mL of thiobarbituric acid. First, the assay mixture was boiled (100 °C for 15 min) and then cooled and centrifuged (5000× g) for 15 min. For MDA calculation, the absorbance was noted at 600, 532, and 450 nm and stated as nmol kg−1. MDA (nmol kg−1) = [6.45 (A532 − A600) − 0.56 × 450] × Vt × Vr/(Vs × m) where m is the mass sample; Vt expresses the extracted solution volume; Vr denotes the reaction mixture’s volume; Vs is the total extracted solution volume that contains the reaction substance ## 2.8. Soluble Quinone (SQ), Total Anthocyanin Content (TAC), Total Phenolic Content (TPC) Banerjee et al. [ 31] assay was used to calculate the content of SQ. One gram of litchi peel was homogenized entirely in 10 mL methanol before being centrifuged (12,000× g; 20 min). The supernatant was then obtained and directly utilized for absorbance at 437 nm, representing OD437 g−1 FW. Zheng and Tian’s [13] approach was utilized to determine the TAC. The 10 g of litchi skin was extracted with 15 mL of $0.15\%$ HCl and $95\%$ methanol (15: 85), and the extract’s absorbance was recorded using a UV-1800 UV-VIS spectrophotometer at 530, 620, and 650 nm (Shimadzu, Kyota, Japan). The formula used to calculate the TAC was ΔA g−1 = (A530 − A620) 0.1(A650 − A620). The Folin-Ciocalteu reagent assay used as an extraction solution to determine the TPC was the same as that used previously by Ainsworth and Gillespie [32], and the absorbance was calibrated at 735 nm. For TPC calculation, gallic acid’s standard curve was plotted and the concentration was given in mg kg−1. ## 2.9. Total Soluble Solids (TSS), Titratable Acidity (TA), Ascorbic Acid (AA) TSS was calculated using a digital refractometer and was represented as Brix. TA was determined through titration with NaOH (0.1 N) and reported as a percentage of malic acid [33]. The amount of ascorbic acid (AA) in the litchi juice was determined using a titration assay with 2,6-dichlorophenol-indophenol [34]. ## 2.10. Enzymatic Activities DPPH-RSA was analyzed in litchi pericarp using the procedures described by Brand-Williams et al. [ 35]. Firstly, extracting the litchi skin in methanol, and then 50 µL of the extract was mixed with DPPH (0.1 mmol L−1). Then, the solution was left to stand for 30 min at room temperature (25 °C) in a dark room, and the absorbance was gauged at 517 nm. This process was carried out three times, and the DPPH-RSA was computed as a percentage. Using the protocols and directions of commercial kits (Nanjing Jiancheng Bioengineering Institute), the activity of enzymes such as superoxide dismutase (SOD), polyphenol oxidase (PPO), ascorbate peroxidase (APX), peroxidase (POD), and catalase (CAT) were measured in litchi peel tissues. ## 2.11. Statistical Analysis The data were represented as the mean ± standard error (SE). The SPSS statistical program, version 16.0 (SPSS, Inc., Chicago, IL, USA), was used to analyze the data. To compare the means, an Independent-Samples T-test was utilized. The factorial design (LSD) of the study was used to examine the significance of the vanillin treatment and storage period at level p ≤ 0.05. ## 3.1. The Preparation of VTSB and In Vitro Antioxidant Capacity The research commenced with the synthesis of vanillin Schiff base. Vanillin was added to the methanol solution containing NaOH and taurine. After stirring the reaction mixture under reflux for 30 min, the expected VTSB molecule was formed successfully as shown in Figure 2A which was structurally identified by 1H NMR. The antioxidant activity of vanillin and Schiff base compound (vanillin and taurine) at different concentrations was measured (Figure 2B,C). The DPPH-RSA improved as the concentration of the Schiff base solution increased, and the subsequent activity was steady until it stabilized. The greatest scavenging rate of $65.18\%$ was recorded at a concentration of 4.0 mg/mL of Schiff base solution, whereas the rate of DPPH-RSA increased and approached $50\%$ at concentrations lower than 1.5 mg/mL (Figure 2A). Regarding vanillin (3.0 to 5.0 mg/mL), DPPH-RSA concentration was quickly raised. Vanillin has a very low level of antioxidant activity, as evidenced by the outcome that when the concentration of the vanillin reference solution is between 5.0 and 6.0 mg/mL, DPPH-RSA slowly increased, tended to stabilize, and the rate did not exceed $10\%$ (Figure 2B). The findings showed that compounds based on the Schiff reaction (vanillin and taurine) have greater antioxidant activity at lower concentrations as compared to simple vanillin. ## 3.2. Optimization of VTS-Compound The first step was to optimize the VTSB compound based on the measurement of the following variables: pericarp browning, weight loss, litchi fruit quality (TSS; TA and ascorbic acid), BD, and REL. The 0.3 mM treatment was found to be more effective as compared to 0.6 mM and 0.8 mM (Figure 3). ## 3.3. BI, BD, Weight Loss Compared to untreated (control) fruit, vanillin-treated fruit displayed a considerably lower BI throughout the storage period (Figure 4A). Only a small proportion of the litchi fruit in the vanillin-treated group had BI on day two. However, a minor increase was found on day four. Besides, BI quickly grew in the fruit that was not treated, and the fruit completely turned brown on day six (Figure 4A). BD values increased with storage time and were observed consistently significantly lower in vanillin-treated fruit than in non-treated fruit throughout the study (Figure 4B). Post-harvest browning had a negative impact on the overall appearance of fruit and led to substantial economic losses as a result of the lack of marketability [8]. Litchi fruit turns brown after harvest because anthocyanin pigments are oxidized due to desiccation. In this study, litchi weight loss (WL) increased significantly as storage time progressed. Furthermore, the increase in WL was substantially lower in the fruit treated with vanillin application as compared to the control (Figure 4C). On day six of storage, the WL percentage in the vanillin group was markedly lower (2.04-fold) than in the control fruit. Water loss from the fruit’s surface reduces consumer acceptance as well as market value [8]. Vanillin was found to be effective in reducing water loss and preserving the quality of litchi fruit in this study. ## 3.4. H2O2 and O2•− Contents During storage, ROS (O2 and H2O2) accumulation rates eventually increased in both the control and VTSB treatments; nevertheless, the accumulated levels of these two ROS in the treated fruit were significantly lower than those in the non-treated fruit (Figure 4D,E). The production of ROS is frequently associated with loss of membrane integrity and other stress-related situations, such as browning and senescence during the storage of litchi fruit [36]. Therefore, in our study, the VTSB compound may decrease H2O2 and O2•− formation because it minimized membrane peroxidation and senescence. ## 3.5. SQ, TAC, TPC The principal catalyst for the enzyme-induced browning of fruits and vegetables is the transformation of phenolic chemicals into quinones, which are then polymerized to form brown, red, or black pigments. In both treatments, the concentration of SQs was much higher than it was on day 0 of the experiment (Figure 5A). However, post-harvest treatment of VTSB compounds significantly reduced the rise in SQ compared to the control. A direct correlation existed between the generation of SQs and processes that led to browning, with larger production of SQs being the cause of browning [37]. Overall, TAC decreased gradually in both treatments during storage (Figure 5B). However, the TAC of the non-treated fruit decreased at a faster rate than that of the VTSB-treated fruit during storage. Anthocyanin is responsible for the red coloration of the litchi pericarp [38]. On the other hand, it degrades quickly after harvest. The distinctive red color of the litchi fruit is thought to be essential for visual appearance and economic potential [2]. The breakdown of the vacuole leads to the degradation of anthocyanin, which is spurred on by the breakdown of cellular compartmentation. Anthocyanins are eventually degraded by enzymes [39]. In this study, VTSB compounds play an essential role in slowing the degradation process of anthocyanin and ultimately attracting the consumer. Regardless of the treatments, a significant decline in TPC occurred in the litchi pericarp tissues as storage days progressed (Figure 5C). This decrease was, however, more pronounced in the control treatment than in the fruit treated with VTSB. TPC levels in litchi fruit treated with VTSB were $16.14\%$ higher than those in the control. Reduced TPC probably occurs from oxidation, which ultimately causes the browning of the litchi fruit [7]. As a result, fruit treated with VTSB had higher TPC values, which might play a significant role in slowing down the oxidation process in litchi fruit. ## 3.6. REL and MDA Content The VTSB compounds significantly lowered the production of MDA content compared to the control (Figure 5D). From day two to six, there was a noticeable difference in MDA level between VTSB compounds and control fruit. The results showed that the control had a $10.36\%$ higher MDA concentration on day six than the VTSB treatment. The storage times and treatments also significantly affected REL. Overall, REL increased throughout the study for both the control and the VTSB-treated fruits (Figure 5E). Leakage is a key indicator of membrane integrity, and loss of membrane integrity can lead to the decompartmentation of enzymes and substrates, which can contribute to the pericarp browning of litchi [40]. However, fruit treated with VTSB significantly improved membrane integrity and displayed inferior REL than non-treated fruit. On day four of storage, the VTSB treatment significantly reduced REL by $35.06\%$ compared to the control group. ( Figure 5E). According to Sun et al. [ 41], oxidative stress is the cause of cell membrane damage, which raises the MDA and REL levels in litchi pericarp [4,42]. By maintaining antioxidant activity and delaying post-harvest senescence, treated fruit can protect its cellular membranes from lipid peroxidation and membrane disruption. Therefore, low REL and MDA content in VTSB application was typically influenced by preserved membrane integrity, greater antioxidant activities, and less oxidative damage and senescence. ## 3.7. TSS, TA and AA TSS continuously dropped during the six days of storage. However, compared to untreated control fruit, the TSS remained higher as in VTSB-treated fruit during storage (Figure 5F). The taste is closely associated with the changes in the TSS of litchi fruit during the post-harvest period [39]. Post-harvest senescence causes TSS to decline, which eventually downgrades the eating quality of fruit [6]. The application of VTSB compounds has been found to prevent pericarp discoloration and reduce water loss as a result of delayed senescence. Accordingly, the application of VTSB significantly prevented senescence and preserved TSS during storage. TA gradually decreased as storage time increased (Figure 5G). TA content normally decreased in litchi fruit with longer storage times [7]. Fruit senescence occurs after harvest, the TA level is reduced due to oxidation [43]. On day two, VTSB-treated fruit showed a lower TA content than untreated control fruit (Figure 5G). However, the difference in TA between the two treatments on days two and four was non-significant. Regarding ascorbic acid levels in fruit during the entire period of storage, there was a non-significant difference between the VTSB treatment and control group (Figure 5H). Nevertheless, the rate of reduction in AA level considerably accelerated with storage time. According to Mditshwa et al. [ 44], the oxidative degradation of AA causes its content to decrease normally during storage. ## 3.8. Antioxidant Activity DPPH-RSA is generally used to calculate non-enzymatic antioxidant activity in plant tissues, and its level typically decreases as a result of increased free radical production in litchi peel tissues after extensive post-harvest storage [7]. The outcomes indicated that the VTSB compound has good potential to keep higher DPPH-RSA due to a lower generation of free radicals. Therefore, VTSB-treated litchi fruit displayed increased DPPH-RSA, likely as a consequence of a lower rate of senescence and H2O2 and O2•− content during the post-harvest period. Furthermore, VTSB treatment demonstrated higher content than the control during storage (Figure 6D); a particularly large differential of $31.14\%$ was detected on day six. It is thought that litchi peel with a higher DPPH-RSA will help to reduce oxidative stress [34]. SOD, CAT, and APX have been linked to plant stress responses associated with senescence as well as direct and indirect ROS scavenging [45]. SOD, CAT, and APX activity in both vanillin-treated and untreated control litchi fruit showed a decreasing trend with progress in the storage period. The APX and CAT activities remained constant on days four and six of the storage. Fruit treated with the VTSB compound had significantly increased antioxidant enzyme activity (SOD, CAT, and APX) throughout storage than the control fruit (Figure 6A–C). Antioxidative enzymes like SOD, CAT, and APX detoxify various free radicals and minimize oxidative damage, which helps to prevent the litchi fruit from browning. Therefore, upregulation of the aforementioned enzymes is essential to decrease the prevalence of browning in litchi fruit [34]. According to Tomas-Barberan and Espin [46], the POD and PPO enzyme are reported to be associated with the oxidation of phenolic compounds. In the litchi fruit, phenolics and anthocyanidin produced by the enzyme anthocyanase may act as substrates to be oxidized by POD and PPO to o-quinone and other substances, resulting in pericarp browning [40]. As far as the activities of POD and PPO are concerned, the overall storage study showed an increasing trend irrespective of treatments (vanillin-treated litchi and untreated control litchi fruit) (Figure 6E,F). Furthermore, the fruit that had been dipped in the novel vanillin solution showed lower activity as compared to the water-dipped fruit. Vanillin-dipped litchi fruit showed $40.73\%$ and $21.42\%$ lower POD and PPO activities, respectively, than the control after six days of storage. Prooxidant enzymes (POD and PPO) are present in organelles (besides the phenolics) that oxidize phenolics and cause the browning of litchi fruit [14]. Therefore, to prevent the harvested litchi fruit from turning brown, the activity of the above enzymes should be reduced [4]. The dipping treatment preserves membrane integrity and inhibits POD and PPO activities and creates a barrier among the phenolics and enzymes that helps to reduce phenolics oxidation and prevents membrane rupture [47]. Novel vanillin compounds might have displayed reduced PPO and POD enzyme activity as a result of conserved compartmentation. ## 4. Conclusions The newly synthesized compound VSTB showed remarkable in vitro antioxidant activity, with a DPPH-scavenging rate of $65.18\%$. The 0.3 mM VTSB application enhances the three-day marketable shelf life of litchi fruit which might be due to the suppression of browning and senescence. The VTSB treatment can prevent litchi pericarp browning due to its potential to suppress anthocyanin, phenolics, and flavonoid degradation as well as minimize membrane leakage (REL, H2O2, and O2) as compared to the control. Furthermore, it maintains antioxidant properties, both enzymatic and non-enzymatic, which may improve in scavenging ROS. As a result, the application of VTSB might be a feasible and secure approach to preventing pericarp browning and improving post-harvest storage of litchi fruit. The effectiveness of VTSB compounds as a post-harvest preservative on various fruits, including litchi fruit texture and quality will be examined in further investigations. ## References 1. Fahima A., Levinkron S., Maytal Y., Hugger A., Lax I., Huang X., Eyal Y., Lichter A., Goren M., Stern R.A.. 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--- title: 'GYY4137-Derived Hydrogen Sulfide Donates Electrons to the Mitochondrial Electron Transport Chain via Sulfide: Quinone Oxidoreductase in Endothelial Cells' authors: - Bastiaan S. Star - Elisabeth C. van der Slikke - Céline Ransy - Alain Schmitt - Robert H. Henning - Frédéric Bouillaud - Hjalmar R. Bouma journal: Antioxidants year: 2023 pmcid: PMC10044827 doi: 10.3390/antiox12030587 license: CC BY 4.0 --- # GYY4137-Derived Hydrogen Sulfide Donates Electrons to the Mitochondrial Electron Transport Chain via Sulfide: Quinone Oxidoreductase in Endothelial Cells ## Abstract The protective effects of hydrogen sulphide (H2S) to limit oxidative injury and preserve mitochondrial function during sepsis, ischemia/reperfusion, and neurodegenerative diseases have prompted the development of soluble H2S-releasing compounds such as GYY4137. Yet, the effects of GYY4137 on the mitochondrial function of endothelial cells remain unclear, while this cell type comprises the first target cell after parenteral administration. Here, we specifically assessed whether human endothelial cells possess a functional sulfide:quinone oxidoreductase (SQOR), to oxidise GYY4137-released H2S within the mitochondria for electron donation to the electron transport chain. We demonstrate that H2S administration increases oxygen consumption by human umbilical vein endothelial cells (HUVECs), which does not occur in the SQOR-deficient cell line SH-SY5Y. GYY4137 releases H2S in HUVECs in a dose- and time-dependent fashion as quantified by oxygen consumption and confirmed by lead acetate assay, as well as AzMC fluorescence. Scavenging of intracellular H2S using zinc confirmed intracellular and intramitochondrial sulfur, which resulted in mitotoxic zinc sulfide (ZnS) precipitates. Together, GYY4137 increases intramitochondrial H2S and boosts oxygen consumption of endothelial cells, which is likely governed via the oxidation of H2S by SQOR. This mechanism in endothelial cells may be instrumental in regulating H2S levels in blood and organs but can also be exploited to quantify H2S release by soluble donors such as GYY4137 in living systems. ## 1. Introduction Hydrogen sulfide (H2S) has antioxidant, anti-inflammatory, and anti-apoptotic properties that allow the prevention of cellular injury in various diseases, such as sepsis, ischemia-reperfusion, and neurodegenerative diseases [1,2,3,4]. One of the supportive effects of H2S is attributed to fuel mitochondria by donating electrons to the electron transport chain (ETC) after being oxidized by the sulfide:quinone oxidoreductase (SQOR) unit [5,6]. However, the physical and chemical properties of H2S make it a highly unstable molecule, whereby concentrations of H2S in biological matrices (e.g., blood plasma, cells) will drop within seconds following its administration by fast dispersion, evaporation, and reaction with proteins. Increasing the dosage of H2S will lead to higher levels in blood plasma, but the narrow therapeutic window, where H2S concentrations in the µM range and above lead to neurological dysfunction and rapid cardio-circulatory failure leading to cardiac arrest [7,8], preclude the use of H2S in higher dosages. Consequently, soluble long-acting H2S donors have been developed to provide sustained, physiologically relevant H2S levels in blood plasma. GYY4137 is a long-acting H2S donor that contains two sulfur groups producing H2S upon hydrolysis [9,10], demonstrated protective effects against oxidative stress, organ function, and apoptosis in animal models of sepsis, ischemia-reperfusion, arteriosclerosis, and neurogenerative diseases [10,11,12,13,14,15,16,17]. Thus, the narrow therapeutic window and volatile properties of H2S limit safe application in patients that can be overcome by the administration of H2S-donors to allow a safe release of H2S to govern cytoprotective effects and preserve organ function. Although it is clear that GYY4137 can release H2S and protects against sepsis, ischemia-reperfusion, and neurodegenerative diseases [10,11,12,13,14,15,16,17], the mechanism by which GYY4137 supports mitochondrial function either via sulfhydration, metalloprotein interaction, antioxidant, or via direct H2S effects on the ETC is not elucidated yet [18]. As mitochondria play an important role in the pathophysiology of diseases in which H2S has a beneficial effect [5,6,16], intracellular H2S release by GYY4137 may be inferred to boost mitochondrial electron transport. Endothelial cells likely comprise an important target of GYY4137, as they represent the primary target cell after parenteral administration. Moreover, endothelial dysfunction is a common hallmark of organ failure in sepsis, ischemia-reperfusion, and neurogenerative diseases [19,20,21,22]. To gain a better understanding of the mechanisms by which GYY4137 protects against organ failure in reaching mitochondria, it is essential to assess the release of H2S from GYY4137 within endothelial cells and its effect on cellular respiration. The SQOR enzyme is able to oxidize H2S, yielding electrons to enter the mitochondrial electron transport chain. Most of the insight into the expression and function of SQOR comes from invertebrate species, while knowledge about the expression and function of SQOR in vertebrate species remains limited. The expression of SQOR differs between cell types, as intestinal cells have a relatively high expression, while the neuroblastoma cell line SH-SY5Y does not seem to express SQOR-like proteins [6,23,24]. In rats, SQOR is widely expressed, as shown in neurons, oligodendrocytes, endothelial cells, liver tissue, renal podocytes, tubular cells, sperm, and T cells [24,25]. The expression of SQOR is restricted to the mitochondria [24]. Remarkably, both cerebral and renal expression of SQOR increased during ageing in rats [24,25]. Whether human endothelial cells express SQOR is yet unknown. We hypothesize that the presence of mitochondrial SQOR underlies the differential effects of GYY4137-derived H2S on mitochondrial oxygen consumption in different cell types. To this end, we confirmed the presence of SQOR in human umbilical vein endothelial cells (HUVECs) and quantified the direct functional effect of GYY4137 on SQOR by measuring mitochondrial oxygen consumption. Finally, we confirmed the ability of GYY4137-derived H2S to directly increase intramitochondrial H2S levels by trapping H2S with zinc. Collectively, these data demonstrate that GYY4137 influences mitochondrial function in endothelial cells depending on the presence of SQOR. ## 2.1. Cell Culture HUVECs were obtained from the RuG/UMCG Endothelial Cell Facility. Briefly, primary isolates of umbilical cords were mixed and subsequently cultured on HUVECs culture medium, consisting of RPMI 1640 (Lonza, #BE12-115F, Breda, The Netherlands) supplemented with $20\%$ heat-inactivated fetal calf serum (ThermoFisher Scientific, #10082147, Waltham, MA, USA), 2 mM l-glutamine (Life Technologies #25030, Carlsbad, CA, USA), 5 U/mL heparin (Leo Pharmaceutical Products, Amsterdam, The Netherlands), $1\%$ Penicillin/Streptomycin (Sigma-Aldrich #P4333, St. Louis, MI, USA), and 50 μg/mL EC growth factor supplement from (Sigma-Aldrich, #E2759, St. Louis, MI, USA). The SH-SY5Y cells were used on DMEM culture media with $10\%$ heat-inactivated fetal calf serum and $1\%$ Penicillin/Streptomycin. The cells were cultured in 75-cm2 tissue culture flasks (Corning #430720U, St. Louis, MI, USA) at 37 °C under $5\%$ CO2/$95\%$ air. HUVECs were used for experiments up to passage 8. Cells were detached with trypsin (Sigma-Aldrich #25300, St. Louis, MI, USA). All compounds were dissolved in Milli-Q water. Cells were incubated with GYY4137 (Sigma-Aldrich, #SML0100, St. Louis, MI, USA) and or Zinc chloride (ZnCl2) (Sigma-Aldrich #3208086, St. Louis, MI, USA). Final concentrations of the mitochondrial inhibitors rotenone 1 µM (Sigma-Aldrich, R8875, St. Louis, MI, USA) and antimycin 5 µM (Sigma-Aldrich, #A8674, St. Louis, MI, USA). ## 2.2. Sulfide Solution, Preparation, and Use A stock of 1 M sulfide solution was prepared from Na2S (Sigma-Aldrich) for each experiment. Ten microliters of this solution were diluted in 2 mL of Milli-Q water, and this 5 mM solution was immediately loaded in the glass syringes of the minipump so that it was not exposed to air for more than a few tenths of seconds. The pH of sulfide solutions was not equilibrated to a neutral value as it would enhance the volatility of sulfide by increasing H2S content. ## 2.3. Oxygen Consumption Rate Seahorse Seahorse XF96 analyzers (Seahorse Biosciences, North Billerica, MA, USA) were used to assess the cellular oxygen consumption rate (OCR) and extracellular acidification rate (ECAR). Briefly, HUVECs were seeded in XF-96 cell culture plates (Seahorse Bioscience) at 1104 cells/well and incubated under standard conditions for 24 h. Cells were washed with XF Base RPMI (Seahorse Bioscience #103336, North Billerica, MA, USA) containing 8 mM glucose, 8 mM pyruvate, and 2 mM L-glutamine. The overall oxygen consumption rate was measured during the addition of GYY4137 (0.1 mM, 1 mM,10 mM)(Sigma-Aldrich, #SML0100, St. Louis, MI, USA) and ZnCl2 (Sigma-Aldrich #3208086, St. Louis, MI, USA). Experiments were conducted using six replicates for each condition and repeated in two independent experiments. Data were analysed by using Wave Desktop and Controller 2.6 Software. ## 2.4. Analysing the Presence of SQOR by Western Blot Protein lysates were obtained using RIPA lysis buffer (50 mM Tris-Cl pH 8.0, 150 mM NaCl, $1\%$ Igepal Ca 630, $0.5\%$ Sodium Deoxycholate, $1.0\%$ SDS, $0.4\%$ protein inhibitor cocktail, 1 mM sodium orthovanadate, 10 mM NaF, 10 mM β-mercaptoethanol). Next, protein concentrations were measured with a Bio-Rad protein assay on a Bio-Tek Synergy H4 plate reader. Samples were loaded to 4–$20\%$ sodium dodecyl sulfate-polyacrylamide pre-casted gels (Bio-Rad TGX gels #4568096, Hercules, CA, USA) and transferred to a nitrocellulose membrane. A stain-free picture was captured to allow post-hoc normalisation for protein load. Membranes were blocked with $5\%$ skimmed milk for 30 min and incubated with the primary antibody anti-SQORDL (Sigma-Aldrich #HPA017079, St. Louis, MI, USA) (1:1000, v/v) overnight at 4 °C. Secondary antibody goat anti-rabbit (DAKO #P0448, Santa Clara, CA, USA) (1:2000, v/v) were used to incubate for 2 hr at room temperature. Visualisation was performed using a SuperSignal (Perkin Elmer #NEL112001EA, Waltham, MA, USA) on a Bio-Rad ChemiDoc MP imaging system, while protein levels are quantified using ImageLab 6.0 (Bio-Rad, Hercules, CA, USA). ## 2.5. Measurement of Cellular Respiration and Sulfide Oxidation The Oroboros O2k apparatus was used to monitor cellular oxygen consumption. Sulfide infusions or injections were made with the Tip2k minipump (Oroboros instrument). The respiration medium contained cell culture media. The pH was adjusted with 20 mM of HEPES buffer to a pH of 7.5. Cell suspensions were obtained after trypsinisation and immediately dissolved in culture media for measurements, for experimental procedures of GYY4137, 3 × 106 cells/mL were used. Na2S experiments with HUVECs were performed with 1.5 × 106 cells/ ml and SH-SY5Y with 3 × 106 cells/mL. ## 2.6. Imaging HUVECs were cultured on coverslips of glass, coated with gelatin $2\%$, and incubated with GYY4137 for 30 min. The TMRM (100 nM) (Thermofisher #T668, Waltham, MA, USA) and AzMC (10 µM) (Sigma-Aldrich #802409, St. Louis, MI, USA) were incubated for 20 min and washed with HBSS. Images were measured with the Deltavision Elite microscope emission/excitation filter settings DAPI/FITC for AzMC and TRITC/TRITC for TMRM. Fluorescence was analysed with ImageJ. ## 2.7. AzMC Dose Response Cells were cultured in a 96-wells plate until confluent, then the culture medium was replaced by HBSS supplemented with Na2S or GYY4137 and loaded with AzMC (10 µM). Fluorescence was measured after 30 min with the Bio-Tek Synergy H4 plate reader at ex: 340 and em: 445. ## 2.8. Lead Acetate Lead acetate paper reacts with hydrogen sulfide and forms brownish-black lead sulfide. It is prepared by soaking filter paper in a $1\%$ lead acetate solution followed by drying. Cells were cultured in a 96-wells plate, covered with lead acetate papers, and placed at 37 °C under $5\%$ CO2/$95\%$ air. Images were taken with ChemiDoc and analysed with ImageJ. ## 2.9. Electron Microscopy HUVECs were cultured in a 24-wells plate. GYY4137, zinc, and GYY4137 together with zinc were added, and after 30 min incubation, cells were embedded. HUVECs were fixed with $2\%$/$2\%$ formaldehyde/glutaraldehyde for 30 min at room temperature. Cells were washed 3 × in 0.1 M PBS. A second fixation was performed in $1\%$ osmium tetroxide in 0.1 M PBS for 1 h at 4 °C. After washing (3 times in water), cells were gradually dehydrated in graded series of ethanol and then gradually infiltrated with resin at room temp. Gelatin capsules were put upside-down on the coverslips and polymerised for 24 h at 60 °C. Samples were lightly heated to remove the glass coverslip and cut on an ultramicrotome Reichert S at 90 nm of thickness. Acquisitions were performed on a JEOL 1011 TEM with a Gatan Orius 1000 CCD Camera. ## 2.10. Data Analysis Statistical analysis was performed using a GraphPad Prism 7.02 for Windows. Kruskal-Wallis test, followed by a Mann-Whitney U-test in the case of non-normally distributed variables, was used to calculate statistical differences between groups. For normally distributed variables, ANOVA was used. Data are expressed as mean ± standard error of the mean. $p \leq 0.05$ was considered statistically significantly different. Images were created with BioRender.com. ## 3.1. Human Umbilical Vein Endothelial Cells Oxidize H2S via the Sulfide:Quinone Oxidoreductase Cellular H2S oxidation is accomplished by SQOR, a protein part of the sulfide oxidation unit (SOU), together with the sulfur dioxygenase and the thiosulfate-cyanide sulfurtransferase, catalysing sulfide oxidation to thiosulfate [6,23]. Consequently, cellular H2S oxidation is characterized by two properties: firstly, in contrast with carbon oxidation (Krebs cycle), it is resistant to inhibition by rotenone. Secondly, sulfide oxidation by SQOR requires oxygen, hence it increases cellular oxygen consumption. Therefore, in HUVECs, the presence of SQOR activity will be measured [6]. In agreement with sulfide oxidation by SQOR, administration of sodium sulfide (Na2S, 1–5 µM) dose-dependently increased oxygen consumption of HUVECs (Figure 1A grey trace), which effect was also observed when the endogenous respiration was inhibited by rotenone (inhibition of complex I and the Krebs cycle), uncovering the electron entrance via SQOR (Figure 1A, blue trace). While both traces (Figure 1A) revealed similar kinetics over the 1–5 µM Na2S range, a sharp difference appeared when the final Na2S concentration reached 10 µM, presumably due to partial inhibition of mitochondrial complex IV by sulfide. Thus, the dose-dependent increase of oxygen consumption by H2S supports the presence of an SQOR in endothelial cells. Next, to quantify the oxidation capacity of SQOR, we used a continuous infusion of sulfide in the absence and presence of rotenone, resulting in a synchronous increase in cellular oxygen consumption when the 5 mM solution of Na2S was infused at a rate of 24 nL/s (Figure 1B). Increasing the infusion rate to 36 nL/s disrupted this similarity between the oxygen consumption in the control group (grey trace) and the rotenone group (blue trace), explained by competing electrons entrance via complex I from the Krebs cycle in control cells. While a further increase of Na2S infusion rate to 48 nL/s persistently inhibited oxygen consumption in HUVEC, it increased oxygen consumption in the presence of rotenone, which was maintained after the cessation of infusion. This observation indicates a saturation of SQOR activity with an accumulation of H2S, which in the presence of rotenone remains available for oxidation after the infusion, while in the absence of rotenone results in inhibition of cellular respiration. To substantiate that SQOR function in HUVECs is unaffected by passaging of cells, experiments were repeated in HUVECs within 2 h after their isolation from the umbilical cord. Freshly isolated HUVECs showed a similar sulfide oxidation capacity as HUVECs (Figure S1). In addition, HUVECs express detectable protein levels of SQOR, while SH-SY5Y cells do not express detectable levels (Figure 1C and Figure S1B). Consistently, SH-SY5Y cells without SQOR failed to increase oxygen consumption upon Na2S, either in the presence or absence of rotenone (Figure 1D). Moreover, the continuous infusion of sulfide produced a gradual inhibition of oxygen consumption. Finally, antimycin A blocked mitochondrial complex III, and thereby mitochondrial oxygen consumption (Figure 1B grey trace), demonstrating electron donation upstream to complex III of the mitochondrial electron transport chain. Together, these results demonstrate the presence of a potent SQOR in endothelial cells and argue for the importance of endothelial cells in regulating H2S levels in blood and organs. ## 3.2. Stoichiometry between H2S Oxidation and Oxygen Consumption Sulfide Oxidation by SQOR and endogenous respiration, the stoichiometry between the sulfide infusion rate and the increase in oxygen consumption rate was assessed (∆JO2) (Table 1). Basal respiration of HUVECs (1.5 × 106/mL) amounted 55 O2 pmol/(s × mL) (Figure 2B). In this experiment, the maximal rate for SQOR activity in the HUVECs was able to neutralise 24 nL/s of Na2S (5 mM) in the 2 mL chamber, hence a sulfide flux of (24 × 5 ÷ 2) = 60 pmol/(s × mL). Rotenone reduced basal oxygen consumption to 10 pmol O2/(s × mL), and infusion of Na2S raised it to 40 pmol/(s × mL). The theoretical stoichiometry O2/Na2S for oxidation of sulfide is 1.0, and the difference with the experimental value observed here in the presence of rotenone ($\frac{40}{60}$ = 0.67) results, for the largest part, from impure/degraded sulfide with less than the theoretical sulfide concentration in the solution infused. When sulfide infusion took place in the presence of the endogenous respiration, the presence of SQOR ensured the same sulfide elimination rate, but the increase in cellular oxygen consumption was only 28 pmol/(s × mL). The difference is expected to reveal interactions between SQOR and endogenous respiration and notably with complex I., the target of rotenone inhibition. Accordingly, if one assumes an unchanged stoichiometry of sulfide oxidation by SQOR in the absence and presence of rotenone, the difference (40 − 28 = 12 pmol O2/(s × mL)) represents the reduction in complex I activity caused by SQOR. Half of the oxygen consumption rate observed with sulfide in the presence of rotenone is explained by electron transfer in the mitochondrial electron transport chain and cytochrome oxidase reaction. The other half is explained by the dioxygenase activity of SOU. With the maximal rate of 40 pmol O2/(s × mL) oxygen consumption, 20 resulted from cytochrome oxidase reaction. This is to be compared with the endogenous respiratory rate that recruited cytochrome oxidase oxygen consumption at a rate of 55. The experimental stoichiometries in this report are close to others values reported so far with measurements made in similar conditions. It makes it then very likely that here we observe the activity of SOU that is expected to be present in the majority of mammalian cell lines. Hence when artificially recruited by high sulfide levels, the SQOR present in HUVECs cells could ensure a consequent activity of the mitochondrial respiratory chain (at $40\%$ of the normal respiratory rate). ## 3.3. SQOR Oxidises H2S Released from GYY4137 Because of the fast evaporation of H2S, Na2S effects on oxygen consumption cannot be assessed using the Seahorse apparatus* due to its open architecture [6]. Yet, in contrast, Seahorse can be used to assess the effects of GYY4137, being a slow-releasing H2S donor, with the advantage of increasing cell/medium ratio [from 1.5–3 × 103 cells per µL (Oroboros) to 1 × 104 cells per µL (Seahorse). GYY4137 (0.1–10 mM) dose-dependently increased cellular oxygen consumption in HUVECs (Figure 2A). Next, we sought to scavenge H2S released from GYY4137 by the addition of an excess of zinc ions, which remove sulfide ions from the solution because of the low solubility of zinc sulfide (ZnS; ~10−25 Ksp) (Figure S2). The addition of zinc ions dose-dependently decreased GYY4137-induced oxygen consumption (Figure 2A), but the effect appeared to extend beyond the normalisation of the oxygen consumption back to the value obtained before the GYY4137 injection. Furthermore, the extracellular acidification rate acutely dropped upon the administration of the highest concentration of GYY4137, which is likely caused by the abrupt pH change upon injection of the GYY4137 solution (Figure 2B). Collectively, these data show that GYY4137 released H2S increased cellular respiration of SQOR-expressing HUVECs. ## 3.4. GYY4137 Only Increases Oxygen Consumption in Cells with a SQOR To assess H2S release by GYY4137 in HUVECs with SQOR and SH- SY5Y without SQOR, we measured the sulfide released by cells using lead acetate paper placed on top of cell culture wells. GYY4137 (10 mM; 48 h) blackened lead acetate paper, signifying H2S release in HUVECs and SH-SY5Y cells (Figure 3A,B). Next, we estimated the H2S release from GYY4137 by measuring oxygen consumption in cells using the closed 1 mL chamber of the Oroboros. Administration of GYY4137 (10 mM) increased oxygen consumption in HUVEC by 12.7 pmol O2/(s × mL) (Figure 3C), while the effect, if any, would be a slight decrease that remained non-significant with SH-SY5Y cells lacking SQOR (Figure 3D). Administration of 80–800 µM zinc chloride (black arrow) to HUVECs inhibited oxygen consumption only in the presence of GYY4137 (blue trace; Figure 3E), while zinc chloride was without effect on cellular oxygen consumption in control cells. Next, to validate SQOR activity, HUVECs were treated with rotenone to inhibit complex I and the Krebs cycle incubated with GYY4137 (10 mM), which increased oxygen consumption initially with 14 pmol O2/(s × mL) (Figure 3F). Administration of 80–800 µM zinc chloride (black arrow) to SH-SY5Y cells inhibited oxygen consumption only in the presence of GYY4137 (blue trace; Figure 3G), while zinc chloride was without effect on cellular oxygen consumption in control cells. As expected, oxygen consumption was absent in GYY4137 administered to the culture medium (Figure 3H). The increase in oxygen consumption caused by GYY4137 in the presence of rotenone could be considered to result from SQOR activity and be proportionate to the H2S release by GYY4137. The theoretical oxygen-to-sulfide stoichiometry of one for the SOU reaction results in the same value for oxygen consumption and sulfide release. Thus, high levels of GYY4137 release a detectable amount of H2S on mitochondrial SQOR, providing insight into the releasing capacity following the administration of this sulfide donor. Together, GYY4137 released H2S in HUVECs and SH-SY5Y cells, yet its effect on mitochondrial oxygen consumption is only observed in HUVEC, which is likely explained by the presence of SQOR. ## 3.5. GYY4137 Increased Intramitochondrial H2S Levels Next, we assessed intracellular H2S levels in HUVECs that were loaded with the 7-azido-4-methyl-coumarin (AzMC) fluorescent H2S-probe and the mitochondrial fluorophore tetramethylrhodamine methyl ester (TMRM) (Figure 4A,B and Figure S3A). GYY4137 demonstrated widespread H2S release in organelles and overlapping in mitochondria. Given that GYY4137 releases intracellular H2S and that zinc co-administration inhibited mitochondrial oxygen consumption, we next assessed the mitochondrial membrane potential with TMRM in HUVECs treated with GYY4137, zinc chloride, and the combination of both (Figure 4C). While the treatment with GYY4137 or zinc chloride alone did not affect the mitochondrial membrane potential, co-treated cells showed an immediate drop in mitochondrial membrane potential. In addition, mitochondrial morphology was assessed for the same conditions with electron microscopy (Figure 4D,E), whereas mitochondrial numbers were unaffected by treatment with GYY4137 or zinc chloride alone, co-treated cells showed a decrease in mitochondrial number (Figure 4E and Figure S3B,C), in line with the observed decrease in cellular oxygen consumption under these conditions (Figure 2A). Thus, H2S release from GYY4137 is widely distributed inside HUVECs, including mitochondria, and together with zinc, leads to the formation of mito-toxic zinc-sulfide precipitates, resulting in a reduced number of mitochondria. ## 4.1. Sulfide Oxidation by Human Endothelial Cells Hydrogen sulfide is widely used as a therapeutic intervention to improve disease outcomes in experimental models. To translate the experimental use of H2S donors into clinical applications, the processing of H2S inside the cell must be explored to understand the final distribution for dosing. Endothelial cells constitute the first barrier for parenteral administered circulating H2S and H2S-donors to reach any end organ. The present study demonstrates that human endothelial cells express a functional SQOR. Maximal stimulation of SQOR in HUVECs had the consequence that electrons from H2S accounted for up to $40\%$ of total electron flow in the respiratory chain. We further eliminated the possibility of SQOR expression in HUVECs to be artefactual and resulting from cell culture conditions, as similar responses were found in freshly isolated cells. In addition, the widely used H2S-donor GYY4137 was assessed via SQOR in human endothelial cells to measure actual H2S release, demonstrating a dose-dependent (1 mM–10 mM) increase in oxygen consumption. The H2S release of GYY4137 increases oxygen consumption in HUVECs (which contain an SQOR), while this effect was absent in SH-SY5Y cells (lacking an SQOR). Furthermore, co-incubation of cells with GYY4137 and zinc chloride leads to the formation of mito-toxic zinc-sulfide precipitates as observed by electron microscopy and leads to mitochondrial loss, thereby confirming relevant intramitochondrial levels of H2S derived from GYY4137. Together, we demonstrated endothelial cells to scavenge H2S by SQOR, which allowed us to assess the H2S release of GYY4137 on the mitochondria of human endothelial cells. ## 4.2. H2S Donation by GYY4137 Influences Mitochondrial Oxygen Consumption Despite the beneficial effects of GYY4137, the H2S amount released by GYY4137 remains unclear in living systems. The intracellular H2S release rate of GYY4137 has been questioned by studies showing different release rates and measured outside the cell, showing GYY4137 concentration of 1 mM releasing H2S levels ranching from 2 µM to 100 µM [9,26,27,28,29,30,31]. The assays used so far to assess H2S-release by GYY4137 in vitro, such as the methylene blue method, influence acidity, thereby shifting the equilibrium towards H2S outside the living system, questioning its relevance for assessing intracellular H2S [17]. Under physiological conditions, the endogenous flow of sulfide release is expected to be considerably lower than the flux required for this maximal stimulation of SQOR [32]. Consequently, under physiological conditions, SQOR operates well below its maximal rate, hence with a large enzymatic reserve able to avert sulfide accumulation. Moreover, it indicates intracellular sulfide concentrations that would remain far below the SQOR affinity constant (Km) for sulfide, which is around 1 µM [32,33]. To demonstrate H2S release from GYY4137 to increase H2S levels inside mitochondria, we administered zinc ions. The toxicity of zinc for cellular respiration in the presence of GYY4137 is expected to result from the intracellular formation of ZnS and constitutes a further argument for the intracellular release of sulfide from GYY4137. Two factors could cooperate to enhance mitochondrial sensitivity to ZnS formation: mitochondrial accumulation of the Zn2+ driven by its membrane potential and/or intramitochondrial generation of sulfide. Altogether, GYY4137 demonstrates that measurable H2S release takes place within cells, which is removable by zinc ions forming the highly insoluble zinc sulfide (ZnS). Nevertheless, the sulfide release from GYY4137 is slow, necessitating mM concentrations of GYY4137 to detect SQOR activity in acute experiments. The present study clarifies this issue by demonstrating unambiguously that GYY4137 administration to cells causes a direct increase in mitochondrial oxygen consumption with characteristics fully consistent with the mitochondrial sulfide oxidation by SQOR. ## 4.3. GYY4137 Administration and Consequences of Intracellular (Autocrine) H2S Release Here, we provided insight into the ability of GYY4137 to release H2S, resulting in increased oxygen consumption. The slow-release rate of sulfide by GYY4137 is expected to generate a steady state with a low sulfide concentration in cells. The steady-state concentration would result from the balance between sulfide generation and elimination rates with a prominent role for SQOR, if present, in elimination. Therapeutic effects are expected to originate from the sum of endogenous and GYY4137-derived sulfide release, generating an increased steady-state concentration of sulfide, in turn resulting in an oxygen-dependent effect on mitochondrial bioenergetics and/or stimulation of diverse sulfide signaling pathways [32]. However, it should be mentioned that hypoxia leads to increased production of endogenous H2S and that inhibiting sulfide-producing enzymes or chemically scavenging sulfide has also demonstrated protective effects against ischemia/reperfusion, comparable to the administration of exogenous sulfide donors. SQOR may exert its protective effects by scavenging H2S, as sulfide pre-conditioning in mice by breathing H2S led to the upregulation of SQOR and made the mice more tolerant to hypoxia [34]. Given that endothelial cells are the first barrier to pass for plasma-bound GYY4137, it is essential to assess whether SQOR processes sulfide that is intra or extracellularly generated from GYY4137-released H2S in endothelial cells by direct measurement of mitochondrial SQOR activity. Although we demonstrate the direct effects of GYY4137-derived H2S on mitochondrial oxygen consumption of endothelial cells, it remains unknown to what extent these effects of GYY4137-derived H2S are mediated by its direct effects on endothelial cells or whether GYY4137 also diffuses into target tissues. To answer this question, future pharmacokinetic studies would be needed to assess the distribution of GYY4137 into target tissues. Until then, we should realize that the beneficial effects of parenteral-administered H2S-donors such as GYY4137 are likely mediated by their effect on endothelial cells. Further, genetically silencing SQOR would allow excluding alternative pathways that may contribute to H2S catabolism in human endothelial cells. ## 4.4. Effects of H2S on Protein Function Protein activity can be affected by post-translational modification. H2S can modify proteins post-translationally through a process called persulfidation, which affects protein activity, localisation, and interactions with other proteins. SQOR and thiosulphate sulfurtransferase (TST) are enzymes that produce persulfides (RSSH) during H2S oxidation and thereby can affect post-translational protein modifications [35]. Persulfidation mainly occurs on cysteine residues, preventing them from being oxidized by free radicals and allowing the preservation of protein function [36]. The availability of H2S for persulfidation depends on cellular redox status, and during oxidative stress, the number of substrates such as cysteine for H2S production may be limited [37]. Furthermore, the indirect effect of H2S can affect protein function. As such, the reduction of intramitochondrial Fe3+ to Fe2+ that is catalyzed by H2S affects the function of cytochrome c and phosphodiesterase proteins [38]. To our knowledge, whether persulfidation affects SQOR activity is yet unknown. ## 5. Conclusions In endothelial cells, the release of H2S via GYY4137 increases mitochondrial H2S levels. Because endothelial cells possess an SQOR, GYY4137-released sulfide increases mitochondrial oxygen consumption, which is absent in SH-SY5Y cells without SQOR. H2S can increase the total mitochondrial electron flow in endothelial cells by up to $40\%$ in the presence of SQOR. 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--- title: Progranulin Deficiency Induces Mitochondrial Dysfunction in Frontotemporal Lobar Degeneration with TDP-43 Inclusions authors: - Guiomar Rodríguez-Periñán - Ana de la Encarnación - Fermín Moreno - Adolfo López de Munain - Ana Martínez - Ángeles Martín-Requero - Carolina Alquézar - Fernando Bartolomé journal: Antioxidants year: 2023 pmcid: PMC10044829 doi: 10.3390/antiox12030581 license: CC BY 4.0 --- # Progranulin Deficiency Induces Mitochondrial Dysfunction in Frontotemporal Lobar Degeneration with TDP-43 Inclusions ## Abstract Loss-of-function (LOF) mutations in GRN gene, which encodes progranulin (PGRN), cause frontotemporal lobar degeneration with TDP-43 inclusions (FTLD-TDP). FTLD-TDP is one of the most common forms of early onset dementia, but its pathogenesis is not fully understood. Mitochondrial dysfunction has been associated with several neurodegenerative diseases such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS). Here, we have investigated whether mitochondrial alterations could also contribute to the pathogenesis of PGRN deficiency-associated FTLD-TDP. Our results showed that PGRN deficiency induced mitochondrial depolarization, increased ROS production and lowered ATP levels in GRN KD SH-SY5Y neuroblastoma cells. Interestingly, lymphoblasts from FTLD-TDP patients carrying a LOF mutation in the GRN gene (c.709-1G > A) also demonstrated mitochondrial depolarization and lower ATP levels. Such mitochondrial damage increased mitochondrial fission to remove dysfunctional mitochondria by mitophagy. Interestingly, PGRN-deficient cells showed elevated mitochondrial mass together with autophagy dysfunction, implying that PGRN deficiency induced the accumulation of damaged mitochondria by blocking its degradation in the lysosomes. Importantly, the treatment with two brain-penetrant CK-1δ inhibitors (IGS-2.7 and IGS-3.27), known for preventing the phosphorylation and cytosolic accumulation of TDP-43, rescued mitochondrial function in PGRN-deficient cells. Taken together, these results suggest that mitochondrial function is impaired in FTLD-TDP associated with LOF GRN mutations and that the TDP-43 pathology linked to PGRN deficiency might be a key mechanism contributing to such mitochondrial dysfunction. Furthermore, our results point to the use of drugs targeting TDP-43 pathology as a promising therapeutic strategy for restoring mitochondrial function in FTLD-TDP and other TDP-43-related diseases. ## 1. Introduction Heterozygous loss-of-function (LOF) mutations in granulin (GRN) gene leading to progranulin (PGRN) happloinsufficiency have been identified as a major cause of familial frontotemporal lobar degeneration with TDP-43 accumulation (FTLD-TDP) [1,2,3,4,5,6]. FTLD-TDP patients exhibit behavioral changes and language difficulties associated with the neuronal death in the frontal and temporal lobar brain cortex. Currently, the mechanisms by which reduced levels of PGRN lead to neurodegeneration are still unknown. Mitochondrial dysfunction has been shown to contribute to neuronal death in neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD) and amyotrophic lateral sclerosis (ALS) [7,8,9,10,11]. Recent evidence has shown that PGRN plays an important role in regulating mitochondrial homeostasis and activity [12,13]. Furthermore, previous results from our lab showed that PGRN insufficiency regulated the intrinsic/mitochondrial apoptosis pathway in GRN knockdown (KD) SH-SY5Y neuroblastoma cells [14,15] and peripheral cells from FTLD-TDP patients carrying a LOF GRN mutation (c.709-1G > A) [16]. Altogether, this evidence suggests that mitochondrial dysfunction could also contribute to neurodegeneration in FTLD-TDP linked to PGRN deficiency. Therefore, understanding the molecular mechanisms by which PGRN deficiency leads to mitochondrial dysfunction in FLD-TDP may lead to the identification of new therapeutic strategies. In this work, we have investigated the effect of PGRN deficit on mitochondrial function, dynamics and degradation, using a cellular model of FTLD-TDP with PGRN deficiency (SH-SY5Y GRN KD cells) and lymphoblasts from FTLD-TDP patients carrying a LOF mutation in the GRN gene (c.709-1G > A). Our results showed that PGRN deficiency induced mitochondrial damage in both FTLD-TDP models. Interestingly, we found that because of the autophagy impairment associated with PGRN loss, the damaged mitochondria in PGRN-deficient cells failed to be degraded, leading to mitochondrial accumulation. Importantly, the treatment with TDP-43 phosphorylation inhibitors rescued mitochondrial function in GRN KD cells, suggesting a key role for TDP-43 pathology in the mitochondrial dysfunction observed in PGRN-deficient cells. Together, our results indicate that mitochondrial impairment might contribute to neuronal death in FTLD-TDP associated with LOF GRN mutations and that modulating TDP-43 phosphorylation might represent a good therapeutic approach to rescue the mitochondrial dysfunction and the consequent neurodegeneration in FTLD-TDP and other TDP-43 proteinopathies. ## 2.1. Cell Lines Culture and Treatments The control and stable GRN KD human neuroblastoma SH-SY5Y cells (Clone # 207) were a generous gift from Drs. Alvin P. Joselin and Jane Y. Wu from the Center for Genetic Medicine (Northwestern University, Chicago, IL, USA). These lines were generated using the pSUPERIOR RNAi construct containing a sequence of 19 nucleotides targeting human GRN, as was previously described [14,17]. Cells were cultured in Dulbecco’s Modified Eagle Medium (DMEM; Thermo Fisher Scientific, Waltham, MA, USA), supplemented with $10\%$ (v/v) heat-inactivated fetal bovine serum (FBS; Thermo Fisher Scientific, MA, USA) and $1\%$ penicillin/streptomycin (P/S; Thermo Fisher Scientific, MA, USA). When necessary, DMEM was supplemented with other compounds as follows: (i) to block autophagy, SH-SY5Y cells were cultured for 12 h in DMEM containing 100 nM of V-ATPase inhibitor bafilomycin A1 (BafA1; Sigma-Aldrich, MO, USA) and (ii) to block TDP-43 phosphorylation, SH-SY5Y cells were cultured for 48 h in DMEM containing the casein kinase 1 (CK1) inhibitors IGS-2.7 and IGS-3.27 (5 μM). These two small molecules were synthetized in our laboratories according to procedures previosly described [18,19]. The lymphoblastic cell lines used in this study (Table 1) were generated in our laboratory by infecting peripheral blood lymphocytes from FTLD-TDP patients and control subjects with Epstein Barr virus (EBV), as previously described [20]. Lymphoblastic cell lines were grown in suspension using RPMI-1640 medium supplemented with $1\%$ P/S and 10 % (v/v) FBS. PGRN levels were measured in plasma samples from all subjects involved in this study using the PGRN ELISA kit AG-45A-0018YEK-KI01 (AdipoGene, Füllinsdorf, Switzerland), following the manufacturer’s protocol. Peripheral blood samples of all the individuals enrolled in this studio were taken after obtaining written informed consent of the patients or their relatives. All study protocols were approved by the Donostia Hospital and the Spanish Council of Higher Research Institutional Review Board ($\frac{01}{01}$/2006) and are in accordance with National and International Guidelines (Declaration of Helsinki). ## 2.2. Cell Lysates and Immunoblot To extract the proteins, cells were harvested, washed with phosphate buffered saline (PBS; Thermo Fisher Scientific, MA, USA) and lysed with NP-40 lysis buffer (50 mM Tris pH 7.4, 150 mM NaCl, 50 mM NaF, $1\%$ Nonidet P40) containing mini-complete protease and phosphatase inhibitor cocktails (Roche). Lysates were centrifuged at 15,000 rpm for 15 min at 4 °C to obtain the supernatant containing soluble proteins. To perform the immunoblot, protein concentration was determined by bicinchoninic acid (BCA) assay (Thermo Fisher Scientific, MA, USA) and then equivalent amounts of protein were separated on 4–$12\%$ SDS-PAGE gels (Thermo Fisher Scientific, MA, USA) and transferred onto PVDF membranes (Millipore, MA, USA). Membranes were blocked at room temperature with $5\%$ non-fat milk or $5\%$ BSA and incubated at 4 °C overnight with the following primary antibodies: rabbit anti-Progranulin (PGRN; 1:500, Abcam, Cambridge, UK), rabbit anti-TDP-43 (10782-2-AP, 1:2000 Proteintech, IL, USA), rabbit anti-phosphorylated TDP-43 (23309-1-AP, 1:500 Proteintech, IL, USA), rabbit anti-TDP-43 C-terminal (12892-1-AP, 1:1000 Proteintech, IL, USA), mouse anti-complex V-β subunit (CxVβ, 1:1000; Abcam, Cambridge, UK), rabbit anti-peroxisome proliferator-activated receptor γ co-activator 1α (PGC1α, 1:200; Santa Cruz Biotechnologies, CA, USA), mouse anti-Mitofusin1 (Mfn1, 1:1000; Abcam, Cambridge, UK), mouse anti-Mitofusin2 (Mfn2, 1:1000; Abcam, Cambridge, UK), mouse anti-mitochondrial Dynamin-like GTPase (Opa1, 1:1000; Novus Biologicals, CO, USA), rabbit anti-Dynamin-related protein 1 (Drp1, 1:1000, Cell Signalling Technology, MA, USA), rabbit anti-mitochondrial Fission protein 1 (FIS1, 1:1000, Abcam, Cambridge, UK), mouse anti-β-actin HRP (1:25,000; Abcam, Cambridge, UK), rabbit anti-p62/SQSTM1 (1:20,000. Abcam, Cambridge, UK) and rabbit anti-LC3 (1:1000, Novus Biologicals, CO, USA). Prior to band visualization, the membranes were incubated for 1 h at room temperature with species-specific horseradish peroxidase conjugated secondary antibodies as follows: goat anti-rabbit HRP secondary antibody (1:5000; Thermo Fisher Scientific, MA, USA) and goat anti-mouse HRP secondary antibody (1:5000; Abcam, Cambridge, UK). Bands were visualized by chemiluminescent substrate detection (ECL Clarity; Bio Rad, CA, USA) system using the ImageQuant LAS 4000 system (GE Healthcare, IL, USA). Images showing gels/blots and fluorescence images are in compliance with the digital image and integrity policies. Protein band densities were quantified using Fiji software (https://imagej.net/ (accessed on 11 January 2023)). ## 2.3. Quantitative Real-Time PCR Total RNA from lymphoblasts was extracted using Trizol (Invitrogen, Alcobendas, Madrid, Spain) and was used to perform a qPCR as previously described [21]. Briefly, RNA was treated with DNase I Amplification Grade (Invitrogen, Alcobendas, Madrid, Spain) and then transcribed into cDNA using the Superscript III Reverse Transcriptase kit (Invitrogen, Alcobendas, Madrid, Spain). Quantitative real-time polymerase chain reaction (PCR) was performed in triplicates using the TaqMan Universal PCR MasterMix No Amperase UNG reagent (Applied Biosystems, Alcobendas, Madrid, Spain) and the Bio-Rad iQ5 system with a thermal profile of an initial 5 min melting step at 95 °C followed by 40 cycles at 95 °C for 10 s and 60 °C for 60 s. GRN relative messenger RNA (mRNA) levels were normalized to β-actin expression using the simplified comparative threshold cycle delta–delta CT method (2-[ΔCT PGRN -ΔCT actin]). Primers were designed using the Universal ProbeLibrary for Human (Roche Applied Science, Madrid, Spain) and used at a final concentration of 20 μM (GRN primers: 5′-tctgtagtctgagcgctaccc-3′ and 5′-agggtccacatggtctgc-3′; β-actin primers: 5′-ccaaccgcgagaagatga-3′ and 5′-ccagaggcgtacagggatag-3′). ## 2.4. Cell Viability and Apoptosis Measurement Cell viability was assessed using the MTT assay. Cells were seeded in triplicate in 96-well plates, and 72 h later, 10 µL of 5 mg/mL 3-(4,5-dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide reactive (MTT; Sigma-Aldrich, St. Louis, MO, USA) was added to each well containing 100 µL of media and incubated for 4 h at 37 °C to allow the viable cells to reduce the MTT to formazan. Purple formazan crystals were then dissolved in 100 µL DMSO and the absorbance was measured at 595 nm using a microplate reader (EnSpire, PerkinElmer Waltham, MA, USA). Apoptosis was assessed by the microscopic examination of nuclei morphology. To do so, cells were stained with DAPI (4,6-diamidino-2 phenylindole) (Thermo Fisher Scientific, MA, USA) and then imaged using a Leica TCS SP5 confocal microscope (Leica Microsystems, Wetzlar, Germany) with an excitation peak at 359 nm and an emission peak at 457 nm. Cells displaying highly condensed nuclei, or pyknotic nuclei, were considered apoptotic cells. ## 2.5. Measurement of Mitochondrial Membrane Potential (ΔΨm) Mitochondrial membrane potential (ΔΨm) was analyzed using the cell-permeant fluorescent dyes tetramethylrhodamine ethyl ester (TMRE, Thermo Fisher Scientific, MA, USA) and tetramethylrhodamine methyl ester (TMRM, Thermo Fisher Scientific, MA, USA), according to previously established protocols [22,23]. Briefly, cells were seeded either in 96-well plates or in 6-well plates on 25 mm coverslips and 48 h after seeding, cells were incubated with 40 nM TMRE or TMRM in a HEPES-buffered salt solution (HBSS) (156 mM NaCl, 3 mM KCl, 2 mM MgSO4, 1.25 mM KH2PO4, 2 mM CaCl2, 10 mM glucose and 10 mM HEPES; pH adjusted to 7.35 with NaOH) for 40 min at 37 °C. Then, TMRE/TMRM fluorescence was assessed using either a POLARstar Galaxy spectrofluorimeter (BMG Labtechnologies, Offenburg, Germany) or a Zeiss 510 confocal microscope equipped with META detection system (Zeiss, Oberkochen, Germany) with 40× oil immersion objective. In both cases, excitation wavelength was 560 nm and emission was detected above 580 nm. Microscope images were analyzed using the Volocity software (Quorum Technologies, Ontario, Canada) and TMRM values for untreated cells were set to $100\%$. ## 2.6. Reactive Oxygen Species (ROS) Measurement Intracellular accumulation of ROS was determined using the fluorescent probe CM-H2DCFDA (Thermo Fisher Scientific, MA, USA). To do so, control and GRN KD SH-SY5Y cells were seeded in 96-well plates, and 48 h later, cells were loaded with 10 µM CM-H2DCFDA for 30 min. Fluorescence measurements were carried out using a POLARstar Galaxy spectrofluorimeter (BMG Labtechnologies, Offenburg, Germany). The Excitation wavelength used was 495 nm and emission was detected above 510 nm. Mitochondrial ROS levels were measured using MitoSox (Thermo Fisher Scientific, MA, USA), a fluorogenic dye specifically targeted to mitochondria in living cells, which produces red fluorescence when oxidized by superoxide. SH-SY5Ycells were cultured in 6-well plates on 25 mm coverslips during 48 h and then incubated with 5 µM MitoSox in HBSS for 30 min at room temperature. Z-stack images were obtained using a Zeiss 510 confocal microscope equipped with META detection system (Zeiss, Oberkochen, Germany) and 40× oil immersion objective using an excitation wavelength of 510 nm. Emission was detected at 580 nm. Fluorescence intensity was quantified using the Volocity software (Quorum Technologies, Ontario, Canada). ## 2.7. Measurement of Cellular Oxygen Consumption Cellular oxygen consumption rate (OCR) was measured using a Seahorse XF24 Extracellular Flux Analyzer (Seahorse, Agilent Technologies, CA, USA). A total of 20,000 control and GRN KD SH-SY5Y cells per well were plated in DMEM supplemented with $10\%$ FBS and $1\%$ P/S. Then, 24 h later, cell growing media was replaced by 25 mM glucose, 1 mM Pyruvate and 2 mM L-glutamine containing XF Base medium. Cells were incubated for 1 h in a CO2-free incubator at 37 °C allowing temperature and pH equilibration before loading into the XF24 analyzer. Mitochondrial function was determined through sequential addition of 1 µM oligomycin (Sigma-Aldrich, MO, USA), 0.5 µM carbonylcyanide-p-trifluoromethoxyphenylhydrazone (FCCP; Sigma-Aldrich, MO, USA) and 1 µM antimycin (Sigma-Aldrich, MO, USA)/1 µM rotenone (Sigma-Aldrich, MO, USA). This sequential addition allowed us to determine the basal oxygen consumption, oxygen consumption-linked to ATP synthesis (ATP), maximal respiration and the cellular spare capacity. ## 2.8. ATP Levels Measurement In SH-SY5Y cells, ATP was measured using a FRET-based ATP-plasmid indicator (AT1.03 sensor) kindly provided by Dr. H. Imamura, following previously established protocols [24]. Briefly, control and GRN KD SH-SY5Y cells were seeded in 6-well plates on 25 mm coverslips and then transfected with the FRET-based ATP-plasmid indicator using Effectene transfection reagent (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. One day after transfection, cell culture media were replaced with HBSS medium plus Ca2+ and Mg2+ and then cells were subjected to a time-dependent fluorescence imaging using a Zeiss 510 LSM confocal microscope with META detection system. Images were obtained using a 63× oil-immersion objective. Excitation of cyan fluorescent protein was 405 nm and emission was detected between 460 and 510 nm. Yellow fluorescent protein was excited using the 405 nm laser line and emission was detected using a band-pass filter from 515 to 580 nm. Minimal illumination intensity was kept to avoid phototoxicity (at 0.1–$0.2\%$ of laser output) and the pinhole was adjusted to give an optical slice of ~2 μm. The ratio-metric analysis of the yellow- and cyan-fluorescent proteins was assessed using the ZEN software (Zeiss, Oberkochen, Germany) and allowed the estimation of ATP kinetics within single cells. In lymphoblasts, ATP basal levels were measured by using the Vialight plus assay kit (Lonza, Verviers, Belgium). This assay allows the measurement of ATP present in all metabolically active cells and is based upon the bioluminescent measurement of ATP. Lymphoblasts were seeded in 24-well plate and after 48 h, cells were lysed using the cell lysis buffer provided by the kit. Protein concentration of the cell lysates was estimated using the BCA assay (Thermo Fisher Scientific, MA, USA) and samples were diluted in cell lysis buffer to obtain 0.1 μg/μL protein concentration. Then, 100 μL of the protein solutions were transferred to each well of a 96-well plate containing 100 μL of ATP monitoring reagent plus (in triplicates). Plates were incubated for 2 min at room temperature and then luminescence was measured using an EnSpire microplate reader (PerkinElmer Waltham, MA, USA). ## 2.9. Mitochondrial Mass Mitochondrial mass was measured using the cell permeable mitochondria-selective dye MitoTracker Green FM (Thermo Fisher Scientific, MA, USA). MitoTracker is a fluorescent dye that localizes to the mitochondrial matrix regardless of the mitochondrial membrane potential and covalently binds to mitochondrial proteins by reacting with free thiol groups of cysteine residues. GRN KD and control SH-SY5Y cells were seeded in 96- or 24-well plates. For this assay, the same number of GRN KD and control SH-SY5Y cells were seeded in each well. Then, 72 h later, SH-SY5Y cells were incubated for 15 min with 400 nM of MitoTracker Green FM and then fluorescence was determined by using either a confocal microscope Leica TCS SP5 (Leica Microsystems, Wetzlar, Germany) or a spectrofluorimeter (BMG Labtechnologies, Offenburg, Germany). In both cases, excitation wavelength was 490 nm and emission was detected at 516 nm. Mitochondrial mass was also assessed by analyzing the levels of the mitochondrial structural protein complex V-β subunit (CxVβ) by immunoblotting or immunofluorescence, as previously described [25,26,27]. Briefly, to assess mitochondrial mass by immunoblotting, equal amounts of total protein were loaded for all the samples and CxVβ levels were normalized using the cytoskeleton protein β-actin. To assess mitochondrial mass by immunofluorescence, cells were seeded in 24-well plates on 13 mm coverslips and stained using an anti-CxVβ antibody. Then, Z-stack images were obtained using a Zeiss 510 LSM confocal microscope with META detection system with a 40× oil immersion objective, which were analyzed using the Volocity software (Quorum Technologies, Ontario, Canada), which allowed us to visualize, analyze and quantify 3D fluorescence images. ## 2.10. Immunofluorescence and Colocalization Analysis SH-SY5Y cells were seeded at an initial density of 1 × 105 cells/well in 24-well plates containing 12 mm coverslips, and 48 h after seeding, half of the coverslips were treated with BafA1 (100 nM) for 12 h and then fixed with $4\%$ PFA. For the immunostaining, cells were blocked with $2\%$ BSA in PBS, permeabilized with $0.1\%$ saponin, incubated with primary antibodies (rabbit anti-p62/SQSTM1, 1:200 and mouse anti-CxVβ, 1:200) at room temperature. Then, 1 h later, cells were incubated with the corresponding secondary antibodies (Alexa-fluor 488 goat anti-rabbit, 1:200, and Alexa-fluor 568 goat anti-mouse, 1:200, both from Thermo Fisher Scientific, MA, USA) for 45 min at room temperature. Preparations were mounted using proLong Gold antifade mountant with DAPI (4′,6-diamidino-2-phenylindole) (Thermo Fisher Scientific, MA, USA) and visualized using a Zeiss 510 LSM confocal microscope with META detection system and a 40× oil immersion objective. Fluorescence intensity and colocalization were analyzed using Volocity software (Quorum Technologies, Ontario, Canada). Pearson’s correlation coefficient was used to estimate the colocalization between green and red channels. ## 2.11. Statistical Analysis Student’s t test, one-way and two-way analysis of variance (ANOVA) statistical analyses were performed using GraphPad Prism 6. Bonferroni’s analysis was used to analyze the statistical significance between multiple groups. Plots show means ± Standard Error of the Mean (SEM) of all experiments performed. Differences were considered statistically significant when $p \leq 0.05.$ ## 3.1. GRN KD SH-SY5Y Cells Recapitulate Pathological Characteristics of FTLD-TDP Previous reports have demonstrated that PGRN depletion induces cytosolic TDP-43 accumulation in several cell models [18,28,29], suggesting that PGRN-deficient cells could be used to study FTLD-TDP. Here, we have investigated if GRN KD SH-SY5Y cells also recapitulate key aspects of FTLD-TDP pathophysiology such as TDP-43 phosphorylation/cleavage, neuronal death and oxidative stress [30,31,32]. We found that GRN depletion in SH-SY5Y cells led to increased TDP-43 protein phosphorylation at S$\frac{409}{410}$ and cleavage into 25 kDa C-terminal fragments (Figure 1A–D and Figure S1). Furthermore, GRN KD cells exhibited decreased cell viability (Figure 1E) and increased apoptosis (Figure 1F), as was indicated by the presence of morphological features of apoptotic cell death such as increased chromatin condensation and the formation of pyknotic nuclei (Figure 1F). To determine if GRN deficiency induced changes in oxidative stress status in SH-SY5Y cells, we assessed the cytosolic and mitochondrial levels of reactive oxygen species (ROS) by measuring fluoresce of CM-H2DCFDA and MitoSox probes, respectively. Both cytosolic (Figure 1G) and mitochondrial (Figure 1H) ROS production were increased in PGRN-deficient cells, compared with control SH-SY5Y cells. Together, these results indicate that GRN KD SH-SY5Y cells are an adequate model to study FTLD-TDP, as they mimic some of the main hallmarks of FLTD-TDP. ## 3.2. PGRN Insufficiency Impairs Mitochondrial Bioenergetics in SH-SY5Y Cells and FTLD-TDP Patient’s Lymphoblasts Because mitochondria play a key role in ROS production and apoptotic cell death, we studied the mitochondrial function in our in vitro GRN KD cellular model. To do so, we analyzed the mitochondrial membrane potential (ΔΨm), which reflects the mitochondrial health and function, in control and GRN KD SH-SY5Y cells using the TMRE probe. TMRE is a cell permeant, positively charged fluorescent dye that accumulates in active mitochondria. GRN KD cells exhibited a significant reduction of the TMRE signal (Figure 2A), indicating that PGRN deficiency induced mitochondrial depolarization. Consistent with the reduced ΔΨm, we found that GRN KD cells exhibited reduced mitochondrial ATP levels (Figure 2B). To further investigate how PGRN deficiency affects mitochondrial bioenergetics, we estimated the oxygen consumption rate (OCR) using the Seahorse XF analyzer (Figure 2C). GRN KD cells showed reduced basal OCR (Figure 2D). Consistent with the above results, after FoF1-ATP synthase inhibition with oligomycin (Figure 2C) PGRN-deficient cells demonstrated lower ATP production linked to respiration (Figure 2E). In addition, GRN KD cells displayed lower maximal respiration (Figure 2F) and spare capacity (Figure 2G), both obtained after addition of the mitochondrial uncoupler FCCP (Figure 2C). Together, these findings indicate that PGRN deficiency could be associated with reduced activity or lack of substrates for the mitochondrial respiratory complexes I or II. We then investigated if the mitochondrial bioenergetics deficits observed in the GRN KD model could be extensible to FTLD-TDP patients. To do so, we used lymphoblastoid cell lines generated from FTLD-TDP patients carrying the c.709-1G > A heterozygous mutation in the GRN gene. This mutation is predicted to cause exon eight skipping, frameshift and premature translation termination, resulting in nonsense-mediated mRNA decay [33]. As expected, FTLD-TDP patients carrying this mutation exhibited decreased PGRN levels in plasma, compared with control subjects (Figure S2A). Furthermore, lymphoblastoid cell lines generated from the c.709-1G > A GRN mutation carriers exhibited reduced GRN mRNA and PGRN protein levels (Figure S2B–D). Importantly, similarly to GRN KD SH-SY5Y cells, lymphoblasts from FTLD-TDP patients carrying the c.709-1G > A GRN mutation exhibited depolarized mitochondria (Figure 2H) and reduced ATP levels when compared with lymphoblasts from healthy subjects (Figure 2I). These observations suggest that mitochondrial impairment might be a pathological feature of FTLD-TDP. ## 3.3. Progranulin Deficiency Increases Mitochondrial Mass To explore whether the impairment of mitochondrial bioenergetics in GRN KD cells could be explained by a reduced amount of mitochondria, we measured mitochondrial mass in control and PGRN-deficient cells. Interestingly, GRN KD SH-SY5Y cells exhibited higher mitochondrial mass as assessed using the MitoTracker Green FM fluorescence signal (Figure 3A,B). The increased mitochondrial mass in PGRN-deficient cells was then validated by immunoblot by measuring the levels of the mitochondrial structural protein complex V-β subunit (CxVβ) (Figure 3C and Figure S3A) and normalized by the levels of the cytoskeleton protein β-actin. Remarkably, the accumulation of mitochondria in GRN KD cells was not the result of increased mitochondrial biogenesis, as indicated by the presence of equal levels of the mitochondrial biogenesis marker PGC1α in both control and GRN KD cells (Figure 3D and Figure S3B). These results suggested that the PGRN deficiency-induced increase in mitochondrial mass is not due to enhanced mitochondrial biogenesis but may be the result of impaired degradation of damaged mitochondria. ## 3.4. Impaired Autophagy in GRN KD Cells Blocks the Removal of Damaged Mitochondria Mitochondria are dynamic organelles that constantly fuse and divide. The processes of mitochondrial fusion and fission, known as mitochondrial dynamics, are key mechanisms for the mitochondrial quality control as they regulate the removal of damaged mitochondria by mitophagy. We studied the mitochondrial dynamics in the control and GRN KD SH-SY5Y cells by analyzing the levels of fusion and fission proteins such as mitofusin 1 and 2 (Mfn1-2), Opa1, FIS1 and Drp1 (Figure 4A,B, Figures S4 and S5). PGRN-deficient cells showed decreased levels of the mitochondrial fusion protein Opa1 (Figure 4A and Figure S4C) and increased levels of the mitochondrial fission proteins FIS1 and Drp1 in GRN KD cells, compared with control cells (Figure 4B and Figure S5), demonstrating an imbalance in the mitochondrial fusion/fission dynamics towards increased mitochondrial fission. To study whether the increased mitochondrial fission targeted the mitochondria for their disposal by mitophagy, we assessed the colocalization of mitochondria with the mitophagy marker p62 [34]. PGRN deficiency increased the colocalization of the mitochondrial marker CxVβ with p62 (Figure 4C and Figure S6), showing that damaged mitochondria were targeted for mitophagy in PGRN-deficient cells. It has been previously demonstrated that PGRN plays an important role in regulating autophagy [35] and that PGRN depletion leads to autophagy blockage [36]. To address whether autophagy was also impaired in GRN KD SH-SY5Y cells, we measured autophagic flux by monitoring changes in the levels and localization of the autophagy adaptor p62 and the autophagosome marker LC3II (microtubule-associated protein 1 light chain 3B), before and after bafilomycin A1 (BafA1) treatment (Figure S7) [37]. Under basal conditions, GRN KD cells showed increased p62 levels together with decreased LC3II levels, compared with control cells (Figure S7A,B). Notably, when we added Baf1A, a V-ATPase inhibitor that inhibits autophagosome-lysosome fusion and blocks autophagosome degradation, the rate of LC3 II formation was lower in GRN KD cells than in control cells (Figure S7A). Together, these results suggest that GRN KD SH-SY5Y cells have reduced autophagy flux, probably associated with a failure in autophagosome formation. Thus, we asked if the mitochondrial accumulation associated with PGRN deficiency could be a consequence of the autophagy failure observed in GRN-deficient cells. To do so, we measured mitochondrial mass before and after blocking autophagy with BafA1. Consistent with the above results (Figure 3A–C), GRN KD cells showed increased CxVβ staining compared with control cells (Figure 4D). BafA1 treatment induced mitochondrial accumulation in control cells but did not modify CxVβ levels in GRN KD cells (Figure 4D). These results confirmed that mitochondrial accumulation in PGRN-deficient cells was a consequence of a general failure of autophagy. ## 3.5. Inhibition of TDP-43 Phosphorylation Restores Mitochondrial Bioenergetics in GRN KD Cells GRN KD SH-SY5Y cells accumulated S409/S410 phosphorylated C-terminal fragments of TDP-43 protein. It has been reported that casein kinase-1 δ (CK-1 δ) is the kinase that phosphorylates TDP-43 at these residues [38]. We previously developed two brain-penetrant CK-1δ inhibitors inhibitors (IGS2.7 and IGS3.27) and demonstrated that both compounds decreased TDP-43 phosphorylation and accumulation as well as prevented neuronal death in FTLD-TDP patient-derived lymphoblasts [18]. Because TDP-43 pathology in FTLD could be related to mitochondrial impairment [10,39,40,41], here we investigated if the inhibition of TDP-43 phosphorylation could have an effect in the mitochondrial bioenergetics of PGRN-deficient cells. Treatment with both CK-1δ inhibitors, IGS2.7 and IGS3.27, restored the ΔΨm in SH-SY5Y GRN KD cells, with no effect on control cells (Figure 5). These results suggested that the accumulation of phosphorylated forms of TDP-43 might be responsible for the mitochondrial impairment observed in GRN KD cells. ## 4. Discussion Subjects carrying heterozygous GRN LOF mutations develop early onset FTLD-TDP, a neurodegenerative disease considered the second most common cause of dementia after AD [42]. However, the pathological mechanisms resulting in the clinical and cellular features of FTLD-TDP associated with GRN mutations are still not well understood. There is growing evidence that mitochondrial abnormalities are involved in the pathogenesis of common neurodegenerative diseases such as AD, PD and ALS [40,43,44,45], but little is known about the role of mitochondrial dysfunction in the pathogenesis of FTLD. This work was undertaken to investigate the link between mitochondrial dysfunction and FTLD-TDP associated with PGRN deficiency using a neuronal model of FTLD-TDP based on GRN gene silencing and lymphoblasts from FTLD-TDP patients carrying a GRN LOF mutation. Our results indicated that PGRN deficiency impaired mitochondrial bioenergetics in both the FTLD-TDP neuronal model and the FTLD-TDP patient’s derived lymphoblasts. Interestingly, previous reports from our lab demonstrated that PGRN deficiency induced alterations in mitochondrial/intrinsic apoptotic cell death [14,15,16]. Since apoptosis has been largely related to neuronal cell death in FTLD [46] it is likely that the mitochondrial impairment caused by PGRN deficiency may be one of the factors contributing to neuronal death in FTLD-TDP. Further analysis of the bioenergetics status of our cell model showed that PGRN deficiency was associated with lower oxygen consumption. We also observed that GRN KD cells reached poor maximal respiration rates upon addition of the FCCP uncoupler, compared with control cells. These results along with the mitochondrial depolarization and the increased ROS production in GRN KD cells suggested that mitochondrial respiration might be inhibited in a complex I-dependent manner [47,48]. Previous reports demonstrated that in ALS and FTLD models, TDP-43 bound to the mitochondrial mRNA and impaired the expression of the complex I subunits ND3 and ND6 causing complex I disassembly. Similar to our results, in these reports the dysfunctional complex I resulted in increased ROS production, mitochondrial depolarization and reduced ATP production [40,41]. Together, this evidence demonstrates that in FTLD-TDP associated with PGRN deficiency, the inhibition of mitochondrial respiration may be due to a complex I deficiency caused by the accumulation of aberrant forms of TDP-43 protein. Interestingly, lymphoblasts from FTLD-TDP patients carrying a GRN LOF mutation also exhibited mitochondrial depolarization and reduced ATP levels, suggesting that the mitochondrial impairment could be a main feature of FTLD-TDP associated with GRN mutations. Because mitochondria are crucial organelles for maintaining the physiological activity of cells, damaged mitochondria are rapidly degraded. Interestingly, PGRN-deficient cells showed accumulation of depolarized mitochondria. Among other causes, the accumulation of dysfunctional mitochondria could be associated with a defect in mitochondrial degradation. Mitochondrial degradation is regulated by mitochondrial dynamics and mitophagy [49]. Mitochondria are dynamic organelles that constantly undergo fission and fusion events. Whereas fusion helps maintain mitochondrial function, the fission process enables damaged mitochondria to be removed from the mitochondrial network for degradation by mitophagy. Our results showed that PGRN deficiency favored mitochondrial fission and the initiation of the mitophagy process, allocating the damaged mitochondria of GRN KD cells to degradation. These results agree with previous reports showing that depolarized mitochondria were degraded by mitophagy in vivo and in vitro [50,51,52]. However, although in PGRN-deficient cells the depolarized mitochondria initiated the mitophagy process, their degradation in the lysosomes was not completed. It has been demonstrated that PGRN regulates the autophagy–lysosomal pathway and that PGRN deficiency induces autophagy impairment [35,36]. In agreement with these reports, we found that PGRN deficiency induced autophagy failure in SH-SY5Y cells, implying that the accumulation of damaged mitochondria in GRN KD cells was due to a defect in the autophagy–lysosomal pathway and not to a failure of mitochondrial dynamics or mitophagy initiation. Our findings of altered mitochondrial bioenergetics, dynamics and mitophagy in PGRN-deficient cells agree with previous reports showing that PGRN acts as a regulator of mitochondrial homeostasis [13] and activity [12]. However, these reports did not demonstrate whether the effect of PGRN in regulating mitochondrial function was direct or indirect. The fact that the inhibition of TDP-43 phosphorylation restored the mitochondrial membrane potential in GRN KD cells suggests that the accumulation of phospho-TDP-43 protein might be the responsible for the mitochondrial impairment observed in PGRN-deficient cells. Several studies using murine and cell models of ALS and FTLD overexpressing wild type or mutant TDP-43 have demonstrated a link between TDP-43 and mitochondria [10,39,40,41,53], describing that TDP-43 localizes to mitochondria, causing mitochondrial damage and a reduction in mitochondrial ATP synthesis [40,41]. Our results are consistent with these previously published data and support the hypothesis that TDP-43 pathology could play an important role inducing mitochondrial dysfunction in FTLD-TDP patients carrying LOF GRN mutations. On the other hand, PGRN deficiency might also affect the mitochondrial homeostasis through other pathways unrelated to TDP-43. For example, we previously reported an overactivation of Wnt signaling in GRN KD SH-SY5Y cells and lymphoblasts from FTLD-TDP patients carrying a LOF GRN mutation [15,54,55]. Both canonical and non-canonical Wnt signaling pathways have been implicated in mitochondrial dynamics and biogenesis [56,57,58]. Thus, the impairment of Wnt signaling in GRN KD cells might also contribute to the mitochondrial dysfunction. Interestingly, more recent reports have demonstrated a mitochondrial-initiated regulation of Wnt signaling [59,60], implying a bidirectional crosstalk between mitochondria and the Wnt pathway and suggesting that mitochondrial impairment might also be responsible for the alterations in Wnt pathway observed in PGRN-deficient cells. As is summarized in Figure 6, this study describes that the partial loss of PGRN provokes the imbalance of mitochondrial bioenergetics in a neuronal-like cell model and patient-derived lymphoblasts, which might contribute to the neuronal death in FTLD-TDP. Furthermore, it demonstrates that the autophagy failure associated with PGRN deficiency blocks the degradation of impaired mitochondria in the lysosomes leading to the aberrant accumulation of damaged mitochondria in FTLD-TDP cellular models. Our results also point out that the TDP-43 pathology contributes to the mitochondrial damage observed in GRN KD cells. Interestingly, the treatment with brain penetrant phospho-TDP-43 inhibitors restores mitochondrial function in PGRN-deficient cells, suggesting that the regulation of TDP-43 pathology might prevent neuronal death in FTLD-TDP and other TDP-43-related pathologies by reverting or preventing mitochondrial dysfunction. ## 5. Conclusions This study demonstrates that PGRN deficiency causes mitochondrial dysfunction in an FTLD-TDP cell model and in lymphoblasts derived from FTLD-TDP patients, suggesting that mitochondria may be damaged in FTLD-TDP associated with LOF GRN mutations. Furthermore, we found that the autophagy failure associated with PGRN deficiency affects mitochondrial degradation, leading to the accumulation of damaged mitochondria. Importantly, our results show that the treatment with phospho-TDP-43 inhibitors restores mitochondrial function in PGRN-deficient cells, suggesting that the mitochondrial depolarization could be a consequence of TDP-43 pathology. 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--- title: Nrf2 Activation Does Not Protect from Aldosterone-Induced Kidney Damage in Mice authors: - Ronja Brinks - Christoph Jan Wruck - Jutta Schmitz - Nicole Schupp journal: Antioxidants year: 2023 pmcid: PMC10044832 doi: 10.3390/antiox12030777 license: CC BY 4.0 --- # Nrf2 Activation Does Not Protect from Aldosterone-Induced Kidney Damage in Mice ## Abstract Nuclear factor erythroid 2-related factor 2 (Nrf2) is downregulated in chronic kidney disease (CKD). Activation of Nrf2 might be a therapeutic option in CKD. Here we investigate the effect of Nrf2 activation on aldosterone (Aldo)-induced renal injury. Wild-type (WT) mice, transgenic Keap1 hypomorphic (Nrf2ꜛ, genotype results in upregulation of Nrf2 expression) mice and WT mice treated with the Nrf2 activator sulforaphane (Sulf) received Aldo for 4 weeks. In Aldo-treated mice, kidneys were significantly heavier and pathologically altered, reflected by increased urinary albumin levels and tissue damage. In Nrf2ꜛ-Aldo mice the tubule damage marker NGAL was significantly decreased. Increased oxidative damage markers (8-OHdG, 15-isoprostane F2t) were measured in all Aldo-treated groups. Aldo-increased Nrf2 amounts were mainly found in the late tubule system. The amount of phosphorylated and thus putatively active Nrf2 was significantly increased by Aldo only in WT mice. However, expression of Nrf2 target genes NQO1 and HO1 was decreased in all Aldo-infused mice. GSK3β, which promotes Nrf2 degradation, was significantly increased in the kidneys of Aldo-treated WT mice. Neither genetic nor pharmacological Nrf2 activation was able to prevent oxidative injury induced by Aldo, probably due to induction of negative regulators of Nrf2. ## 1. Introduction The main risk factors for the development of chronic kidney disease (CKD) are diabetes and hypertension. Hypertension is present in approximately $70\%$ of individuals with moderate CKD in Germany [1]. In hypertension, the renin–angiotensin–aldosterone system (RAAS) is almost always activated, and is usually also causally involved in the development of hypertension. Therefore, treatment with RAAS inhibitors such as angiotensin converting enzyme inhibitors or mineralocorticoid receptor blockers is standard therapy for achieving the first therapeutic goal in hypertensive CKD patients, the lowering of systolic blood pressure to 120 mmHg [2]. Beyond that, the therapeutic options for treating CKD and especially stopping the progression of CKD are limited. Several clinical studies focused on investigating the effects of activators of the transcription factor nuclear factor erythroid 2-related factor 2 (Nrf2) on progression of mainly diabetes-induced CKD [3,4]. This is based, on the one hand, on results of animal studies in which Nrf2 activation led not only to improved renal morphology, but also to an increase in renal function [5]. On the other hand, an increased glomerular filtration rate was observed in clinical antitumor studies after intake of the Nrf2 activator bardoxolone methyl (CDDO-Me) [6]. However, initial larger studies in CKD patients showed serious cardiac side effects of CDDO-Me, increased proteinuria and no arrest of CKD progression [3,7]. Recent studies include only patients without risk factors for heart failure [8]. It was suggested that the actual effects of Nrf2 activators in the kidney are not yet sufficiently understood for use of these compounds in the clinic [9,10]. In rats with aldosterone (Aldo)-induced hypertension and moderate kidney damage we found that the use of another Nrf2 activator, sulforaphane (Sulf), protected kidneys from injury and oxidative damage [11]. As an isothiocyanate, Sulf belongs to another substance class than CDDO-Me, which is a synthetic triterpenoid, and as a natural compound Sulf is found in cruciferous vegetables and was isolated from broccoli [12]. CDDO-Me and Sulf, being electrophilic compounds, activate Nrf2 by inhibiting its repressor Kelch-like ECH-associated protein 1 (Keap1) through interaction with one of its cysteine groups, as do all other Nrf2 activators currently in clinical trials [13]. Due to their reactivity towards thiols, these substances are not specific Nrf2 activators but can affect many targets and signaling pathways [14]. New, specific protein–protein inhibitors of the Keap1–Nrf2 interaction are being explored at the moment but have not yet reached the clinic [13]. We have recently shown that in mice with moderate kidney injury caused by increased Aldo concentrations the expression of Nrf2 target genes in kidney cells was significantly reduced despite an increased abundance of Nrf2 and its putatively activated phosphorylated form in the kidney [15]. Similar findings have been observed in patients with advanced CKD [16] and also in animal models of CKD [5,17]. Nrf2 is ubiquitously expressed in tissues and cells and, upon activation, induces transcription of antioxidant enzymes and enzymes of metabolism in response to oxidative stress or appearance of electrophiles [18]. However, Nrf2 plays a role not only in induced stress but also in maintaining redox homeostasis under physiological conditions [19]. Nrf2 belongs to the basic leucine zipper (bZIP) transcription factor family and is regulated transcriptionally, posttranscriptionally and posttranslationally [20]. Under normal physiological conditions, Nrf2 is localized in the cytoplasm and bound to its repressor Keap1 [18]. Binding of Nrf2 by Keap1 induces its degradation via the ubiquitin ligase complex (Cul3/Rbx1) [21]. However, if the cell is under oxidative stress or electrophiles are present, the thiol groups of the cysteine residues of Keap1 are modified, resulting in a conformational change in Keap1, preventing ubiquitin transfer. Keap1 inactivation thus leads to stabilization, accumulation and translocation of Nrf2 into the nucleus [22]. Furthermore, phosphorylation of Nrf2 via diverse kinases can also affect binding to Keap1. For example, it has been shown that phosphorylation of Nrf2 at serine 40 via oxidative stress-activated PKC leads to stabilization of Nrf2 and subsequent translocation to the nucleus [23]. In the nucleus, Nrf2 forms a heterodimer with small Maf (sMaf) proteins (MafF, MafG and MafK). Formation of the dimer is necessary for binding to the antioxidant responsive element (ARE) and induction of transcription of Nrf2 target genes [24]. However, there are other Keap1-independent mechanisms that may influence Nrf2 activity. For example, the kinase Fyn, which is activated via the kinase glycogen synthase kinase 3β (GSK3β), has been shown to phosphorylate Nrf2 in the nucleus, which in turn leads to export of Nrf2 out of the nucleus and its degradation [25]. GSK3β can also phosphorylate Nrf2 directly, leading to stabilization but also degradation of Nrf2 [26]. After phosphorylation by GSK3β, ubiquitination occurs via β-transducin repeat-containing protein (β-TrCP) followed by degradation in the proteasome [25]. Furthermore, the Nrf2 heterodimer in the nucleus competes for binding at the ARE with sMaf hetero- or homodimers and the Bach1/sMaf heterodimer. In this context, binding of the BTB and CNC homology 1, basic leucine zipper transcription factor 1 (Bach1)/sMaf dimer acts to repress transcription of Nrf2-regulated genes. Therefore, in the present work, we investigate the effects of both pharmacological (using Sulf) and genetic Nrf2 activation on moderate renal injury triggered by elevated levels of Aldo with a focus on the difference in the two approaches on kidney parameters and the Keap1-independent regulation of Nrf2. Sulf was chosen as the Nrf2 activator in this study because, although it has the same mode of action as CDDO-Me mentioned above, it belongs to a different class of compounds, has shown promising effects in the trial with our rats and has to date shown no severe adverse effects in the more than 70 clinical trials in which it has already been used [14]. ## 2.1. Animal Treatment Thirty-six male C57BL/6-mice (Janvier, LE Genest Saint Isle, France) were allocated randomly to four equal-sized groups at the age of 12 weeks. Additionally, 16 B6.Cg-Keap1tm2Mym(Alb-cre)21Mgn/J mice (from here on named Nrf2ꜛ), negative for Alb-cre (genotyped with the primers in Table S1), were divided into two age-matched groups of 8 animals each at an average age of 12 weeks. The strain was generated by crossing Keap1loxP/loxP with mice expressing Cre recombinase under the control of the albumin (Alb) promoter by Okawa et al. [ 27]. Osmotic minipumps were implanted (Model 1004, Alzet, Durect Coporation, Cupertino, CA, USA) subcutaneously in the neck region of the mice under inhalant isoflurane anesthesia (1.5–$2\%$, anesthesia station MiniTAG, TEM SEGA, Pessac, France). The minipumps administered 125 µg Aldo/kg × day for 28 days. The control group received $15\%$ EtOH in PBS as a solvent control. In addition, all mice had free access to food and $1\%$ (w/v) NaCl as drinking water. Eight of the WT mice infused with Aldo or solvent also received an average of 10–20 mg/kg sulforaphane (Sulf) per day, dissolved in $1\%$ NaCl, for 28 days, depending on their drinking volume. Preemptive analgesia was achieved with 5 mg/kg carprofen (Zoetis Deutschland GmbH, Berlin, Germany). The blood pressure was measured non-invasively twice weekly using the tail cuff method (Visitech Systems, Apex, NC, USA). The mice were habituated to the blood pressure measurement procedure 2 weeks before implantation of minipumps. At the beginning and the end of the experiment, the mice were placed into metabolic cages for 20 h to collect urine samples. After 4 weeks of treatment, mice were deeply anesthetized (120 mg ketamine/kg and 8 mg xylazine/kg i.m.) and perfused with ice-cold Deltadex 40 (AlleMan Pharma GmbH, Rimbach, Germany) supplemented with $1\%$ procaine hydrochloride (bela-pharm, Vechta, Germany), followed by ice-cold $0.9\%$ NaCl solution (Fresenius Kabi Deutschland GmbH, Bad Homburg, Germany). Kidneys and hearts were removed, weighed and either embedded in paraffin or snap-frozen in liquid nitrogen and stored at −80 °C. ## 2.2. Quantification of Aldosterone Serum aldosterone levels were measured using the Aldosterone ELISA Kit (BT E-5200, BioTrend, Cologne, Germany) as instructed by the manufacturer. ## 2.3. Parameters of Renal Function Renal function was assessed by measuring serum creatinine, calculating creatinine clearance and quantifying excretion of albumin, kidney injury molecule-1 (KIM-1) and neutrophil gelatinase-associated lipocalin (NGAL). Creatinine in urine and serum was determined using creatinine urinary/serum colorimetric assay kits (No. $\frac{500701}{700460}$, Cayman Chemical Company, Ann Arbor, MI, USA) according to the manufacturer’s protocol. The mouse albumin ELISA kit (EMA3201-1, Assay Pro, St. Charles, IL, USA), the mouse KIM1/TIM1 ELISA kit (PicoKineTM, EK0880, BosterBio, Pleasanton, CA, USA) and the mouse NGAL ELISA kit (KIT 042, BioPorto, Gentofte, Denmark) were used for the quantification of albumin, KIM-1 and NGAL excretion following the manufacturer’s protocol. Albumin, KIM-1 and NGAL were related to urinary creatinine. ## 2.4. Histopathology For histopathological examinations of the kidney, 3 µm paraffin sections were stained with hematoxylin and eosin, periodic acid–Schiff stain and Sirius red, which was also performed on heart sections. Both tubular and glomerular damage were determined as previously described [28]. ## 2.5. Immunohistochemistry Kidney sections (3 µm) (RM 2164, Leica, Wetzlar, Germany) were mounted on glass slides, heated at 60 °C for 1 h and deparaffinized with Roti-Histol (Roth, Karlsruhe, Germany) and ethanol. Antigen retrieval was performed with citrate buffer (DAKO Retrieval Solution, pH 6.0, Agilent Technologies, Santa Clara, CA, USA) at 95 °C for 30 min. Slides were then blocked and incubated overnight at 4 °C with the appropriate primary antibodies. The specific antibodies and dilutions were as follows: anti-γ-H2AX (#9718, 1:200, Cell Signaling, Herts, UK), anti-Nrf2 (sc-722, 1:1000, Santa Cruz Biotechnology, Dallas, TX, USA) and anti-pNrf2 (S40, ab76026, 1:1000, abcam, Cambridge, UK). Sections were next incubated with the biotinylated secondary goat anti-rabbit antibody (ab6720, 1:200, abcam, Cambridge, UK) for 45 min at room temperature. Antibody binding was visualized as previously described [11,29]. Sections were counterstained with hematoxylin. Images were acquired at 200-fold magnification. The ratio of positive to negative nuclei or areas was scored via ImageJ [30] within 10 visual fields of the cortex and 3–5 visual fields of the medulla. ## 2.6. Double Staining To localize pNrf2 in the kidney, double staining was performed to identify pNrf2 positives in different types of kidney cells. Staining of the first antigen (pNrf2) was performed as described above using diaminobenzidine (DAB) as a chromogen. After visualization of antibody binding, the protocol was repeated with an antibody against calbindin (1:200, #2173, Cell Signaling, Herts, UK) to identify distal tubular cells and cells of the early collecting duct. Proximal tubular cells were identified by the presence of the brush border, whereas glomeruli were identified by their capillary tuft (blue circles). Fifty glomeruli were analyzed per animal. The late collecting duct was identified by the absence of positive calbindin staining and brush border. The VECTOR® VIP Peroxidase Substrate Kit was used to visualize the second antigen (SK-4600, Vector Lab, Burlingame, CA, USA), which produced a purple stain. Sections were counterstained with hematoxylin and dehydrated in descending alcohol concentrations. Pictures were taken at 400-fold magnification. The ratio of positive to negative nuclei for pNrf2 on 10 visual fields was assessed via ImageJ [30] by counting only nuclei positive for the specific kidney cell identifier. ## 2.7. Quantification of 8-OhdG and 15-Isoprostane F2t in Urine 8-hydroxy-2′-deoxyguanosine (8-OHdG) in urine was quantified using the DNA Damage ELISA Kit (StessMarq Biosciences Inc., Victoria, BC, Canada) according to the manufacturer’s protocol. Urinary 15-isoprostane F2t levels were determined with the Urinary Isoprostane ELISA Kit (EA85, Oxford Biomedical Research, Rochester Hills, MI, USA) following the manufacturer’s protocol. 15-isoprostane F2t was related to urinary creatinine. ## 2.8. Western Blot For protein isolation, frozen kidney tissue was manually pestled and lysed in RIPA buffer (50 mM Tris, 150 mM NaCl, 1 mM EDTA, $0.025\%$ Natriumdesoxycholat, $1\%$ Nonidet, 1 mM NaF) supplemented with a protease inhibitor cocktail (Sigma, Taufkirchen, Germany) and a phosphatase inhibitor cocktail (Thermo Scientific, Rockford, IL, USA). After a centrifugation step at 10,000× g for 15 min at 4 °C, protein extracts were stored at −80 °C. Subsequently, 50 µg protein was loaded onto an SDS gel and transferred to a nitrocellulose membrane (GE Healthcare, Little Chalfont, UK) after separation. Membranes were incubated with specific primary antibodies against Bach1 (abx322188, Abbexa, Cambridge, UK), FYN (ab184276, abcam, Cambridge, UK), γGCLC (PA1492, BosterBio, Pleasanton, CA, USA), GSK3β (#9315, Cell Signaling, Herts, UK), pGSK3β (#9336, Cell Signaling, Herts, UK), HO1 (ab13243, abcam, Cambridge, UK), Keap1 (ab227828, abcam, Cambridge, UK), MafK (GTX129240, GeneTex, Irvine, CA, USA), NQO1 (ab34173, abcam, Cambridge, UK), Nrf2 (sc-722, Santa Cruz Biotechnology, Dallas, TX, USA), pNrf2 (ab76026, abcam, Cambridge, UK), SOD1 (GTX100554, GeneTex, Irvine, CA, USA), TrxR1 (GTX108727, GeneTex, Irvine, CA, USA) and, as housekeepers, α-tubulin (sc-5286, Santa Cruz Biotechnology, Dallas, TX, USA), GAPDH (#2118, Cell Signaling, Herts, UK) and lamin B2 (#2328, Cell Signaling, Herts, UK) overnight at 4 °C, followed by an incubation with HRP-conjugated secondary antibodies for 2.5 h at room temperature. Antibody binding was visualized using the BM Chemiluminescence Blotting Substrate Kit (Roche, Basel, Switzerland) according to the manufacturer’s instructions. Chemiluminescence signals were recorded using the ChemiDoc™ Touch Imaging System (BIO-RAD, Hercules, CA, USA). ## 2.9. Preparation of Cytosolic and Nuclear Protein Fractions To detect translocation of specific proteins into the nucleus, a cytosolic and a nuclear fraction were prepared. To the cytosolic (10 mM Tris-HCl, 50 mM NaCL, 500 mM sucrose, 0.1 mM EDTA, and $0.5\%$ Triton-X) and nuclear lysis buffers (10 mM Tris-HCl, 500 mM NaCL, 0. 2 mM EDTA, $1\%$ Nonident P40, and $1\%$ Tergitol) were added 1 mM DTT, 1 mM PMSF, 1 mM sodium orthovanadate and 1× protease inhibitor cocktail (Sigma, Taufkirchen, Germany) just before the start of the experiment. All work was performed at 0–4 °C. For extraction, frozen kidney tissue was first minced and homogenized with a pestle in cytosolic lysis buffer for 2 min and then incubated for an additional 5 min. The homogenate was centrifuged at 1000× g for 10 min to separate the nuclear proteins (pellet) from the cytosolic proteins (supernatant). After a wash step, the supernatant representing the cytosolic fraction was removed and stored on ice. After another centrifugation (1000× g, 4 min), the remaining pellet was resuspended in 300 µL of cytosolic lysis buffer. After washing, the pellet was resuspended in 150 µL of nuclear lysis buffer and sonicated. After centrifugation at 14,000× g for 10 min, the supernatant representing the nuclear fraction was removed. Fifty micrograms of the protein lysate of the cytosolic fraction and 10 µg of the protein lysate of the nuclear fraction were used for a Western blot. The cytosolic and nuclear fractions were stored at −80 °C until further use. ## 2.10. Quantitative RT-PCR mRNA was extracted from 20–40 mg frozen kidney tissue using the QIAcube (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Isolated mRNA was transcribed into cDNA using the High Capacity cDNA Reverse Transcription Kit (Thermo Fisher, Waltham, MA, USA). Quantitative Real-Time PCR (qRT-PCR) was performed with 20 µg cDNA and the SensiMix SYBR Hi-ROX Mastermix (Bioline GmbH, Luckenwalde, Germany) using the CFX96 Real-Time System (BIO-RAD, Hercules, CA, USA). Primer sequences utilized for gene expression analysis are listed in Table S2. Relative expression levels of target genes were normalized to the housekeepers GAPDH and β-actin and calculated using the comparative CT method with the analysis software Bio-Rad CFX Manager 3.1 (BIO-RAD, Hercules, CA, USA). ## 2.11. Statistics Data from seven to eight animals per group are expressed as mean ± standard error of the mean (SEM). GraphPad Prism 6 (GraphPad Software, La Jolla, CA, USA) was used for statistical analyses. Data were tested for normal distribution using the Kolmogorov–Smirnov test with Dallal–Wilkinson–Liliefor p values. Groups of each mouse strain were tested for significance (normal distribution) among themselves or against control using analysis of variance (one-way ANOVA) followed by Tukey’s correction. Values not normally distributed were tested for significance using the Kruskal–Wallis test followed by Dunn’s multiple comparison test. Significant differences between mouse strains (controls or Aldo-infused animals) were tested for significance using 2-way ANOVA followed by Tukey’s correction. A p value ≤ 0.05 was considered significant. ## 3.1. Characteristics of Keap1 Hypomorphic (Nrf2ꜛ) Mice Hypomorphic Keap1 (Nrf2ꜛ) mice showed significantly decreased expression of Keap1 in the kidney at both the mRNA and protein levels (Figure 1a,b). This did not affect Nrf2 gene expression (Figure 1c), which was not expected because Keap1 regulates Nrf2 primarily posttranslationally. However, an increased amount of Nrf2 protein could be measured in the mice (Figure 1d). ## 3.2. Blood Pressure Changes and Clinical Characteristics Only the WT mice infused with 125 µg/kg Aldo per day showed a temporary significant increase in blood pressure up to 155 mmHg until the 15th day after implantation (Figure 2a). After this time point, however, the blood pressure decreased to the level of the WT control (C) group by the end of the experiment. In the Aldo-infused WT mice treated additionally with Sulf in the drinking water, blood pressure did not increase above the level of the WT control. Quantification of the aldosterone levels in the serum of the six mouse groups showed a significant increase in all Aldo-treated groups (Figure 2b). A difference in body weight could not be detected between the six different groups (Table 1). However, compared to the WT mice, the Nrf2ꜛ mice had a higher kidney weight already basally. Aldo infusion in the WT mice and in the Nrf2ꜛ mice resulted in a significant increase in relative kidney weight. Aldo treatment in the Nrf2ꜛ mice resulted in an even greater increase in kidney weight, which was even significantly higher than in the Aldo-infused WT mice. Additional treatment of the Aldo-exposed WT mice with Sulf also resulted in an increase in kidney weight compared with the WT control and Aldo-only treatment. The relative heart weights of WT control and Nrf2ꜛ control mice did not differ basally. Sulf had no impact on relative heart weight, but Aldo infusion resulted in a significant increase in relative heart weight compared with WT control in both WT and Nrf2ꜛ mice. Collagen deposition was significantly increased in Aldo-treated Nrf2 animals, while Aldo-treated WT animals showed no change at all. ## 3.3. Histopathological Changes in the Kidney and Renal Function During organ removal, hydronephrosis of the right kidney was observed in two of eight mice in the Nrf2ꜛ control group and in four of eight mice in the Nrf2ꜛ-Aldo-infused group (these kidneys were not used for further analyses). Significant pathologic damage to the tubular system was recorded in Aldo-treated WT animals, and included infiltration of inflammatory cells, atrophy of basement membranes and fibrotic changes (Table 1, TSI). Nrf2ꜛ mice were not affected, neither basally nor by Aldo. Aldo-treated WT mice also showed a significant increase in mesangiolysis (Table 1, MSI), whereas all mice, including those treated only with Sulf and the Nrf2ꜛ control, presented increased glomerular sclerosis (Table 1, GSI). Aldo treatment resulted in a significant increase in drinking and urine volume (Table 1) in all groups compared with the matched controls and the control of the respective other mouse strain (WT-C vs. Nrf2ꜛ-Aldo and Nrf2ꜛ-C vs. WT-Aldo). Creatinine in serum was not different between the groups. Aldo alone did not affect creatinine clearance. However, treatment with Sulf resulted in significantly lower creatinine clearance compared with WT control and combination treatment (WT-Aldo + Sulf) and the Nrf2ꜛ mice showed a slightly lower creatinine clearance basally. Albumin was used as a marker for glomerular damage in this experiment, while urinary NGAL and KIM-1 served as markers of tubular damage. All Aldo-treated animals had significantly elevated albumin levels, with the Nrf2ꜛ-Aldo group presenting the highest levels. Aldo infusion resulted in a tenfold increase in KIM-1 to creatinine ratio in WT mice only, whereas Nrf2ꜛ mice treated with Aldo had significantly lower levels. All Aldo-treated animals had significantly elevated NGAL levels, with the Nrf2ꜛ-Aldo group here presenting the lowest levels. In the co-treated mice, Sulf treatment had no effect at all on the three markers. ## 3.4. Oxidative Stress Markers Systemic oxidative stress was determined by the excretion of 15-isoprostane F2t, a marker of lipid peroxidation, and 8-OHdG, a marker of oxidative damage to nucleic bases, in the collection urine of mice. 15-isoprostane F2t was significantly increased in all Aldo-treated animals. Higher 8-OHdG excretion was detected in WT and Nrf2ꜛ mice compared with the WT-Sulf group and the Nrf2ꜛ-C group, respectively (Figure 3a,b). As another reasonably stable marker for oxidative stress, the DNA damage marker γH2AX was quantified on paraffin sections of the kidneys. Here, a localization of damage in renal cortex and medulla is feasible. Compared with the WT-C group, there were approximately twice as many γH2AX-positive nuclei in the renal cortex of Sulf-treated WT mice and in the Nrf2ꜛ-C group (Figure 3c,d). Aldo resulted in a significant increase of 4- and 6-fold in γH2AX-positive nuclei in all three Aldo-treated groups compared with the respective control groups. In the medulla, there was no elevated damage after Aldo treatment. As a possible source of oxidative stress, we examined the change in expression of NADPH oxidase isoform 2, which was significantly upregulated in the two Aldo-treated WT groups (Figure 3e). ## 3.5. Nrf2 Expression, Activation and Target Gene Regulation No significant difference in Nrf2 abundance in nuclear and cytosolic kidney samples was observed between groups, except for the Aldo-treated Nrf2ꜛ group (Figure 4a,b). Here, the amount of cytosolic Nrf2 was significantly increased compared with the WT control and the WT-Aldo group. To obtain better local resolution of Nrf2 levels, renal tissue was immunohistochemically stained with an antibody against Nrf2 (Figure 4c–e). A 4- to 8-fold higher expression of Nrf2 was observed in the medulla compared with the cortex, but significantly altered abundance of Nrf2 was observed only in the cortex, increased in all Aldo-treated groups. When the phosphorylated form of Nrf2, pNrf2, which is ostensibly regarded as the active form of the transcription factor, is considered, we found its significant increase only in the Aldo-treated Nrf2ꜛ group in the cytosolic fraction (Figure 5a,b). In contrast, in the nuclear and the cytosolic extracts of the Aldo-treated WT mice, interindividual variations prevented the finding of significant changes of pNrf2. Here, immunohistochemical staining revealed a significantly increased amount of pNrf2 only in the cortex of WT-Aldo mice (Figure 5c,d). An even more detailed localization of pNrf2 according to kidney structure or cell type (Figure 5e–i) showed a significant increase in the distal tubule, early and later collecting duct of all Aldo-treated groups and also in the later collecting duct in the Nrf2ꜛ-C group. Considering the gene and protein expressions of the target genes of Nrf2, it is striking that only the target exclusively regulated by Nrf2, NADPH quinone dehydrogenase 1 (NQO1), was significantly downregulated at both gene and protein levels in the Aldo-treated WT groups and upregulated in both Nrf2ꜛ groups (Figure 6a). Furthermore, thioredoxin reductase 1 (Trxr1) and superoxide dismutase 1 (Sod1) showed significant downregulation at the gene level in the Aldo-treated WT group and heme oxygenase 1 (HO1) at the protein level in all Aldo-treated groups (Figure 6b–d). The catalytic subunit of glutathione cysteine ligase (γGCLC) showed no difference between groups (Figure 6e). ## 3.6. Keap1-Independent Regulation of Nrf2 Because downregulation of Nrf2 targets was found despite increased Nrf2 and pNrf2 levels in Aldo-treated WT animals, known negative regulators of Nrf2 activity were examined. The heterodimerization partner of Nrf2, MafK, was not changed in nuclear extracts of Aldo-treated WT animals (Figure 7a). The transcriptional regulator Bach1 is a competitor to Nrf2 in dimerization with MafK, but was also not changed in any group (Figure 7b). The kinase GSK3β, which targets Nrf2 to proteasomal degradation via phosphorylation at Ser 335 and 338, was changed neither in the cytosol (Figure 7c) nor in the nucleus (Figure 7d). In contrast, its inactive form phosphorylated at serine 9 was significantly increased in the cells of both Aldo-treated WT groups (Figure 7e). The Src family tyrosine kinase FYN, which can also mark Nrf2 for proteasomal degradation via phosphorylation, showed a significant reduction in nuclear extracts in both groups of Nrf2 mice compared with WT animals (Figure 7f). ## 4. Discussion Nrf2 activation was only minimally able to protect kidneys of mice from damage induced by Aldo. Neither pharmacological activation of Nrf2, which proved highly ineffective in this model, nor genetic activation of Nrf2 prevented systemic or local oxidative stress and also had little effect on renal damage markers. In WT animals treated with Aldo, there was broad downregulation of the Nrf2 system. To examine the effects of Nrf2 activation on moderate renal injury, we chose an Aldo concentration that did not persistently increase blood pressure significantly above normal but produced measurable histological and physiological changes in the kidney [15]. These changes to the kidney were different in the treatment groups and showed higher damage to the glomeruli and moderate protection of the tubules in the Aldo-treated Nrf2ꜛ animals, whereas the Nrf2 activator Sulf appeared to provide mild protection of the glomeruli in the WT mice, but none to their tubuli. Overall, Nrf2ꜛ animals had decreased creatinine clearance, increased albumin excretion and a tendency to hydronephrosis from the outset. Risk factors for the development of hydronephrosis include ureteral stenosis and heart failure and also increased water and sodium intake [31], which was the case for our animals. Hydronephrosis has not been previously reported in the Nrf2ꜛ mouse, but only in a mouse in which Keap1 was specifically deleted in the renal epithelium [32]. Here, all animals studied already suffered from the renal alteration without treatment. In our study, hydronephrosis was more pronounced in the Aldo-treated group, which also had an increased drinking volume. The Nrf2ꜛ mouse seems to be predisposed here, as this phenomenon was not found in the Aldo-treated WT mice despite similar drinking volume, and because a quarter of the Nrf2ꜛ control mice already showed hydronephrosis, possibly caused by the NaCl administered to all mice in the drinking water. All of this suggests a negative effect of Keap1 knockdown on renal development and/or function, which has not been reported previously [27,33,34,35]. Furthermore, we saw increased collagen deposition in the hearts of animals with Nrf2 activation, which may be a contributing factor to the cardiac side effects of the Nrf2 activator CDDO-Me. It is known that complete deletion of Keap1 in Nrf2ꜛ mice leads to death from malnutrition due to hyperkeratosis of the esophagus shortly after birth [36]. Until our report, only studies showing positive effects of Keap1 knockdown in renal damage models, such as protection from tubular damage in reperfusion or ureteral obstruction, had been published [35,37]. Our animals also showed some protection of tubuli, however, only by the genetic modification and not by the activator. This contradicts observations from studies performed with another Nrf2 activator, CDDO-Me, which was able to protect tubules at least during shorter treatment periods [38,39]. The increased Aldo-induced albumin excretion reveals the sensitivity of Nrf2ꜛ animals to glomerular damage, which has already been reported from three other renal injury models and in which increased fibrosis in the glomerulus was observed, as in the animals in this work [34]. There, loss of podocyte processes was additionally seen. Sulf somewhat reduced albuminuria in the WT animals, as previously shown for Sulf and also curcumin in other models [5,11]. As expected for the Nrf2ꜛ mouse strain used, the expression of Keap1 was already decreased by more than $50\%$ by insertion of the loxP sites in the absence of activating Cre expression [33,37]. That the decreased amount of Keap1 basally has no direct effect on Nrf2 expression has also been observed previously by another group, but does not indicate anything about the posttranslational regulation of Nrf2 [37]. Looking closer at the localization of Nrf2, in these groups of animals, as in the preparatory experiment [15], a significantly increased Nrf2 amount was shown in all Aldo-treated groups only in the cortex, not in the medulla of the kidney. Too large interindividual differences among the animals prevented an evaluation of the presence of Nrf2 in the nucleus. Therefore, the presence of phosphorylated and thus usually labeled active pNrf2 was additionally quantified in nuclear and cytosolic extracts but also in the different cells of the kidney. Indeed, phosphorylation is not essential for translocation to the nucleus [40]. Increased phosphorylation of Nrf2 was confirmed in the cortex of Aldo-treated WT animals and, at greater resolution, increased phosphorylation in the nuclei of the distal tubules and collecting duct in all Aldo-treated groups and, at the latter site, also in the Nrf2ꜛ control group. Sulf alone failed to alter phosphorylation status. Differential distribution of Nrf2 but also of other components of the antioxidant defense system within the different nephron segments was reported by several groups, with frequently better equipment found in the proximal tubule [41,42]. Again, as previously reported, Aldo treatment in mice did not induce Nrf2 target genes as we previously observed in rats [11,15], consistent with reports of downregulated Nrf2 targets from the human situation in CKD and other animal CKD models [5,16]. Unexpectedly, based on our previously conducted rat study with Sulf as the Nrf2 activator [11], Sulf had no effect at all on Nrf2 target genes, either alone or in combination with Aldo. It is unlikely that the reason for this was the chosen oral route of administration, which represented less suffering for the animals. Oral application was supposed to compensate for the short half-life of Sulf of about 2 h, which was also the time point where the highest concentration of Sulf was detected in kidneys of mice after a single oral administration [43,44]. This kind of application had worked very well in our rats, in which it was shown that $80\%$ of orally administered Sulf was bioavailable [11,45]. The choice of the Sulf dose used was in the range of published doses which varied from 0.5 to 12.5 mg/kg × day, with one report in which a higher dose of 10 mg/kg × day produced no effects compared with the lower dose of 2 mg/kg × day [46]. As some effects were noted in the animals, it cannot be assumed that the Sulf was not incorporated at all, but possibly the dose was not appropriate for this model. The assumption that Sulf may be less effective in the kidney than CDDO-Me was not expected based on its use and effects in animal models of renal injury [11,47]. That we do not find a clear protective effect against Aldo-induced damage, as seen in other models of renal injury, is partly because of the adverse effects of Aldo on Nrf2 signaling discussed below. On the other hand, the type of renal injury also plays a crucial role. Protective effects of Nrf2 activators have been seen mainly in acute injury and in models that primarily induce tubular injury [35,37,39]. When glomerular damage is involved, reports that find no protection by Nrf2 activation accumulate, as we observe in our animals [34,46]. Thus, it may be that only patients with specific renal diseases that do not involve marked proteinuria benefit from Nrf2 activation. In contrast to the situation in the WT mice, in the Nrf2ꜛ mice, there was a clear induction of the most specific Nrf2 target, NQO1. Other targets such as HO1 regulated by other transcription factors besides Nrf2 [48] were decreased or only tended to be increased, such as TrxR1 or γGCLC. In most cases, Aldo enhanced the response already seen basally, with the exception of SOD1. Other studies using Nrf2ꜛ mice all showed induction of NQO1 at the protein or mRNA level, suggesting sustained activation of Nrf2 signaling in the kidney [33,34,35,37]. Additionally, microarray analysis showed that gene expression of most Nrf2 target genes was already basally increased in Nrf2ꜛ mice, which included the targets NQO1, TrxR1, γGCLC, and HO1, also studied in this work [35]. Thus, although increased levels of Nrf2 and also of pNrf2 were found in the kidney, no induction of Nrf2 targets was detected in the WT mice. This suggests that either dimerization partners were lacking or not present in the appropriate proportion for the unhindered activity of Nrf2 or pNrf2 in the nucleus, or that the function of Nrf2 was negatively regulated. For example, the formation of a heterodimer with sMaf is essential for the binding of Nrf2 to the ARE [24]. However, sMaf proteins can also form homo- or heterodimers with each other or with other proteins, such as Bach1, which inhibits transcription of ARE-regulated genes [49]. Nguyen et al. demonstrated that overexpression of MafK represses the expression of ARE-dependent catalase in a dose-dependent manner [50]. The MafK level was indeed slightly higher in the nuclear fraction of Aldo-treated WT groups, which may have resulted in an inhibition of expression, whereas no clear picture emerged for Bach1. The potential negative regulator GSK3β was slightly increased in the nucleus, where its phosphorylation of Nrf2 would lead to its nuclear export with subsequent degradation, whereas no change in GSK3β was detected in the cytosol. In the Aldo-targeted WT groups, even the inactive phosphorylated form of GSK3β, pGSK3β, was significantly increased and therefore cannot be responsible for the decreased expression of Nrf2 targets. Higher expression of GSK3β was observed in a folic acid-induced CKD model in mice after 4 weeks and resulted in increased oxidative stress but lower Nrf2 accumulation in the nucleus and decreased HO1 expression [51]. This study further demonstrated that after initial damage to the kidney, the Nrf2 pathway was activated, but this activation progressively decreased over the next 4 weeks, coupled with an increase in GSK3β protein levels. Furthermore, these effects were also found in renal biopsies from CKD patients [51]. The important role of GSK3β is underlined by a study in which inhibition of GSK3β was able to protect against Aldo-induced inflammation and fibrosis in the kidney and heart of mice [52]. Sulf-only-treated animals showed no significant change in GSK3β or pGSK3β, although Sulf theoretically not only has an effect on the Keap1–Nrf2 interaction but can also affect Nrf2 regulation via deactivation of the GSK3β kinase. Thus, inactivation of GSK3β via its phosphorylation was observed in the heart of Sulf-treated mice [53] and a negative effect of Sulf on GSK3β activity was also shown in diabetic rats [54]. A clearer picture could be obtained if regulators were also examined at the cell type level rather than in total kidney extracts. The last negative regulator we examined in this work was the kinase FYN, which can eject Nrf2 from the nucleus by phosphorylation and is itself regulated by GSK3β [55]. Here, despite the analysis of whole kidney extracts, there was a significant difference between the WT and Nrf2ꜛ animals in the expression of regulators of Nrf2, because the FYN level was slightly higher in the WT animals, whereas it was already present at a significantly lower level basally in the Nrf2ꜛ-C group and was also significantly decreased compared to the Aldo-WT animals after Aldo treatment. This could explain the difference in Nrf2 target expression between the two mouse strains. A limitation of our study is that specific Nrf2 activators were not yet available at the time of the experiment. It would be very interesting in the future to investigate the responses of the kidney to the specific protein interaction inhibitors. Furthermore, the very high interindividual variations of the groups were a hindrance to obtaining significant results from more of the parameters studied. The unexpected lack of protection of the kidneys of Aldo-treated animals against damage by Nrf2 activation could be explained with the help of recent publications, the findings of which are supported by our study. ## 5. Conclusions In conclusion, neither the Nrf2 activator Sulf as used in the model presented here nor genetic upregulation of Nrf2 activity provided protection against Aldo-induced moderate renal injury. In contrast, negative effects of constitutive genetic Nrf2 upregulation were observed with respect to the glomeruli and the filtration barrier, which was also the case, only to a lesser extent, in the Sulf-treated mice. In addition, the hearts of the Sulf-treated animals showed an increase in collagen deposition, as did the animals with genetically activated Nrf2. Treatment with Aldo resulted in extensive inhibition of Nrf2-regulated antioxidant defenses in the kidney. 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--- title: PCL/Graphene Scaffolds for the Osteogenesis Process authors: - Silvia Anitasari - Ching-Zong Wu - Yung-Kang Shen journal: Bioengineering year: 2023 pmcid: PMC10044836 doi: 10.3390/bioengineering10030305 license: CC BY 4.0 --- # PCL/Graphene Scaffolds for the Osteogenesis Process ## Abstract This study aims to characterize the osteoconductivity, optimal bioresorbable, biodegradability, biocompatibility, and mechanical properties of Poly-ε-caprolactone (PCL)/graphene (G) scaffolds at concentrations of 0.5, 1, 1.5, 2, 2.5, and 3 wt%, which are used to support bone regeneration through solvent casting and particulate leaching. The water contact angle measurement revealed a transition from a hydrophobic to a hydrophilic surface after incorporating various G concentrations. The scaffolds with 0.5 wt% G had smaller pores compared to those produced using 3 wt% G. Furthermore, numerous pores were connected, particularly those with larger diameters in the 2 and 3 wt% G samples. The proportion of water absorption varied between $50\%$ and $350\%$ for 4 months, with large percentages of scaffolds containing high G concentrations. Raman spectroscopy and X-ray diffraction, which were used to confirm the presence of nanofiller by increasing the ratios of ID/IG, I2D/IG, and band 2θ = 26.48°. The mechanical properties were improved by the addition of G, with a Young’s modulus of 3 wt% G, four times that of PCL. Measuring cell biocompatibility, adhesion, proliferation, and differentiation with osteoblast-like (MG-63) cells revealed that PCL/G scaffolds with higher concentrations were more biocompatible than PCL as well as those with lower concentrations. ## 1. Introduction At the beginning of this decade, natural progenitor cells or autologous cells were considered the best option for regenerating damaged or missing tissue [1]. However, using autologous cells for regenerative purposes can be challenging due to limited tissue volumes, contamination, immune reactions, and difficulty controlling growth and regeneration in 2D cells. To achieve functional integrity, a 3D framework is necessary for complex biological systems. This has led to the integration of cell biology and materials sciences to create degradable biomaterials such as 3D scaffolds made from natural or synthetic polymers which can enhance cell adhesion and proliferation [2]. Several methods and technologies have been developed to produce 3D scaffolds, such as phase separation, self-assembly, electrospinning, emulsion freeze-drying, gas foaming, free radical polymerization, and 3D printing. They allow adherent cells and bioactive molecules to interact with surrounding tissues through the porous structure of the product [3]. For example, synthesized polymeric composite material was fabricated from arabinoxylan (ARX), β-glucan (BG), nano-hydroxyapatite (nHAp), graphene oxide (GO), and acrylic acid (AAc) through free radical polymerization and porous scaffold using the freeze-drying technique. The result found that BGH3 has desirable morphological, structural (with optimum swelling), biodegradation, and mechanical behaviors [4]. Polymeric nanocomposite material was developed using cellulose and a co-dispersed nanosystem (Fe3O4/GO) by free radical polymerization to fabricate porous polymeric scaffolds via freeze-drying. Antibacterial activities of porous scaffolds were studied against severe Gram-positive and Gram-negative pathogens and increased Fe3O4 amount in nanosystems with increased antibacterial activities [5]. The synthesis of nanocomposites based on acrylic acid (AAc)/guar gum (GG), nano-hydroxyapatite (HAp NPs), titanium nanoparticles (TiO2 NPs), and optimum graphene oxide (GO) amounts via the free radical polymerization method was reported. Increasing the amount of TiO2 in combination with optimized GO has improved the physicochemical and microstructural properties, mechanical properties and Young’s modulus, porous properties, and porosity [6]. The combined advantages of PCL and Zn were fused by fabricating PCL/Zn composite scaffolds with different Zn powder contents (1 wt%, 2 wt%, 3 wt%) through deposition modeling. Finally, Zn2+ revealed that regulated osteogenesis and osteoclastogenesis by activation of the Wnt/β-catenin and NF-κB signaling pathways, respectively [7]. The polymeric nanocomposite was prepared by free-radical polymerization from sodium alginate, hydroxyapatite, and silica with different GO amounts. The increased GO amount provides different multifunctional materials with different characteristics [8]. This study used a solvent-casting and particulate-leaching method to construct 3D scaffolds which were economical but still showed promising potential to produce porous bone-growth-promoting materials [9,10]. The scaffolds are designed to be biocompatible, biodegradable, and have properties that encourage cell attachment, proliferation, and integration into host tissues for regeneration. These scaffolds also mimic the extracellular matrix (ECM) in a defect area [11,12]. The use of synthetic poly (ε-caprolactone) (PCL), an aliphatic polyester that is biocompatible and biodegradable, has received a lot of attention in bone tissue engineering [2]. However, the lack of mechanical properties of polycaprolactone (PCL) scaffolds restricts their applicability because human cortical and cancellous bones need a higher Young’s modulus. It is, therefore, necessary to combine it with another material, such as graphene (G). Graphene, a two-dimensional (2D) carbon nanofiller with sp2-bonded atoms, can be used to improve polymeric materials’ solubility, processing ability, and conductivity. It has a high specific surface area, a poly-aromatic structure, functionalization, and excellent protein adhesion properties [13,14]. Several studies revealed that its concentration affects chemical functionalization through increased hydrophilicity. It also modified the extracellular environment, enhanced osteoblast adhesion and proliferation, and also facilitated differentiation [15]. The combination of PCL and G has been studied as a potential solution to improve the mechanical properties of PCL scaffolds used in bone tissue engineering. Graphene is known to have high mechanical strength and stiffness, which can enhance the Young’s modulus of PCL composites, making them more suitable for use in bones. Therefore, further research is needed to determine the optimal concentration and method of incorporating graphene into PCL to achieve the best mechanical properties [13]. Furthermore, there are concerns about the product’s medical toxicity because it remains in the human body for an extended period as an implantable material. Malhotra et al. [ 16] have shown that G promoted attachment and proliferation of human neurons, cardiomyocytes, and several types of stem cells without any harmful effects on cell and mitochondrial membranes. Another study by Chang et al. [ 17] also showed that G promoted bone formation without causing any bone destruction. Osteoblast-like (MG-63) cells play a crucial role in bone remodeling and bone formation by secreting various proteins such as ECM proteins, cytokines, collagen, and growth factors [18,19]. These cells differentiate into osteocytes for complete bone synthesis and integrate into the bone matrix. The surface properties and toxicity of scaffolds are crucial in promoting osteoblast proliferation at the fracture site, and limited research has been done in this area, especially in relation to waste G and its influence on osteoblast growth [20]. This study focuses on analyzing the impact of different weight percentages of G (0.5, 1, 1.5, 2, 2.5, and 3 wt% G) on the physicochemistry, morphology, mechanics, biodegradation, and biocompatibility of PCL scaffolds. The goal is to identify the scaffolds with the best combination of osteoconductivity, biodegradability, biocompatibility, and physicochemical and mechanical properties to support bone regeneration. ## 2.1. Fabrication of the Scaffolds A solvent casting and particle leaching method was used to fabricate PCL and PCL/G scaffolds [10]. PCL (Sigma-Aldrich, Merck, Darmstadt, Germany) was dissolved in chloroform (Honeywell, Charlotte, USA) at room temperature for 12 h. This combination was then mixed with various concentrations of G and NaCl for 2 h. G was previously produced by transferring a graphite intercalation compound into a preheated crucible at 700 °C in a common furnace positioned in the front of a fume cupboard to prevent inhalation of the nanoparticles, and it was left there for 60 s. These layers expanded upon ultrasonication and caused the G to disperse in the solvent. After fabrication, the blend was placed into a cast and cured overnight at room temperature. Chloroform was then evaporated for 24 h at 37 °C in a drying vacuum oven (Deng Yng, Taipei, Taiwan). Deionized (DI) water and a water bath (BH-130D, Taipei, Taiwan) were used to remove porogen from the scaffold. In addition, the DI water was changed every 2 h and then dried in the oven at 50 °C for 12 h. Scaffold fabrication is illustrated in Figure 1. ## 2.2.1. Water Contact Angle (WCA) The surface property of the PCL/G scaffolds was characterized with a WCA measuring system, which was developed in our laboratory using a sessile drop method. The samples were cut to 10 × 10 mm2, and 0.2 μL of a DI water droplets was dropped onto the surface of the scaffold via a motorized syringe at a rate of 1 μL/s. An image was taken at 1 sec, and at least five locations of each PCL/G scaffold were tested, followed by the determination of the average value [2,10]. ## 2.2.2. Water Absorption Rate Water absorption by the scaffold was evaluated using 1× phosphate-buffered saline (PBS; Gibco-Invitrogen, USA). The samples were immersed in 1× PBS, and their weights were evaluated. Water absorption was calculated using the following equation, where W1 represents the wet weight and W2 is the dried weight [21]:[1]Absorption rate %=W1−W2W2×$100\%$ ## 2.2.3. Porosity The porosity of the scaffolds was evaluated by measuring the displacement of ethyl alcohol (EtOH). The initial volume of EtOH was V1. The total volume of EtOH (Nihon-Shiyaku, Japan) after the scaffold was immersed was V2. The residual EtOH volume after the scaffold was removed was V3. The porosity was then calculated using the following equation [13]:[2]Porosity %=V1−V3V2−V3×$100\%$ ## 2.2.4. Pore Sizes The scaffold morphology and pore sizes were evaluated using scanning electron microscopy (SEM; Hitachi, Japan) at an accelerating voltage of 15 kV. In SEM images, the pores were evaluated using Image-J software. Scale bars that described a known distance were set within the SEM image to measure pore sizes. A pore’s contour was then delineated and calculated (μm). Different cross-sections were passed from the scaffolds [13]. ## 2.2.5. Tensile Test The tensile strength of the PCL/G scaffolds was determined using a universal testing machine (Shidmazu, Japan) equipped with a 250-N load cell. Experiments were performed at room temperature and a crosshead speed of 3 mm/min. The samples were prepared by cutting a scaffold with a dimension of 40 × 20 × 10 mm3. The stress vs. strain graphs for each was used to calculate the Young’s modulus, ultimate tensile strength, and elongation-at-break using the linear region (elastic region) of the graphs. The ultimate tensile strength (σmax) was calculated using the following equation [22]:[3]σmax=P/a where P represents the tensile force and a is the cross-sectional area. Young’s modulus (E) was determined using the equation [22]:[4]E=σ/ε where σ represents stress and ε represents strain. Elongation-at-break (𝜀b) was calculated using the equation [22]:[5]εb (%)=∆L/L×$100\%$ where ∆L represents elongation at rupture and L represents initial gauge length. ## 2.2.6. Raman Spectroscopy PCL/G scaffolds were analyzed using Raman spectroscopy (UniDRON, CL Tech, Taiwan). The samples were folded and mounted on glass slides for measurement with a laser at 457 nm, 50 mW, $1\%$ neutral density filter, 50× objective lens, 1 s exposure length, 60 s average time, and a signal normalization at a peak of 2918 cm−1 for processing. Origin Pro 2022 software was used to analyze the data, which ranged from 500 to 3300 cm−1 [23]. ## 2.2.7. X-ray Diffractometer (XRD) The XRD spectra for PCL/G scaffolds were produced on a high-power (18 kW) XRD (Rigaku, TTRAX3, Japan). The determinations were carried out using radiation of λ = 1.54 Å in a range of 2θ = 10~50° at a scan rate of 4°/min. They were then analyzed by fitting a Lorentzian curve for height (intensity) using Origin Pro 2022 software [23]. ## 2.3. Biodegradation Time Test Biodegradation of the PCL/G scaffolds with a dimension of 10 × 10 × 2 mm3 was determined by placing them in a tube containing 5 mL of 1× PBS (Gibco-Invitrogen). The samples were then sealed with parafilm and placed in a water bath at 37 °C for 4 months without refreshing the 1× PBS. Every month, the scaffolds were removed from the water bath, rinsed five times with DI water, and dried for at least 24 h in a vacuum dryer. Raman spectroscopy and XRD were used to examine the samples [10]. ## 2.4.1. Scaffold Preparation and Cell Seeding Scaffolds used for cell culture had a dimension of 10 × 10 × 2 mm3 and contained various G weight ratios. They were sterilized in a $95\%$ ethanol solution for 24 h, followed by washing in a 1× PBS solution three times to eliminate residual ethanol. Before cell seeding, scaffolds were incubated for 3 h in Dulbecco’s modified Eagle medium (DMEM; Gibco-Invitrogen). Osteoblast-like (MG-63) cells at passage 5 (kindly provided by 3D Global Biotech Inc, Taipei, Taiwan) were cultured in culture plates with DMEM containing $10\%$ fetal bovine serum (FBS) and $1\%$ penicillin in an incubator at 37 °C with $5\%$ CO2. The medium was replaced every 2–3 days, and they were digested and subcultured using $0.25\%$ of trypsin-EDTA (Gibco, USA) for detachment after $80\%$ confluence was achieved [13,21]. ## 2.4.2. MTT Assay (3-(4,5-Dimethylthiazol-2-yl)-2-5-diphenyltetrazolium bromide) MTT (a tetrazole) assay was used to examine the biocompatibility and proliferation of osteoblast-like (MG-63) cells [8]. ## Biocompatibility The surface area of each scaffold was measured with following formula [24]:[6]Total Surface Area=2πrh×2π where π is 3.14, r is the radius, and h is the height. Subsequently, DMEM supplemented with $10\%$ FBS and $1\%$ of penicillin/streptomycin was added with the formula:[7]Total medium mL=Total Surface Area/6 The scaffold and DMEM were placed in a 50 mL conical centrifuge tube and shaken in a shaking water bath at 37 °C and 100 rpm for 24 h. The extracts were filtered with a Millipore filter unit (Sartorius, France) with a pore size of 0.22 μm and a polyethersulfone (PES) membrane. Osteoblast-like (MG-63) cells were detached using $1\%$ trypsin-EDTA, and 100 μL of a cell suspension at a concentration of 105 cells/mL was seeded into a 96-well plate. Furthermore, the plates were placed in an incubator at 37 °C with $5\%$ CO2 for 24 h. The medium was then removed and replaced with extracted samples, which were incubated for another 24 h. An MTT-labeling agent reagent of 50 μL was added to each well and then placed in an incubator at 37 °C with $5\%$ CO2 for 3–4 h. The reagent was then removed and solubilization buffer was added to each well to dissolve the purple formazan crystals. Optical density was measured at 570 nm using an enzyme-linked immunosorbent assay (ELISA) reader. The optical density of cells was obtained to determine the cell biocompatibility using the following equation [13,24]:[8]Cell biocompatibility %=OD sampleOD control×$100\%$ ## Proliferation Cells were detached using $0.25\%$ trypsin-EDTA (Gibco-Invitrogen), and each sample was seeded with 0.5 mL at a concentration of 104 cells/mL in 24-well plates, which were placed in an incubator for 21 days. The medium was renewed every 2–3 days during this period. Furthermore, the cells were removed from the culture incubator to evaluate the results on days 1, 4, 7, 14, and 21. A total of 50 μL of MTT-labeling reagent was then added to each well. After 4 h of incubation at 37 °C, the reagent was removed, followed by the addition of a solubilization buffer. The absorbance at 570 nm was determined to establish cell proliferation [2,13]. ## 2.4.3. Alkaline Phosphatase (ALP) Assay (Differentiation Assay) A commercial ALP test kit was used to detect ALP activity (AnaSpec, Fremont, CA, USA). An ALP dilution buffer was prepared by diluting 10× to 1× assay buffer using DI water. The alkaline phosphatase standard of 10 μg/mL was then diluted to 0.2 μg/mL using the dilution buffer. The ALP standard solution was serially diluted by two-fold to yield concentrations of 0, 3.1, 6.2, 12.5, 25, 50, and 100 ng/mL. The wells were filled with 50 μL of solutions ranging 0–200 ng/mL. The samples were cultivated for 21 days, and they were removed from incubator to evaluate on days 1, 4, 7, and 21. Samples were washed twice with 1× assay buffer upon removal from the incubator. The extract buffer (200 μL; 10 mL 1× assay buffer plus 20 μL Triton X-100) was then added to each well for cell extraction. The samples were held at 4 °C for 10 min under agitation. Cell suspensions were then transferred to 1.5 mL tubes and centrifuged for 10 min at 4 °C and 2500× g. A total of 50 μL of supernatant was transferred to a 96-well plate for each sample. Subsequently, 50 μL of a pNPP substrate solution as well as ALP standard were added to each well, followed by incubation for 30 min at the desired temperature. The 96-well plate was shielded from light throughout this process, and the reaction was stopped by the addition of 50 μL of stop solution. The absorbance at 405 nm was then determined using an ELISA reader [12,21]. ## 2.4.4. Cell Morphology and Adhesion Cell adhesion at the surface of the scaffold was evaluated by scanning electron microscopy (SEM). The samples were washed with PBS after the medium was removed, followed by fixation with 0.6 mL of $2.5\%$ glutaraldehyde in a PBS solution for 30 min at 4 °C. After being washed twice with PBS, the scaffolds were dehydrated in ethanol of $30\%$, $50\%$, $70\%$, $90\%$, and $100\%$ and then dried in HMDS. Subsequently, they were gold-coated using a sputter coater and viewed with SEM at an accelerating voltage of 5 kV [13,21]. ## 2.5. Statistical Analysis All experimental data are presented as the mean ± standard error (SE) for each group of samples. All experiments had at least three scientific replicates. The data obtained were analyzed using SAS software. A one-way analysis of variance (ANOVA) and Tukey’s post hoc test were used to determine relevant differences in data. However, if the distribution was not normal and homogeneous, it was analyzed using the Kruskal–Wallis’s test and Mann–Whitney significant difference post hoc test to assess the differences between groups. Significance levels were set at * $p \leq 0.05$, $p \leq 0.01$, * $p \leq 0.001$, and ** $p \leq 0.0001$ [19,21]. ## 3. Results and Discussion Several studies revealed that the surface properties of a scaffold are some of the most important qualities which determine cell adherence. On hydrophobic surfaces, a dense layer of non-specific proteins can displace water from the surface and instantly aggregate on the materials. Meanwhile, a hydrophilic surface allows the attachment of chemicals that improve adhesion. These properties are influenced by low-stiffness and high-stiffness scaffolds [25]. WCA was examined on the solid surfaces of PCL and PCL/G scaffolds with various G concentrations to determine the effects of different concentrations on the wettability of the samples. When a liquid drop makes contact with a solid surface, it either retains its drop-like shape or spreads out on the solid surface, and this property is characterized by using water contact angle (WCA) measurements [26]. The liquid droplet tends to form an angle with the solid surface when it is placed in contact with it as shown in Figure 2a,b. The results showed that the WCA decreased as the proportion of G increased from 106.5° ± 2.1 in PCL to 71.9° ± 1.9 at 3 wt% G ($p \leq 0.0001$). This indicated that the hydrophobicity of PCL/G scaffolds was marginally reduced due to its addition. The reduced hydrophobicity is attributed to the wrinkled surface of graphene, which has a hydrophilic chemical composition [27]. The studies by Al-Azzam et al. [ 28] and Zhang et al. [ 29] reported that mostly mammalian cells adhere best to moderately hydrophilic surfaces with a WCA between 40 and 75°. An increase in hydrophilicity leads to an increase in protein adsorption and reduces scaffold toxicity, which plays a crucial role in cell attachment. The interaction between cells and components of the extracellular matrix (ECM) such as fibronectin, vitronectin, collagen, and laminin can impact cell attachment and migration, as shown in Figure 2b. This study revealed that the addition of 3 wt% G to scaffolds also continuously improved cell proliferation compared to PCL due to its hydrophilic surface. However, superhydrophilic (WCA < 5°) and superhydrophobic (WCA > 150°) surfaces can hinder cellular attachment and spread due to weak binding of cell-adhesion-mediating molecules. This weak binding causes cells to dissociate when multiple cells interact with the surface simultaneously, leading to limited or prevented cellular adherence and spread. [ 28,30]. Another physical characteristic that must be determined is the water absorption rate, which is essential for evaluating a composite material’s suitability for bone tissue regeneration. This is because it represents the effectiveness of body fluid absorption and nutrient transfer [18,31]. Figure 2c shows the water absorption rates of the PCL and the PCL with G (a hydrophilic material) over a 4-month period in PBS solution. The results showed that samples containing G had higher water absorption than PCL due to the hydrophilic properties of G. The percentage of water absorption varied from $50\%$ to $350\%$ during the 4 months, with the highest values observed in samples containing 2, 2.5, 1.5, and 3 wt% G in the first month, but only 2 and 3 wt% G maintained a high volume of PBS throughout the second month. By the third month, every scaffold’s capacity had been reduced, although the capacities of 1, 2, 2.5, and 3 wt% G increased yet again in the fourth month. The results suggest that the water absorption capacity can be improved by controlling the WCA, porosity, and pore size of the scaffold [31,32]. Apart from the WCA and water absorption rate ability, other physical properties support the production of a suitable scaffold. The porosity and pore size on the surface and the interior are required for cell distribution and placement. They are also needed for the exchange of nutrients, gases, and metabolic by-products between the exterior environment and the interior of the scaffold [29,31]. In this study, there was no statistically significant difference ($p \leq 0.05$) in the porosity of PCL compared to PCL/G at various concentrations. The values obtained ranged from 85.8 ± $1.85\%$ to 88.8 ± $1.4\%$, as shown in Figure 2d. This showed that the porosity of the scaffold was more comparable to that of trabecular bone (50–$90\%$) compared to cortical bone (5–$15\%$) [32]. Porosity needs to be increased in the scaffold’s surface and within its area, which can enhance the rate of water uptake. This condition can alter the level of fluid shear on cells, thereby causing adherence and proliferation on the scaffold. However, there is restriction of cellular movement as well as interchange of nutrients and metabolic waste if the pores are not interconnected. The solvent casting and particulate leaching were promising methods according to Lutzweiler et al. [ 33]. The size and interconnection of pores could be controlled based on the size of the salt as a porogen. Additionally, the high porosity of the scaffold (>$85\%$) could also control the interconnected pores [34]. The study showed that the 3 wt% G sample has a greater number of pores with diameters of <100 μm [616], >101 μm [548], and >501 μm [124] compared to the others, as shown in Figure 3a–g. The 0.5 wt% G had three times more macropores with a size of <100 μm compared to >101 μm, while PCL had 2.5 times more macropores of size <100 μm, as shown in Figure 3b,c. As osteoblasts ranged from 10 to 50 μm and fibroblasts ranged from 10 to 15 μm, the pore size of the scaffold must be <100 μm for fibroblast ingrowth, while >100 μm is suitable for osteoblast proliferation. This indicates that a PCL/G scaffold with a high concentration (2, 2.5, and 3 wt%) of G is appropriate for osteoblast ingrowth, as shown in Figure 3f–h. Several studies revealed that micropores of 10 μm were important for enhancing osteoinduction. This was because they were related to the formation of non-mineralized osteoid or fibrous tissues, which can increase the number of cytokines produced by fibroblasts. Furthermore, fibroblasts can increase osteoclast multiplication, inhibit osteoblast functions, and induce local inflammation [35,36]. Vascularization is another component that influences osteogenesis. Wang et al. [ 37] showed that the use of scaffolds with pore sizes of 525 μm increased osteogenesis and vascularization due to newly formed arteries providing appropriate oxygen and nutrients for osteoblastic activity within the larger pores of the scaffolds. This led to osteopontin (OPN) upregulation, chondrogenesis (collagen type I), and bone mass production. Additionally, graphene materials have excellent angiogenesis properties, which is important for osteogenesis [38] because poor vascularity can hinder the regeneration of complex tissues such as bone [37,39]. The mechanical properties of 3D scaffolds are an important design factor because of their impact on biostability. PCL has strong covalent bonds but weak van der Waals bonds, resulting in lower strength. However, incorporating graphene into PCL can increase strength due to the alignment of large molecules and decrease the influence of weak van der Waals bonding. This is why PCL/G composites with high graphene content have good strength and stiffness (Young’s modulus) despite having larger pore sizes than PCL, as shown in Figure 4a,b ($p \leq 0.001$) [40,41]. Furthermore, the mechanical properties of the scaffold, such as its ultimate tensile strength and Young’s modulus, play a role in regulating osteoblast behavior by affecting cell–ECM interactions. This interaction between the scaffold, ECM, and cells creates a complex microenvironment that influences cell behavior through mechanosensing. It enhances the ability of the cells to generate traction forces and enter the cell cycle, resulting in increased spreading and proliferation [40,42]. The addition of G to polymer materials increases the ultimate tensile strength of the material but reduces its ductility. This is shown in this study, where the addition of G to PCL in a sample with 3 wt% G resulted in an increase in ultimate tensile strength ($p \leq 0.001$) but a reduction in elongation-at-break (𝜀b) ($p \leq 0.0001$), which is related to the strain of the substrate as shown in Figure 4b,c. Moreover, the tensile strain of the substrate promoted osteoblast ECM formation by increasing integrin density on the surface of the ECM, such as integrin1 mediating osteoblast differentiation [43,44]. Raman spectroscopy and X-ray diffraction methods are relatively accurate at determining the chemical structure of various materials. Furthermore, Raman spectroscopy can also detect changes in vibrational spectral features which are induced by the production of defects, crystal disorder, edge structures, oxidation, or changes in the number of layers of the high activity. These changes can occur because of certain factors. On the spectrum, G displayed all four properties, namely D, G, D’, and 2D bands at 1320–1350 cm−1, 1580–1605 cm−1, 1602–1625 cm−1, and 2640–2680 cm−1, respectively. The presence of disorder in the aromatic structure or the edge effect of G due to oxidation is associated with the D peak, while the G peak was caused by the stretching of C-C bonds. The 2D peak is related to the thickness and can also be used to identify the number of layers as well as the quality of the aromatic rings [44,45]. The addition of G caused an increase the peaks of the D, G, and 2D bands, and this was clearly evident in the 2, 2.5, and 3 wt% G scaffolds, as shown in Figure 5a. The intensity ratio of the D to G bands, also known as ID/IG, is a measurement that can be used to determine the level of disorder or covalent bond. In this study, the ID/IG showed a slight increase as the concentration of G increased, as shown in Table 1. An increment in this ratio indicated the successful covalent bonding of G to oxygenous groups [46], which led to the introduction of a significant number of defects. A covalent bond happened between free radicals (salt) and C=C bonds of graphene. When salt was heated, a highly reactive free radical was produced, which attacked the graphene sp2 carbon atoms, forming a covalent bond, and the degree of a covalent functionalization reaction was shown in the ratio of ID/IG [47]. Furthermore, defects in the scaffold are responsible for an increased oxygen content, as shown in Figure 3a–h, which causes a reduction in its toxicity and increases cell adhesion [48,49]. The higher the number of oxygen-containing functional groups on the surface of a material, the better its hydrophilic qualities, and this has a significant effect in enhancing cell viability. The I2D/IG ratio of PCL/G showed a slight increase in the 2 wt% G to 3 wt% G, which indicated an increasing number of G layers [Table 1]. Previous studies revealed that the number of layers is an important parameter due to its ability to increase the surface area and the bending stiffness [49,50]. The results of XRD experiment are in line with that of the Raman spectroscopic analysis. Two major peaks were found at 2θ = 21.36° and 23.6° in the diffraction pattern of the semicrystalline PCL. Furthermore, the addition of G did not have a substantial impact on 2θ = 21.36°, but there was a slight decrease at 2θ = 23.6°, as shown in Figure 5f. The peak at 2θ = 26.48° improved as the concentration of G increased. Previous studies showed that increasing its concentration led to an increment in functionalized oxygen. It also enhanced the capacity of G to disperse in water or cell culture media, which can increase cell viability [51,52]. The biodegradation of scaffolds is an important factor to consider when analyzing their biological characteristics. This parameter was explored at a duration of 4 months by submerging the samples in 1× PBS at 37 °C. Biodegradation was then assessed using Raman spectroscopy and XRD to determine its progression. PCL is a polyester containing ester groups (C=O-O) and cyclic alkyl groups. The pre- and post-biodegradation PCL spectra had three significant absorption peaks, which are presented in Figure 5b–e. Absorption bands located around 2900 and 2800 cm−1 were attributed to asymmetric and symmetric C-H stretching, those located between 1730 and 1750 cm−1 were assigned to C=O stretching, and the band located at 1150 cm−1 was linked to the presence of C-O stretching. After biodegradation, the intensity of PCL in the spectrum decreased, and this confirmed the occurrence of the process. The highest intensity of the change in asymmetric and symmetric C-H stretching occurred at 3 months, while those of C=O and C-O stretching were observed at 4 months. The ability of the scaffolds to absorb water decreased due to the absence of these peaks, which are capable of forming hydrogen bonds with water molecules [53,54]. ID/IG was analyzed as part of the G biodegradation evaluation. During the initial phases, the ratio increased due to the addition of G but later decreased. Meanwhile, the intensity of I2D/IG increased in the G band. This shows that oxidation continued to cause biodegradation until all D, 2D, and G bands had disappeared, indicating the complete disintegration of G structure, as shown in Table 1 [54,55]. The XRD biodegradation process is illustrated in Figure 5g–j and Table 2. At 1 and 2 months, the peak at 2θ = 21.36° was similar for all scaffolds. However, at 3 months, the peak at 2θ = 21.36° had decreased for the 0.5, 1, and 1.5 wt% G, while it had increased for the 2, 2.5, and 3 wt% G. Comparison of the peaks of 0.5, 1, and 1.5 wt% G to those of 2, 2.5, and 3 wt% G at 2θ = 23.6° are presented in Figure 5g–j. The peak of 2θ = 23.6° in the two groups revealed that their intensities were reduced between 1 and 2 months. The values then increased at 3 months for 2, 2.5, and 3 wt% G scaffolds before decreasing again at 4 months, but the other groups showed the opposite condition. This finding is relatively similar to that of Raman spectroscopy, which showed that the peak associated with the mediated biodegradation process had increased [20,56]. Based on these results, G, when used as a nanofiller, can have a positive influence on the biodegradation rate of PCL and other polyesters because the hydrolytic biodegradation of other aliphatic polyesters was slowed or delayed by non-G materials. It can also have a positive effect on the hydrophobicity of the polymer, which leads to a rapid biodegradation of the PCL [16,20]. The next problem is the waste products caused by the biodegradation of the scaffold. Several studies have reported the ability of G biodegradation product to biodegrade or biotransform into less-reactive forms as well as to be naturally eliminated from the body [56,57]. Lasocka et al. [ 58] stated that scaffolds with the nanofiller generated a considerable increase in average cell mitochondrial activity, which indicates that they are harmless and can promote cell proliferation. Osteoblast-like (MG-63) cells were cultured for biocompatibility for 24 h, followed by 21 days of proliferation and differentiation. The respective MTT assay results are presented in Figure 6a. An extract containing 2.5 wt% G was shown to have a higher biocompatibility, followed by 3 wt% G ($p \leq 0.0001$). However, the values of PCL and 0.5 wt% G were less than $70\%$, indicating that they were cytotoxic, while the other samples showed values greater than $70\%$. This indicates that all the scaffolds except PCL and 0.5 wt% G were appropriate for the growth of cells [59]. The MTT assay for cell proliferation showed that the concentrations of 1, 1.5, and 3 wt% G increased steadily from day 1 to day 21, but the value for 3 wt% G was greater compared to the others ($p \leq 0.001$). This shows that they were suitable for the growth of osteoblast-like (MG-63) cells due to their consistent growth over a period of 21 days. Nevertheless, PCL and 0.5 wt% G increased from day 1 to day 7, decreased on day 14, and then increased slightly on day 21 ($p \leq 0.001$). This current study revealed that scaffold properties, such as physical (WCA) or mechanical (Young’s modulus) characteristics, have a correlation. They also increase the phase of cell proliferation by prolonging cell growth or inhibiting cellular differentiation, as shown in Figure 6b,c. The MTT result on day 21 increased, while that of the ALP declined [60,61]. ALP acts as a marker of osteoblast differentiation, and its activity in osteoblast-like (MG-63) cells was evaluated on days 1, 4, 7, 14, and 21. PCL and 3 wt% G had lower absorbances than the others on day 1 ($p \leq 0.001$), as shown in Figure 6c. On day 4, the ALP activities of cells were higher compared to the previous days. The values obtained for 2, 2.5, and 3 wt% G scaffolds were considerably higher than that of PCL and the other PCL/G scaffolds ($p \leq 0.001$). On day 7, the 1, 1.5, and 2.5 wt% G showed a steady increase which continue to day 14, while 3 wt% G absorbance was constant from day 4 to 14. All the ALP values of the scaffold decreased on day 21, particularly 1 and 1.5 wt% G. When compared to proliferation result, the 1, 1.5, and 3 wt% G samples increased greatly compared to the others on day 21, but the absorbance of 3 wt% G slowly decreased compared to the 1 and 1.5 wt% G, as shown in Figure 6c. Suh et al. [ 62] stated that the osteoblast proliferation was retarded, while the production of ALP increased. Osteoblast growth showed decreased differentiation activities during the period of rapid proliferation. As the cells slowly proliferated, they began to produce more ALP. The finding showed that the PCL/G scaffold was suitable for osteoblast growth because high concentration of ALP for long duration induced higher frequency of bone fractures (osteomalacia), which led to enlarged or abnormal bone shape due to decreasing bone mineral density [63]. SEM analysis was also carried out on osteoblast-like (MG-63) cells. On the first day (Figure 7a), the cells were uniformly distributed and adhered to the scaffolds at various concentrations. Furthermore, protruding cell membranes were observed on day 4 (Figure 7b) as evidence of their interactions with the surroundings using PCL, 0.5, and 2 wt% G scaffolds. For 1 and 3 wt% G, the cells had a round shape with protruding filaments indicating that they were entering the mitotic process. On days 7 and 14 (Figure 7c,d), almost all the cells had a round appearance, except for those on the PCL scaffold, which retained their flat shape, and the 3 wt% G scaffolds with a triangular appearance on day 7, 14, and 21 (Figure 7c–e). This indicates that the addition of G to the PCL scaffold enhanced both the proliferation and differentiation of cells [64,65]. SEM images show the adhesion, proliferation, and differentiation processes. The next step after cells adhere is proliferation, which is known as a mitotic process and requires the precise coordination of multiple signaling pathways [66]. It is affected by cell surface tension, intracellular pressure, and cortical stiffness. In the beginning, cells lose their capacity to adhere, and changes in intracellular pressure drive mitotic cells, thereby enabling them to exert a force against their surroundings. In previous studies, there was a correlation between changes in cortical stiffness and tension, such as Young’s modulus between the interphase and mitotic stages to resist whole-cell deformation [31,66]. Variations at different cell cycle stages are dependent on the depolymerization of the actin–myosin cortex, a network of filaments and contractile elements. This occurs through the increase in internal osmotic pressure, while depolymerization of actin filaments completely depends on the mechanosensing of the scaffold, which was influenced by mechanical properties. For example, a triangular shape showed on 3 wt% G (Figure 7c–e), but it was absent in the others [67,68]. ## 4. Conclusions Scaffolds for bone tissue engineering must have optimal physical, chemical, morphological, mechanical, biodegradable, and biocompatible properties for bone regeneration. The PCL/graphene (G) scaffold used in this research has the above characteristics, so it is an excellent scaffold. Due to the addition of G, PCL changes from hydrophobic (PCL) to hydrophilic (PCL/G). Compared with low concentrations of PCL/G (0.5, 1, 1.5 wt%) and PCL, the PCL/G scaffolds with high G concentrations (2, 2.5, and 3 wt%) had greater porosity. Therefore, the scaffold used in this research is suitable for the adhesion and growth of osteoblasts, especially because the scaffold’s Young’s modulus of 3 wt% G is close to that of trabecular bone. In addition, the results of the biocompatibility, proliferation, and differentiation experiments showed that the PCL/G scaffold was non-toxic, except for PCL and 0.5 wt% G, because its cell viability was lower than $70\%$ (which is the basic requirement for human beings). Further future studies need to explore the long-term toxicity of graphene-based materials as well as the mechanism of mechanotransduction and mechanosensing to fully understand their effect and application. ## References 1. Bernardi S., Macchiarelli G., Bianchi S.. 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--- title: Mitochondrial ATP Synthase and Mild Uncoupling by Butyl Ester of Rhodamine 19, C4R1 authors: - Ljubava D. Zorova - Irina B. Pevzner - Ljudmila S. Khailova - Galina A. Korshunova - Marina A. Kovaleva - Leonid I. Kovalev - Marina V. Serebryakova - Denis N. Silachev - Roman V. Sudakov - Savva D. Zorov - Tatyana I. Rokitskaya - Vasily A. Popkov - Egor Y. Plotnikov - Yuri N. Antonenko - Dmitry B. Zorov journal: Antioxidants year: 2023 pmcid: PMC10044837 doi: 10.3390/antiox12030646 license: CC BY 4.0 --- # Mitochondrial ATP Synthase and Mild Uncoupling by Butyl Ester of Rhodamine 19, C4R1 ## Abstract The homeostasis of the transmembrane potential of hydrogen ions in mitochondria is a prerequisite for the normal mitochondrial functioning. However, in different pathological conditions it is advisable to slightly reduce the membrane potential, while maintaining it at levels sufficient to produce ATP that will ensure the normal functioning of the cell. A number of chemical agents have been found to provide mild uncoupling; however, natural proteins residing in mitochondrial membrane can carry this mission, such as proteins from the UCP family, an adenine nucleotide translocator and a dicarboxylate carrier. In this study, we demonstrated that the butyl ester of rhodamine 19, C4R1, binds to the components of the mitochondrial ATP synthase complex due to electrostatic interaction and has a good uncoupling effect. The more hydrophobic derivative C12R1 binds poorly to mitochondria with less uncoupling activity. Mass spectrometry confirmed that C4R1 binds to the β-subunit of mitochondrial ATP synthase and based on molecular docking, a C4R1 binding model was constructed suggesting the binding site on the interface between the α- and β-subunits, close to the anionic amino acid residues of the β-subunit. The association of the uncoupling effect with binding suggests that the ATP synthase complex can provide induced uncoupling. ## 1. Introduction The coupling of oxidation and phosphorylation is a tight cooperation of the activity of proton pumps with ATP synthase machinery [1,2,3,4]. In addition, in some prokaryotic organisms such coupling exists between sodium pumps and ATP synthase utilizing sodium gradients [5,6,7]. On the other hand, the term “uncoupling” of oxidative phosphorylation meaning separation of oxidation from phosphorylation is also under wide use being organized by two distinct mechanisms. One of those is hidden in the coupling mechanism and reflects the loss of tightness/efficiency of this mechanism, and another one also compromising the proton potential stands aside of the proton pumps and ATP synthase providing an ion leak through either phospholipids or proteins residing in the coupling membrane. Whatever the mechanism, ultimately both uncoupling mechanisms yield the same result—lower ATP production per protons pumped through the coupling membrane [4]. There are two complementary theories of uncoupling. One process is a passive proton transfer by chemical uncouplers discharging the proton gradient. Within such mechanism, the uncoupler must have a chemical group binding a proton and being highly lipid-soluble, such complex is transferred through the membrane with subsequent dissociation of the complex, ultimately providing a proton transfer from a more acidic compartment to a less acidic one. The proton current through the artificial membranes caused by chemical uncouplers was demonstrated in classic studies many years ago [8,9]. The second uncoupling mechanism was proposed based on data demonstrating the involvement of proteins residing in the inner mitochondrial membrane. The most studied proteins belong to the class of “uncoupling proteins” (UCPs) and, historically, the earliest uncoupling protein was found in the mitochondria of brown fat (UCP1, [10]). In addition to UCP1, a similar set of its orthologs were found in different tissues with a tentative uncoupling function [11]. However, it turned out that the range of uncoupling proteins is not limited only to UCP family. Later, it has been found that proton transport across mitochondrial membranes can be significantly slowed down by chemicals blocking the activity of other protein components of the inner mitochondrial membrane. Within this set of data, we can recall the results with atractylate/carboxyatractylate which are specific inhibitors of adenine nucleotide translocator (ANT) and diminished the uncoupling efficiency of fatty acids [12]. Later, the loss of the uncoupling activity of fatty acids has been demonstrated after inhibiting the activity of mitochondrial dicarboxylate carrier [13]. Both results have been interpreted as a support of a protein-mediated mechanism of uncoupling. Recently, electrophysiological experiments confirmed the direct involvement of ANT in the fatty acid and uncoupler-mediated uncoupling [14,15,16]. We must admit that such dual mechanism including passive and mediated transfer is not unique and, for example, it is valid for a water transfer through the membrane: although lipid membranes are water permeable, aquaporins harboring the phospholipid membranes facilitate the transmembrane water transport [17]. Less interpretable was the result that 6-ketocholestanol abolished the uncoupling activity of the strongest uncouplers [18]. However, besides the direct 6-ketocholestanol effect on the lipid bilayer membrane this also suggested the presence in the inner mitochondrial membrane of proteinaceous components providing mediated proton transfer [19]. As we have already mentioned, the best-known inner membrane proteins having a common name, UCPs might potentially carry this mission. Indeed, UCP1 orthologs (UCP1-5) in mammalian mitochondria from different tissues initially were proposed to also provide an uncoupling, i.e., thermogenic role which was obvious in brown fat, but considering their low abundance this idea was highly argued [20,21,22,23]. Still, one of the most essential functions of UCPs has been proposed to prevent the production of high levels of membrane potential. The latter was suggested to be associated with high level of reactive oxygen species (ROS) production due to exponential dependence of ROS production on the values of the mitochondrial membrane potential [24]. Since in mitochondria having high mitochondrial membrane potential, production of both ATP and ROS is high, it has been found to be reasonable to regulate the production of ROS by lowering the membrane potential still keeping its values above the phosphorylation potential [25]. Fatty acids due to their limited ability to uncouple oxidative phosphorylation were named as intrinsic uncouplers providing moderate (mild) uncoupling, thus keeping the production of ROS low but optional for intracellular signaling level. The reasonable balance between the level of ATP and ROS production has been suggested to be a requisite for a healthy life [26]. There is a set of chemicals demonstrating mild uncoupling ability. Considering a general proof that mild uncoupling is therapeutically beneficial [27,28,29,30,31,32,33] although the protective mechanisms stay mainly unresolved, this study aimed to resolve this mechanism basing on assumption that mild uncouplers bind to specific mitochondrial proteins ultimately providing moderate ion leak through the mitochondrial membrane. In early studies, it has been shown that a number of mitochondria targeted agents from the SkQ family could potentially be used as therapeutic agents associated with mild mitochondrial uncoupling [29]. Over the past 15 years, a wide range of physico-chemical and biological properties of these derivatives has been comprehensively explored [25,26,27,28,29,30,31,32,33,34,35,36,37,38,39,40]. In this study, the final goal was to elucidate the mechanism of the uncoupling potency of the butyl ester of rhodamine 19 (C4R1) which, as previously shown [28], has a greater effect than the more hydrophobic analog dodecylrhodamine 19 (C12R1, see structures in Figure 1), which indirectly indicated different mechanisms of uncoupling by agents similar in structure. Of note, the ethyl ether of rhodamine 19 (rhodamine 6G, C2R1) was long known to exhibit low uncoupling activity [41,42]. It has been shown previously that C12R1 has higher protonophoric activity compared to C4R1 on artificial lipid membranes [39,43]. ## 2.1. Synthesis of Rhodamine Derivatives Derivatives of rhodamine 19 were synthesized in A.N.Belozersky Institute by Natalia V. Sumbatyan and Galina A. Korshunova, as described in [38,40]. ## 2.2. Isolation of Rat Liver Mitochondria Rat liver and heart mitochondria were isolated using the standard method of differential centrifugation [44] in a medium containing 250 mM sucrose, 5 mM MOPS, 1 mM EGTA, and bovine serum albumin (0.5 mg/mL), pH 7.4. The final washing was performed in a medium of the same composition but without albumin. Protein concentration was determined using the biuret method. Handling of animals and experimental procedures with them were conducted in accordance with the international guidelines for animal care and use and were approved by the Institutional Ethics Committee of Belozersky Institute of Physico-Chemical Biology at Moscow State University. ## 2.3. Mitochondrial Respiration Respiration of isolated rat liver mitochondria was measured using a standard polarographic technique with a Clark-type oxygen electrode (Strathkelvin Instruments, Motherwell, UK) at 25 °C using the 782 system software. The incubation medium contained 250 mM sucrose, 5 mM MOPS, 1 mM MgCl2, 1 mM KH2PO4 and 0.2 mM EGTA, pH 7.4. The mitochondrial protein concentration was 0.8 mg/mL. Oxygen uptake was expressed as nmol/min mg protein. ## 2.4. Isolation of ATP Synthase Complex (FoF1, Complex V) FoF1 was purified from isolated rat heart mitochondria according to the manufacturer protocol using ATP Synthase Immunocapture Kit (Abcam, Waltham, MA, USA, ab109715). ## 2.5. Electric Potential of Membranes of Bacillus subtilis with DiS-C3-(5) The Bacillus subtilis strain BR151 was used in experiments. Bacillus subtilis cells were grown in Luria Bertani (LB) broth overnight at 37 °C in an incubator shaker at 210 rpm. The overnight culture reached A600 (absorbance) of 1.5. Cells were diluted 1:20 in the buffer containing 100 mM KCl, 10 mM Tris, pH 7.4. The fluorescence reading of DiS-C3-[5] was monitored by using a Panorama Fluorat 02 spectrofluorimeter (Lumex, Russia), with an excitation wavelength of 622 nm and an emission wavelength of 690 nm. ## 2.6. Binding C4R and C12R1 with Mitochondria and Bacillus subtilis Elucidated by 1D Electrophoresis Mitochondrial samples, their fractions and preparations of purified complex V were mixed with a 4x sample buffer containing 0.125 M Tris-HCl (pH 6.8), $4\%$ sodium dodecyl sulfate, $40\%$ glycerol, $0.05\%$ bromophenol blue, and $10\%$ 2-mercaptoethanol, boiled for 5 min and applied to Tris-glycine polyacrylamide gel (40 μg per well), having previously determined the concentration of total protein using a set of bicynchoninic acid (Sigma, Burlington, MA, USA). Electrophoresis was carried out at 20 mA until the stain came out of the separating gel. At the end of electrophoresis, the fluorescence in the gel was visualized using the FUJIFILM FLA3000 PhosphorImager at an excitation wavelength of 532 nm. To study the binding to the probe after electrophoretic separation of proteins, the gel was incubated with a solution of 20 µM C4R1 for 30 min at a constant rocking of a gel. After that, the gel was washed in a buffer containing 0.375 M Tris-HCl pH 8.8. Fluorescence was evaluated using PhosphorImager at an excitation wavelength of 532 nm. ## 2.7. Two-Dimensional Gel Electrophoresis The protein extracts were fractionated by two-dimensional electrophoresis according to [45]. IEF was performed in glass tubes (2.4 × 180 mm) filled with $4\%$ PAAG prepared in a 9 M urea solution containing $2\%$ TritonX-100 and a $2\%$ mixture of ampholines with pH 5–7 and pH 3.5–10 were used at a ratio of 4:1. The protein extracts (100–150 μL) were applied to the “acid border” of each gel column, and IEF was performed for 3 h at 1400 V/h (Model 175; Bio-Rad, Hercules, CA, USA). The fractionation in the second direction (SDS slab gel electrophoresis with the acrylamide concentration gradient 5–$20\%$) was performed, as previously described [46]. ## 2.8. Mass Spectrometry Polyacrylamide gel stained with Coomassie Brilliant Blue was exposed to trypsin, as described earlier [46]. Gel pieces of about 2 mm3 were destained twice with 50 mM NH4HCO3, $40\%$ aqueous acetonitrile solution, pH 7.5; dehydrated with 200 mL of $100\%$ acetonitrile and rehydrated with 5 mL of the digestion solution containing 15 µg/mL sequencing grade trypsin (Promega) in 50 mM NH4HCO3, aqueous solution, pH 7.5. Digestion was carried out at 37 °C for 6 h. The resulting peptides were extracted with 5 mL of $0.5\%$ TFA, $30\%$ acetonitrile solution. A 1 μL aliquot of in-gel tryptic digest extract was mixed with 0.5 mL of 2,5-dihydroxybenzoic acid solution (30 mg/mL in $30\%$ acetonitrile, $0.5\%$ TFA) and left to dry on the stainless-steel target plate. MALDI-TOF MS analysis was performed on an UltrafleXetreme MALDI-TOF-TOF mass spectrometer (Bruker Daltonik, Bremen, Germany). The MH+ molecular ions were measured in a reflector mode; the accuracy of the monoisotopic mass peak measurement was within 50 ppm. Mass spectra were processed with the FlexAnalysis 3.2 software (Bruker Daltonik, Germany). Protein identification was carried out by peptide fingerprint search with the use of Mascot software version 2.3.02 (Matrix Science) through the Home SwissProt protein database. One missed cleavage, Met oxidation and Cys-propionamide were permitted. Protein scores greater than 70 were considered to be significant ($p \leq 0.05$). ## 2.9. Molecular Docking The Swiss-Model service was used to model the structures of ATP synthases in humans, rats, E. coli and B. subtilis by homology. Further, it became necessary to scan the surface of the subunit by molecular docking methods in order to identify the intended binding site, considering necessity to exclude the interaction surfaces sterically closed by neighboring subunits. For this, the resulting structure of each subunit used in the work was embedded in the extra-membrane part of the yeast ATP synthase complex. Using the Gromacs software package, the point charges of the resulting complex were obtained, which are necessary for calculating electrostatic interactions. The structure of the Rhodamine 6G which is very similar to the molecule of C4R1 and was available from the QacR(E120Q) structure (https://www.wwpdb.org/pdb?id=pdb_00003br6, assessed on 1 December 2022). The charges for the molecule were obtained using the tppmktop service (http://erg.biophys.msu.ru/tpp/, assessed on 1 December 2022) and the LigParGen service (http://zarbi.chem.yale.edu/ligpargen/, accessed on 1 December 2022). The Autodock-Vina program was used to calculate the most optimal position of the ligand in the volume of a rectangle of certain dimensions (the dimensions of the intended binding site), taking into account the non-covalent interactions of the rhodamine 6G and C4R1 molecules with subunits of ATP synthase. To search for the binding site, a small iterator was written using Python methods, taking a list of atoms on the surface of the subunit as the input, setting certain boundaries of local docking (cube 303030 angstrom) centered in the coordinates of each atom from the list, and triggering the calculation of molecular docking. As a result, a set of conformations of rhodamine molecules associated with the investigated ATP synthase subunit was obtained. From all these sets, the conformation with the lowest energy value was selected. ## 3.1. Uncoupling Activity of C4R1 in Mitochondria The first goal was to confirm the uncoupling effect of C4R1, evaluated both by the activation of respiration in state 4 and by the effect on the maximal and ATP synthase-controlled respiration rate (states 3u and 3 correspondingly) of mitochondria isolated from rat liver. Figure 2A shows that C4R1 used in concentration 1–2 µM activates state 4. On the other hand, the effect on initial respiration of its more hydrophobic analogue C12R1 used in the same concentrations was negligible while demonstrating a slightly diminished respiration rate at state 3. This data correlates with the previously shown one that the short-chain derivative of rhodamine 19 (C4R1) in the same concentrations as C12R1 causes a faster and more significant drop in the transmembrane potential of mitochondria [28]. ## 3.2. The Effects of C4R1 and C12R1 in Bacillus subtilis Intact Cells The idea of the involvement of some mitochondrial membrane protein in the process of C4R1-mediated uncoupling was tested using another model, namely intact Gram-positive Bacillus subtilis cells. The addition of 0.6 µM C12R1 led to a decrease in the membrane potential of the bacterial cells, as judged by an increase in fluorescence of the potential-sensitive dye DiS-C3-[5] (Figure 3) known to be quenched by membrane potential [47,48,49]. On the other hand, C4R1 at the same concentration exhibited an almost insignificant effect. The action of C12R1 on the potential was delayed and characterized by slow kinetics depending on the concentration of C12R1 (Figure 3). Thus, in B. subtilis cells we observed the phenomenon fully opposite to that in mitochondria. This indicated that the mechanisms of uncoupling in mitochondria and B. subtilis cells are different. ## 3.3. Binding C4R1 and C12R1 with Mitochondria and Bacterial Cells For a more detailed study of the mechanism of C4R1-induced uncoupling of mitochondria, we incubated isolated rat liver mitochondria with C4R1, after which the mitochondria were solubilized and subjected to electrophoresis in PAAG with subsequent detection of fluorescence in the gel. Our attempts to use two-dimensional electrophoresis for this purpose failed and we were unable to detect fluorescence in any of the many electrophoretic spots detected by the Coomassie blue staining. On the other hand, we found that on the gel, all fluorescence was located in the frontal region near the maximum and minimum pH values (about 3 and 10, see Supplemental Figure S1). We must admit that in accordance with the technique of two-dimensional electrophoresis, in the first dimension there is a separation along the pH gradient, that is, from acidic values to more alkaline, and in the second dimension the separation goes according to the molecular size of proteins. We assumed that in the initial phase of the procedure, the release of the C4R1 binding to the protein occurs, resulting in the formation of free C4R1, which quickly goes into the frontal zone, and cleavage occurs at both low and high pH values. To confirm this, we used 1D electrophoresis and load a sample containing alkaline (NaOH with a final pH 10) to the gel well. Manipulation with the sample, such as heating, did not change the result which was negative: none of the bands in the resulting gel contained fluorescence confirming that an alkaline pH prevents C4R1 binding to any of the mitochondrial proteins. However, if a neutral pH was maintained in the mitochondrial sample (pH 7.2), we were able to observe few fluorescing bands with obvious major band around 48–50 kDa after 1D electrophoresis. The experiment was executed in two ways: the first run was with mitochondria incubated under different modes: [1] without C4R1; [2] with the dye; [3] with the dye in the presence of an uncoupler to exclude the effect of the membrane potential; and [4] with the dye in the presence of alkali (Figure 4). Two mitochondrial preps were used: coupled rat liver mitochondria (RLM) and rat heart mitochondria (RHM). In addition, in one electrophoretic well, we placed a prep of pure ATP synthase isolated from a rat heart. Then, the same gel was soaked with free C4R1 (20 µM) for 30 min and after this, we were able to detect the fluorescing 48–50 kDa component in all samples except that treated with alkali (Figure 4). Importantly, pure isolated ATP synthase complex binds C4R1 with an obviously major component having a molecular mass around 48–50 kDa. Altogether, this data pointed to the fact that some components of the ATP synthase complex (Complex V) with a molecular mass in the range 48–50 kDa (e.g., α and/or β subunits of F1 part of Complex V) bind C4R1. High pH sensitivity of the binding indirectly pointed to its electrostatic nature. We found that C4R1 binding to the 48–50 kDa component was insensitive to the presence of thyroid hormone T3 (triiodothyronine) which also belongs to mild uncouplers indicating a non-competitive character of action of these two compounds (Figure 5). We also compared the mitochondrial binding efficiency of C4R1, C12R1 and another rhodamine derivative carrying antioxidative moiety namely SkQR1 (for structure, see [29]). In contrast to the strong binding of C4R1 to the 48–50 kDa band, SkQR1 had many mitochondrial proteins to bind with and there was a faint binding of C12R1 to the 48–50 kDa band (Figure 6). Next, we tested whether the extracts from B. subtilis bind C4R1 considering that uncoupling activity of C4R1 in these cells was low (see, Figure 3) in contrast to C12R1 demonstrating high efficiency in lowering the membrane potential of these bacterial cells. We found that binding of C4R1 to B. subtilis was negligible (Figure 7). ## 3.4. Identification of the Mitochondrial Component That Binds C4R1 The next goal was to clearly identify the nature of the 48–50 kDa component. To do this, we excised from the gels fluorescing bands of different regions and examined their content by mass spectrometry. One sample contained all fluorescing bands from the region covering molecular masses of 23–50 kDa. In this mixture, a total of 97 peptides were identified with a major contribution of ATP synthase subunit beta and ATP synthase subunit alpha with a mascot score of 517. The highest single protein hit was ATP synthase subunit beta with the number of mass values matched being 51 which covered $87\%$ of its sequence. For ATP synthase subunit alpha, the score was 160, the number of mass values matched was 30 which covered $49\%$ of its sequence. We must admit that in the gel, we could not resolve the binding of a probe to α or β subunits since visually we detected a single fluorescing band in this region. However, for accurate detection from the fluorescent gel, we cut two strips in accordance with the Coomassie staining, corresponding to 48 kDa (presumably belonging to the β-subunit) and 50 kDa (presumably, belonging to α subunit of ATP synthase) bands. Indeed, these two bands contained either α-subunit (mascot score 252, number of peptides:34, sequence coverage $49\%$) or β-subunit (mascot score 417, number of peptides:56, sequence coverage $87\%$). Ultimately, this indicates that ATP synthase subunits α and β were indeed the proteins abundant in selected gel bands (mascot analysis is presented in the archive of the Supplement). ## 3.5. Modelling of C4R1 Binding to Mitochondrial Component First, we created a model of the specific binding of Rhodamine 6G (which is structurally similar to C4R1 and can be named C2R1) to the β-subunit of the ATP synthase. The simulation took into account that rhodamine molecule binds to mitochondria, and not to B. subtilis (Figure 8). We assumed that the different binding mode was caused by a difference in the structure of the β-subunit of the ATP synthase. The next step was to represent the binding locus on the ATP synthase structure for the molecule, which was investigated using the approaches outlined above for C2R1 (Rhodamine 6G), that is, the C4R1 molecule (Figure 9). As expected, the binding was at the same locus of ATP synthase with the participation of the same amino acid residues that participated in the binding of C2R1. Thus, this locus on the α-subunit is to some extent universal for binding analogues of rhodamine 6G, including C4R1. Figure S2 shows the alignment of the structures of the β-subunit of the mitochondrial and bacterial ATP synthase. The fact that binding was very sensitive to pH provides convincing evidence of the electrostatic interaction of positively charged rhodamine molecule with a negatively charged region on the β-subunit. Ultimately, we outlined a few negatively charged specific sites that differs in mitochondria and B. subtilis which in general meets the criteria of the model. ## 4. Discussion In this work, we revealed the association of the uncoupling action of the penetrating C4R1 cation with the predominant binding of this agent to the component of the ATP synthase complex, namely the β-subunit of the F1 component. Although we have not established a causal relationship between binding and uncoupling; nevertheless, given that the binding of C4R1 to other components than mitochondrial ATP synthase was minor, we assumed that it was ATP synthase that could be responsible for mild uncoupling, and our assumption is supported by basic knowledge about the mechanisms of uncoupling. The study of the uncoupling mechanism is primarily determined by practical interest, because in various pathological situations, the beneficial effect of a slight decrease in the mitochondrial membrane potential has been proven, contributing to the mild course of pathology and a better outcome. Such a positive effect, in particular, has been shown in an injured brain and kidney [26,27,28,29,31,38,40]. By its principle, uncoupling of oxidative phosphorylation results in a lower efficiency of ATP-synthetic mitochondrial machinery. The efficiency of mitochondrial ATP synthesis is determined by both the intrinsic properties of ATP synthase depending on its working architecture [50] and from the extrinsic properties of the mitochondrial inner membrane affording an active or passive ion leak compromising the membrane potential [51]. Generally speaking, the activation of respiration is not always associated with the drop in the membrane potential, at least in living cells. An example is taken from the diazoxide-induced activation of respiration in cardiac myocytes, which was not associated with a drop in membrane potential, i.e., not through effective uncoupling. This higher respiration was interpreted as an accelerated respiratory flux, and because of this, it was associated with higher ROS production [52,53]. Although, strictly speaking, diazoxide, which is the opener of the ATP-dependent K-channel, cannot easily be considered as a mild uncoupler; however, in a practical sense it can. Theoretically, any mild uncoupler could do the same thing—activate respiration without changing the membrane potential while increasing the total production of ROS. This situation is unlikely to be accomplished in isolated mitochondria, but in a living cell with a high content of ATP, which can be used to maintain the membrane potential of mitochondria, it does not look improbable. If such a scenario is allowed, then a moderate uncoupling can not only reduce but also increase ROS production. The latter can be achieved by activating proton pumping and increasing oxygen flux in parallel with higher ROS production, given that a constant part of the oxygen consumption by mitochondria is utilized for ROS production. This could compensate for the drop in membrane potential caused by an ion leak, providing a stable membrane potential. The possible conflict of the data obtained with isolated mitochondria and living cells makes it necessary to be very careful when interpreting the data obtained in isolated mitochondria [53]. Considering all these arguments, the beneficial effect of mild uncoupling can be associated not (exclusively) with changes in ROS levels, but also with other components resulting from the activation of respiration, such as an increased production of CO2 and H2O and higher utilization of substrates (for example, fat). CO2 is known as an essential physiological regulator of cellular metabolism [54,55,56,57,58,59,60,61]. Recent data indicates that bicarbonate may also be involved in the mechanism of mitochondrial membrane potential stability [61], which indirectly demonstrates an obvious relationship between CO2 levels and the uncoupling mechanism. Water is another important product of the uncoupling process, and even its slight increase in the mitochondrial matrix can activate mitochondrial metabolism through the so-called “regulatory swelling of mitochondria” [52,53], which is an integral part of the cell protective mechanism. In addition, we must admit that the mild ROS burst (at least temporarily) can also be useful due to its participation in the protective mechanism of ischemic preconditioning [53]. Whatever the source of the useful mechanism of mild uncoupling, our data is in support of that the ATP synthase complex can be considered as belonging to more extensively determined uncoupling proteins. Our data on the binding of the fluorescent agent C4R1, which confirmed its properties of the mild uncoupler in experiments using isolated mitochondria directly indicated the participation of ATPase in the process of uncoupling. We repeat that this study is so far limited to the statement that the binding of C4R1 to ATP synthase is associated with subsequent uncoupling, without insisting on the presence of a cause–effect relationship. It should be noted that data of this study indicates that binding occurs exclusively with alpha and beta structures of the ATPase, and other components of the ATP synthase show only very slight binding to C4R1; however, the participation of components such as gamma stalk and delta subunits can certainly be highly probable. Possibly, other, if not all, components of ATP synthase complex might be involved in the resulting uncoupling because, without a doubt, a proton leak preferably goes through the structure in Fo [3] which contains numerous components, and additional work is needed to uncover the exact mechanism of C4R1 (and may be other drugs)-induced uncoupling. Additionally, we must admit that there is a conflict of ideologies on the functioning of ATP synthase: while one [62,63] insisted that there is a slip in the rotation of the c-subunit disk in the membrane, alternative opinions excluded leak and slip during the normal operation of ATP synthase [50,64,65]. In this work, ATP synthase was not under normal operation mode, which is caused by the binding of a mild uncoupler. We assume that switching from the normal fully coupled mode of operation of ATP synthase to the leaking mode of operation occurring after binding with the uncoupling agent is due to a change in the chemical interaction of Fo and F1. Thus, many components of Fo may take part in the binding-related uncoupling mechanism, but a proper assessment of such an interaction will require additional extensive work. We should note that in our argumentation, we do not take into account the lability of the structure of the ATP synthase complex, in particular its state of dimerization-monomerization, which, according to existing concepts, takes part in the organization of the mitochondrial pore, that is mitochondrial permeability transition (reviewed in [66]). First, uncoupling and permeability transition are different phenomena and secondly, the change in ultrastructural conformations under these conditions goes in different directions, leading to swelling under induction of permeability transition and condensation of the matrix under uncoupling, which differently affects the conformation of the mitochondrial cristae of the inner membrane of mitochondria, possibly participating in the dimerization-monomerization process [66]. We have also excluded from consideration the regulatory role of guanine nucleotides, which are extremely important in the regulation of uncoupling proteins of the UCP family [23], but do not play a role in uncoupling going with the participation of other proteins that are not structurally homologous to the UCPs [12,13,14,15]. Finally, we must note that in the seventies of the last century, based on studies with an affinity-labeling uncoupler, the evidence was obtained that mitochondria contain a specific uncoupler binding site (protein of 30 kDa), which is capable of ATP-Pi exchange, suggesting that this site is located in complex V [67]. ## 5. Conclusions. Uncoupling Proteins: One More? In this study, we confirmed that the process of uncoupling of oxidative phosphorylation in mitochondria can be mediated by mitochondrial proteins residing in the inner membrane of mitochondria. Recently, the old dogma that it is enough to have only a bilayer phospholipid membrane and small molecules that can be protonated on the one side of the membrane and release protons on the other side has become the subject of revision. 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--- title: Muscle Oxidative Stress Plays a Role in Hyperthyroidism-Linked Insulin Resistance authors: - Gianluca Fasciolo - Gaetana Napolitano - Marianna Aprile - Simona Cataldi - Valerio Costa - Maria Teresa Muscari Tomajoli - Assunta Lombardi - Sergio Di Meo - Paola Venditti journal: Antioxidants year: 2023 pmcid: PMC10044862 doi: 10.3390/antiox12030592 license: CC BY 4.0 --- # Muscle Oxidative Stress Plays a Role in Hyperthyroidism-Linked Insulin Resistance ## Abstract While a low level of ROS plays a role in cellular regulatory processes, a high level can lead to oxidative stress and cellular dysfunction. Insulin resistance (IR) is one of the dysfunctions in which oxidative stress occurs and, until now, the factors underlying the correlation between oxidative stress and IR were unclear and incomplete. This study aims to explore this correlation in skeletal muscle, a tissue relevant to insulin-mediated glucose disposal, using the hyperthyroid rat as a model of oxidative stress. The development of IR in the liver from hyperthyroid animals has been widely reported, whereas data concerning the muscle are quite controversial. Thus, we investigated whether hyperthyroidism induces IR in skeletal muscle and the role of oxidative stress in this process. Particularly, we compared the effects of hyperthyroidism on IR both in the absence and presence of vitamin E (Vit E), acting as an antioxidant. Putative correlations between ROS production, oxidative stress markers, antioxidant capacity and changes in intracellular signalling pathways related to insulin action (AKT) and cellular stress response (EIF2α; JNK; PGC1α; BIP; and NRF1) were investigated. Moreover, we assessed the effects of hyperthyroidism and Vit E on the expression levels of genes encoding for glucose transporters (Slc2a1; Slc2a4), factors involved in lipid homeostasis and insulin signalling (Pparg; Ppara, Cd36), as well as for one of the IR-related inflammatory factors, i.e., interleukin 1b (Il1b). Our results suggest that hyperthyroidism-linked oxidative stress plays a role in IR development in muscle and that an adequate antioxidant status, obtained by vitamin E supplementation, that mitigates oxidative stress, may prevent IR development. ## 1. Introduction Reactive oxygen species (ROS) represent by-products of aerobic metabolism and, at low concentrations, are involved in several regulatory processes [1]. However, due to their reactive nature, which makes them capable of oxidizing and damaging biomolecules, they can lead to cellular and tissue dysfunction [2]. Therefore, the cellular ROS content is finely regulated, and the evolution of aerobic life has gone hand in hand with that of an antioxidant defense system to protect cells from the action of ROS [1]. The cellular antioxidant defense system comprising enzymatic and non-enzymatic systems contributes to establishing a dynamic balance between ROS production and removal (redox homeostasis) [1]. In various physio-pathological states, an imbalance between ROS production and their removal can cause oxidatively damaged macromolecule accumulation, which predisposes to cellular and tissue dysfunction [2]. Insulin resistance (IR) is one of the dysfunctions in which oxidative stress occurs [3]. In skeletal muscle and adipocytes, the translocation of glucose transporter 4 (GLUT4) from the cytoplasm to the plasma membrane is triggered by insulin and requires the activation of signaling pathways, involving the phosphatidyl inositol 3 kinases (PI3K) and protein kinase B (PKB, also known as AKT) [4]. In insulin-resistant cells, the impaired insulin response leads to a reduced GLUT4 translocation on the plasma membrane with a consequent increase in blood glucose levels. In turn, the pancreas secretes higher amounts of insulin, activating a positive feedback loop which results in high plasma insulin levels and insulin desensitization of peripheral tissues [4]. IR can be the result of various systemic insults, many of which determine increased oxidative stress, including increased caloric intake [5], sedentary lifestyle [6] and inflammation [7]. Although the link between oxidative stress and IR is of great interest, the knowledge about factors underlying this correlation is still unclear or incomplete, as well as the role played by oxidative stress as a cause or a consequence of IR. Given the pivotal role of skeletal muscle in insulin-mediated glucose disposal, our study aims to assess in this tissue the correlation between IR and oxidative stress. To this aim, we used the hyperthyroid rat as an oxidative stress model. Thyroid hormones contribute to glucose homeostasis, but, in the hyperthyroid state, a well-known condition called thyroid diabetes [8] can develop. Thyroid diabetes could conceivably be a consequence of thyroid-hormone-induced oxidative stress [4]. In hyperthyroid animals, the development of IR in the liver tissue has been widely reported [4], while data on muscles are quite controversial, reporting both increases [9,10,11] and reductions [12] in glucose absorption. Specifically, we studied whether hyperthyroidism could induce IR in the skeletal muscle and the contribution of the oxidative stress to this process. For this purpose, we directly compared two conditions in which the extent of oxidative stress is different, i.e., absence or presence of antioxidant supplementation with Vit E, which reinforces the antioxidant defense and attenuates the oxidative stress. Notably, we verified the existence of correlations among ROS production, markers of oxidative stress, antioxidant capacities and changes in intracellular signaling pathways related to insulin action (phosphorylation of serine/threonine kinase AKT), and the cellular stress response (Eukaryotic Translation Initiation Factor 2α, EIF2α; c-jun NH2 terminal kinases, JNK; Peroxisome proliferator-activated receptor-Gamma Coactivator 1α, PGC-1α; Binding Immunoglobulin Protein, BIP; and Nuclear Respiratory Factor 1, NRF1). Furthermore, we evaluated the effects of hyperthyroidism and Vit E on the expression levels of genes encoding glucose transporters (Solute carrier family 2 member 1, Slc2a1, encoding Glut1; Solute carrier family 2 member 4, Slc2a4, encoding Glut4), factors involved in lipid homeostasis (five), as well as for the interleukin 1b (Il1b) strongly correlated with IR [13,14,15]. Overall, our results suggest that oxidative stress contributes to the development of muscle IR since the attenuation of oxidative stress due to vitamin E supplementation can prevent the development of IR. ## 2.1. Animals The “Ethical-Scientific Committee for Animal Experimentation” of the University of Naples Federico II and the Italian Minister of Health approved all experimental procedures on animals (authorization n° $\frac{836}{2019}$ PR). Thirty-two male Wistar rats (Envigo, Bresso, Italy) were used for the experiments and fed a control diet (Mucedola, Milan, Italy), which contained 70 mg/kg of α-tocopherol until day 80 of age. They were then divided into four groups: rats fed the control diet for ten days (C); rats fed a diet supplemented with 700 mg/kg of α-tocopherol (C + VE) for ten days; and rats rendered hyperthyroid (H) by intraperitoneal administration of triiodothyronine (T3, Sigma-Aldrich, St. Louis, MO, USA), 50 μg/100 body weight) for ten days; rats made hyperthyroid and fed the α-tocopherol (H + VE) enriched diet. Thyroid hormone treatment was chosen considering that IR occurs after ten days of such a T3 administration [16]. The Vit E treatment was chosen because it attenuates both oxidative damage [17] and, also thyroid-hormone-induced alterations of rat heart electrical activity and contraction frequency [18]. Rats were housed at 24 ± 1 °C, with a constant artificial circadian cycle of 12 h of light and 12 h of dark, and 55 ± $10\%$ relative humidity, and received water and food ad libitum. At the end of the experimental period, the glucose tolerance test was performed (as described below) and, the following day, the animals were euthanized after sedation with an intraperitoneal injection of Zoletil (60 mg/kg of body weight). Gastrocnemius muscles were harvested from both hind legs and placed in a beaker containing homogenization solution (HM) (220 mM mannitol, 70 mM sucrose, 1 mM EDTA, $0.1\%$ fatty acid-free albumin, 10 mM Tris, pH 7.4) placed on ice. ## 2.2. Glucose Tolerance Test Venus blood samples were obtained after 6 h of fasting from a small cut on the tail to determine basal glycemia and insulinemia. Then, after administering a glucose load (2 g/kg bw, i.p. injection), blood was drawn after 15, 30, 60, 90 and 120 min. All samples were centrifuged at 1400× g for 8 min at 4 °C and separated plasma was stored at −20 °C. Glucose and insulin levels were determined by a glucometer and a commercial ELISA kit (Mercodia, Winston Salem, NC, USA), rat insulin), respectively. ## 2.3. Tissue Preparations Gastrocnemius muscles were cleaned from adipose and connective tissue, weighed, minced, and washed with HM. The tissue fragments were incubated with HM containing 0.1 mg/mL Protease (Sigma, St. Louis, MO, USA)) for 5 min. Then, they were rinsed and gently homogenized in HM (1:10 w/v) with a Potter Elvejem homogenizer at a speed of 500 rpm, for 2 min. The Vit E content was determined in aliquots of homogenate deproteinized with methanol and subjected to extraction with n-hexane. The hexane was evaporated in the presence of N2 at 40 °C, and the residues were resuspended in ethanol. The Lang [19] HPLC procedure was used to determine the Vit E content, and quantification was achieved using an external standard. Other aliquots of the muscle homogenates were used either for the other assays or to isolate the mitochondrial fractions. ## 2.4. Isolation of Muscle Mitochondria The supernatant obtained using centrifugation of the muscle homogenates (500× g for 10 min at 4 °C) to remove cell debris and nuclei was centrifuged at 3000× g for 10 min at 4 °C to obtain the mitochondrial pellets. Mitochondria were washed twice in washing buffer (WB) (220 mM mannitol, 70 mM sucrose, 1 mM EGTA, 20 mM Tris, pH 7.4) and finally resuspended in WB. The protein content in the mitochondrial fraction was determined with the biuret method. ## 2.5. Oxidative Damage Assessment and In Vitro Susceptibility to Oxidative Stress Lipid and protein oxidative damages were determined in both muscle homogenates and mitochondria by determining the lipid hydroperoxides (Hps) levels [20], and protein-bound carbonyls (CO) [21,22]. After exposure of homogenates and mitochondria to Fe and ascorbate (Fe/Asc, $\frac{100}{1000}$ μM, respectively) for 10 min, the susceptibility to oxidative stress was assessed by measuring the changes in hydroperoxides content [22]. ## 2.6. Total ROS Content The total ROS level was assessed by measuring the ROS-induced conversion of the non-fluorescent 2′,7′-dichlorodihydrofluorescin diacetate (DCFH-DA) in the fluorescent dichlorofluorescein (DCF), according to Driver [23] and as we previously described [22]. Briefly, 12.5 µg of homogenate proteins was incubated for 15 min in 10 µM of DCFH-DA in monobasic phosphate buffer 0.1 M, pH 7.4. After the addition of 100 µM of FeCl3, the mixture was incubated for 30 min. The production of the fluorescent DCF (485 excitation wavelength, 530 emission wavelength) was measured with a multimode microplate reader (Synergy™ HTX Multimode Microplate Reader, BioTek (Winooski, VT, USA). ## 2.7. NADPH Oxidase (NOX) Activity Assay The activity of the NADPH oxidase was assessed in homogenate samples [24,25] by determining the reduction in ferricytochrome c acetylated (80 μM) at 550, at room temperature, with NADPH as a substrate. The activity of the enzyme was calculated as the difference between the values obtained in the presence and absence superoxide dismutase, 100 μg/mL. ## 2.8. H2O2 Mitochondrial Release The rate of the release of H2O2 from mitochondria was assessed through measuring at 30 °C the increase in fluorescence due to H2O2-induced oxidation of p-hydroxyphenylacetate (PHPA, excitation at 320 nm, emission at 400 nm) catalyzed by horseradish peroxidase (HRP) [26] by a fluorometer (JASCO Deutschland GmbH, Pfungstadt, Germany). Mitochondrial proteins (0.1 mg ∙mL−1) were incubated in a buffer containing HRP 6 UmL, PHPA 200 μg/mL, KCl 145 mM, Hepes 30 mM, KH2PO4 5 mM, MgCl2 3 mM, EGTA 0.1 mM, $1\%$ BSA, and pH 7.4. Mitochondrial H2O2 release was assessed on respiring mitochondria using as respiratory substrates 10 mM pyruvate plus 2.5 mM malate in the absence (basal respiration) or in the presence of 500 μM ADP (ADP-stimulated respiration). ## 2.9. Antioxidant Enzymes Activities The activity of the glutathione peroxidase (GPX) was determined at 37 °C using H2O2 and reduced glutathione (GSH) as substrates and measuring the rate of NADPH oxidation catalysed by glutathione reductase (GR) which reduces the oxidized glutathione (GSSG) obtained by the reaction [27], using a multi-mode microplate reader (Synergy™ HTX Multi-Mode Microplate Reader, BioTek). Similarly, glutathione reductase activity (GR) was measured at 37 °C using GSSG as a substrate and assessing the oxidation rate of NADPH [28] using the microplate reader. Catalase activity was assessed according to Aebi [29]. Superoxide dismutase specific activity was measured at 25 °C assessing the decrease in the reduction rate of cytochrome c at 550 nm due to the superoxide radicals, produced by the xanthine–xanthine oxidase system. In brief, muscle homogenates were added to a solution containing 0.1 mM EDTA, 2 mM KCN, 50 mM KH2PO4, pH 7.8, 20 mM cytochrome c, 5 mM xanthine, and 0.01 U of xanthine oxidase. A unit of SOD activity corresponds to the enzyme concentration able to inhibit the reduction in cytochrome c by $50\%$ in the presence of xanthine + xanthine oxidase [30]. ## 2.10. Tissue and Mitochondrial Respiration Tissue and mitochondrial respiration were assessed at 30° by an Hansatech respirometer in 1.0 mL of incubation medium (145 mM KCl, 30 mM Hepes, 5 mM KH2PO4, 3 mM MgCl2, 0.1 mM EGTA, $1\%$ BSA, pH 7.4) containing 50 μL of $20\%$ (w/v) homogenate or 0.25 mg/mL of mitochondrial protein. Pyruvate plus malate (10 and 2.5 mM, respectively) were used as respiratory substrates, in absence (basal respiration) or in presence (ADP-stimulated respiration) of ADP (500 μM). ## 2.11. Response of Skeletal Muscle to Insulin and Immunoblotting Analyses Information on muscle sensitivity to insulin were obtained by exposing pieces of gastrocnemius muscle (50 mg) to 1 μM insulin (Sigma-Aldrich) for 30 min at 37 °C in a Krebs solution according to Amouzou et al., 2016 [31]. At the end of incubation period, the muscle pieces were lysed. Particularly, muscle fragments were incubated for 15 min in a lysis buffer (pH 8, containing 150 mM NaCl, 50 mM Tris-HCl, $0.5\%$ nonidet P-40, $0.5\%$ sodium deoxycholate, $0.1\%$ SDS) supplemented with Tissue Protease Inhibitor Cocktail (Sigma-Aldrich, 1:500, v/v). Lysates were centrifuged at 12,000× g for 30 min at 4 °C, and protein concentration was assessed by the biuret method. Immunoblotting was performed as previously reported [32], using the following primary antibodies: p-AKT (sc-377556, Santa Cruz, San Diego, CA, USA), Akt (sc-81434, Santa Cruz, San Diego, CA, USA), JNK (sc-7345, Santa Cruz, San Diego, CA, USA), p-JNK(sc-6254, Santa Cruz, San Diego, CA, USA)); EIF2α (L57A5, Cell Signaling Technology, Danvers, MA, USA); EIF2αP (9722, Cell Signaling Technology); GRP78 BIP (C50B12, Cell Signaling Technology); PGC-1 (sc-13067, Santa Cruz, San Diego, CA, USA), NRF1 (sc-33771, Santa Cruz, San Diego, CA, USA), and β-actin (A2066, Sigma-Aldrich, St. Louis, MO, USA). Secondary antibodies were from Sigma- Aldrich (sc-2030, Santa Cruz, San Diego, CA, USA). The excellent chemiluminescent detection Kit (ElabScience, Microtech, Naples, Italy) was used for the analysis and visualization of the immunoreactive bands, according to the manufacturer’s instructions. Densitometry data were generated analyzing ChemiDoc images or digital images of X-ray films exposed to immunostained membranes, and signal quantification was performed by Un-Scan-It gel software (Silk Scientific, UT, USA). The protein expression levels, a standard control sample was run on each gel, and all test values were compared to the expression levels analyzed in the control sample (control value = 1). ## 2.12. RNA Isolation, RT-PCR, and qPCR “TRIzol Reagent” (Thermo Fisher Scientific, Waltham, Massachusetts, MN, USA) was used for total RNA isolation from skeletal muscle samples after tissue homogenization, according to the manufacturer’s instructions. RNA quantification was performed by NanoDrop spectrophotometer and reverse transcription in cDNA by “High-Capacity cDNA Reverse Transcription kit” (Thermo Fisher Scientific, Waltham, MA, USA, Cat# 4368813). BrightGreen qPCR MasterMix (Applied Biological Materials, Canada) was used for quantitative PCR assays, according to the manufacturer’s instructions on a CFX Connect Detection System (Bio-Rad, Hercules, CA, USA). Specific primer pairs were designed using the Oligo 4.0 software (Table 1) as already reported in Fasciolo et al., 2022 [22]. Actb and B2m were selected as housekeeping genes and the melt curves have been analyzed for assessing the specificity of all reactions. Relative gene expression variation was analyzed calculating ∆Ct (i.e., the difference between the mean Ct of reference genes and the Ct of testing gene) and applying the 2−ΔΔCt method. ## 2.13. Data Analysis All biochemical and blotting analyses are shown as means ± SEM. All experiments were performed in technical duplicate or triplicate and data analysed using the one-way analysis of variance (ANOVA) method and Tukey’s pairwise comparison tests. The differences with probability values (p) ≤ 0.05 were considered significant. All analyses were performed using GraphPad Prism 8.0.2 (GraphPad Software Inc., La Jolla, CA, USA). For qPCR reactions, normal data distribution was assessed with the Shapiro–Wilk test (“shapiro.test function”, R language). The statistical significance of differences between control and testing samples or between two different groups (p value ≤ 0,05) was evaluated with a two-tailed (one sample and two samples, respectively) Student’s t test (GraphPad Software Inc., La Jolla, CA, USA). Differences were defined as significant at p value ≤ 0.05. ## 3.1. Body Parameters To test the efficacy of hormonal treatments, we first measured thyroid hormone levels (FT3 and FT4). Thyroid hormone administration significantly increased FT3 and decreased FT4 plasma levels, while Vit E did not affect them. FT3 values were 4.05 ± 0.37, 3.41 ± 0.32, 26.10 ± 2.03 and 24.46 ± 2.18 pM, in C, C + VE, H and H + VE, respectively; as expected, exogenous administrated T3 exerted a negative feedback on the hypothalamic–pituitary–thyroid axis. In fact, FT4 values were 19.44 ± 0.51, 19.5 ± 0.51, 2.57 ± 0.13 and 2.44 ± 0.26 pM in C, C + VE, H and H + VE, respectively. Vit E muscle content was increased both by thyroid hormone treatment and Vit E supplementation. In detail, the measured values were: 19.6 ± 0.65, 26.7 ± 1.45, 23.47 ± 0.50 and 31.9 ± 1.8 nmol/g tissue in C, C + VE, H and H + VE, respectively. The increased Vit E levels in H rats can be possibly due to the thyroid-hormone-induced mobilization of endogenous reserves or a higher assimilation of the vitamin from food [33]. ## 3.2. Glucose Tolerance Test and Insulin Levels To assess the onset of insulin resistance due to hyperthyroidism and the effect of Vit E treatments, we performed a glucose tolerance test on the different animal groups. After 6 h of fasting, corresponding to the baseline (time 0) of the glucose tolerance test, the T3 treatment increased the basal blood glucose and plasma insulin level compared to C. Vit E administration to T3-treated animals shifted back the glucose levels to values comparable to C while also reducing the basal insulin level, although the treatment did not fully restore them to the basal condition (Figure 1B,C, time 0). Following glucose i.p. administration, the increase in the glucose plasma level (Figure 1B) was significantly higher in H in respect to C, C + VE and H + VE rats after 15 and 30 min. The insulin plasma level (Figure 1C) was higher in H rats across all time points compared to the other groups. In H + VE, the increase in insulin level was blunted compared to that observed in H and remained higher than that observed in C and C + VE at time 0, 15 and 30 min. These data indicate that the administration of thyroid hormones induces insulin resistance, and that Vit E limits this effect, despite not completely restoring insulinemia to the basal condition. Moreover, to check if systemic insulin resistance could also be associated with skeletal muscle insulin resistance, we verified if in vitro exposure of muscle pieces to insulin was able to activate AKT (Figure 1A). No differences in AKT phosphorylation were observed among the groups in basal conditions. Indeed, insulin treatment increased AKT-P in all animal groups, despite its effect being significantly lower in H group vs. the C one. Notably, the administration of Vit E to hyperthyroid animals ameliorated skeletal muscle response to insulin, as evident by the increase in AKT-P/AKT in H + VE rats compared to H (Figure 1A). However, we noticed that the AKT-P/AKT ration in H + VE rats did not reach the ones observed in group C. ## 3.3. Oxidative Damage to Lipids and Proteins and Susceptibility to Oxidative Damage The assessment of oxidative stress levels was performed by measuring the levels of lipids (hydroperoxides, HP; Figure 2B,E) and protein (protein-bound carbonyls, CO; Figure 2A,D) as markers of oxidative damage to lipids and protein. Since mitochondria are considered crucial actors of insulin resistance [34], we measured lipid and protein oxidation both in the tissue (Figure 2, top panels) and isolated mitochondria (Figure 2, bottom panels). Furthermore, we determined the in vitro susceptibility to oxidative stress by evaluating the changes in HP levels after an oxidative insult (ΔHP) in both muscle homogenates and mitochondria (Figure 2C,F, respectively). Hyperthyroidism increased lipid and protein oxidative damage and the susceptibility to oxidative insult in vitro both in muscle homogenates and mitochondria. Interestingly, dietary Vit E supplementation reduced oxidative stress markers and susceptibility to oxidative stress when administered to both control and hyperthyroid rats. ## 3.4. Muscle Total ROS Content, Mitochondrial H2O2 Release and NADPH Oxidase (NOX) Activity Increased oxidative stress in the skeletal muscle may depend on higher ROS production in the muscle, which, in turn, may be due to an enhanced production from the main cellular sources (NOX and mitochondria). Thus, we measured the total ROS content, the NOX activity and the mitochondrial H2O2 release (Figure 3). The total ROS content (Figure 3A) was significantly increased by T3 treatment, whereas Vit E administration reduced it in both C and H rats. T3 treatment also increased NOX activity, and Vit E supplementation (Figure 3B) reduced NOX activity in both control and hyperthyroid rats. Mitochondrial ROS release was measured during basal (Figure 3C) and ADP-stimulated respiration (Figure 3D). The highest mitochondrial ROS release was observed in the H group, during basal and ADP-stimulated respiration. Vit E supplementation reduced ROS release during basal and ADP-stimulated respiration both when administered to C and H rats. Overall, these data suggest that increased oxidative damage occurs in hyperthyroidism and that Vit E supplementation can rescue this damage at least in part by reducing ROS production. ## 3.5. Antioxidant Enzyme Activity of Skeletal Muscle Homogenate To assess the contribution of muscle antioxidant defense to the oxidative damage we measured the activities of the antioxidant enzymes glutathione peroxidase (GPX), glutathione reductase (GR), catalase and superoxide dismutase (SOD) (Figure 4). The activities of all the antioxidant enzymes tested were increased in T3-treated animals. Vitamin E reduced the activity of the catalase in the C groups, and the activities of GPX, GR and catalase in H rats. ## 3.6. Oxygen Consumption of Skeletal Muscle Homogenate and Isolated Mitochondria To assess the effects of treatments on oxygen consumption in the muscle homogenate and mitochondria, we evaluated oxygen consumption in the presence of pyruvate and malate as respiratory substrates in the absence (basal respiration) or in the presence of ADP (ADP-stimulated respiration). We also measured the respiratory control ratio (RCR), an index of the coupling between electron transport chain flow and ATP synthesis as well as mitochondrial integrity. Thyroid hormone treatment increased homogenate and mitochondrial basal and ADP-stimulated respiration; the effect was observed in both H and H + VE groups vs. C (Figure 5A–E). Notably, Vit E significantly decreased homogenate basal respiration as can be deduced by comparing C + VE vs. C and H + VE vs. H (Figure 5A). No effect of Vit E on ADP-stimulated respiration was observed in homogenates (Figure 5B) In the mitochondria, Vit E decreased the basal respiration rate in C + VE and H + VE rats (Figure 5D). Concerning ADP-stimulated, respiration a slight ($8\%$) but significant reduction was observed (Figure 5E). Vit E increased the RCR in C + VE and H + VE homogenates (Figure 5C) and in the mitochondria of C + VE rats (Figure 5E). Thus, our data suggest that Vit E may counteract the increase in basal oxygen consumption due to thyroid hormone administration. ## 3.7. Factors Involved in Stress Response To verify if oxidative stress affects putative factors involved in mitochondrial biogenesis and cellular stress response, we evaluated the changes in the levels of the peroxisome proliferator-activated receptor-gamma coactivator 1 alpha (PGC1-α), nuclear respiratory factor-1 (NRF1), BIP, and in the phosphorylation levels of the eukaryotic translation initiation factor 2 alpha (EIF) and c-Jun N-terminal kinase (JNK) (Figure 6B). The phosphorylation level of EIF and JNK are reported as the ratio between the content of the phosphorylated and non-phosphorylated proteins (Figure 6B). As reported in Figure 6, thyroid hormone treatment induced an increase in the content of PGC1-α, NRF1 and BIP, as well as in the phosphorylation of EIF and JNK. When administered to T3-treated rats (H + VE), Vit E reduced NRF1 and BIP content and the phosphorylation of EIF and JNK. These data suggest a possible relationship between cellular stress and IR development in skeletal muscle. ## 3.8. Gene Expression Analysis of Slc2a1, Slc2a4, Pparg, Ppara, Cd36 and Il1b To identify putative metabolism-related genes perturbed by hyperthyroidism in the skeletal muscle and potentially modulated by Vit E, we analysed the expression of genes encoding glucose transporters (e.g., Slc2a1 and Slc2a4), genes involved in muscle lipid homeostasis and insulin signalling (e.g., Pparg, Ppara, Cd36), as well as Il1b, known for its proinflammatory activity related to insulin response. As shown in Figure 7, the expression analysis revealed that all analyzed genes, except Il1b which was upregulated, were significantly repressed in muscle of H (vs. C) rats (Figure 7). Notably, we disclosed that Vit E in H rats efficiently induced the expression of muscular glucose transporters Slc2a1 and Slc2a4 (Figure 7), indicating, overall, the beneficial effects of Vit E supplementation on glucose uptake in the skeletal muscle of diseased animals. Moreover, in T3-treated rats, the Vit E administration also counteracted the strong upregulation of Il1b expression (Figure 7) and the reduction in Pparg (Figure 7). ## 4. Discussion In the current work, we report that Vit E supplementation can attenuate hyperthyroidism-related changes in glycaemic and insulinemic response to a glucose load. In our previous article, we suggested that antioxidant treatment can partially prevent the development of hepatic IR in hyperthyroid rats [22], and in this paper, we extended the concept to skeletal muscle. Indeed, here, we observed that IR develops also in hyperthyroid muscle, as suggested by decreased AKT phosphorylation following insulin stimulation, in line with the observation that skeletal muscle plays a relevant role in the onset of whole-body IR [35] and support muscle involvement in hyperthyroidism-induced IR. Hyperthyroidism-induced IR is also associated with increased oxidative damage in skeletal muscle, evident in the increase in lipid hydroperoxides and protein carbonyls content. The evidence that the antioxidant treatment, obtained by Vit E administration to hyperthyroid rats, reduces oxidative stress markers and muscular and systemic IR points toward the involvement of oxidative stress in IR development. The enhancement of lipid peroxidation, observed in hyperthyroid rats, depends, at least in part, on the increase in fatty acid unsaturation degree [36] that makes them more prone to oxidation. Vit E reacts with lipid peroxyl radicals, thereby disrupting the propagation of lipid peroxidation chain reactions and contrasting the thyroid hormone effect. Our data also shed light on the cellular sites responsible for the increased ROS content, observed in hyperthyroid skeletal muscle, that underlines the enhancement in lipid and protein oxidative stress. Indeed, we showed that in hyperthyroid animals, both an increase in NOX activity and in mitochondrial ROS release take place. Concerning NOX, the muscle fibres mainly express two isoforms of the enzyme: NOX2, mainly located at the plasma membrane and transverse tubules [37] and NOX4, mainly located at the sarcoplasmic reticulum, transverse tubules, and inner mitochondrial membrane [38]. NOXs have an important role in muscle function [39], but their increased activity may contribute to disease progression in pathologic conditions, such as IR. Indeed, in this context, it has been reported that the administration of the NOX inhibitor apocynin to streptomycin-induced diabetic rats reduces fasting blood glucose and enhances insulin sensitivity [40]. Thus, the increase in NOX activity observed in hyperthyroid animals may contribute to the occurrence of IR. Our data also confirm that T3 treatment enhances mitochondrial ROS release [41]. This effect, plausibly, depends on changes in the mitochondrial biochemical composition induced by thyroid hormones, with an increase in the content of electron transporters, including those responsible for electron leakage and superoxide formation [42]. It has been suggested that at least three different pathways of target gene expression regulation contribute to thyroid hormone induced synthesis of mitochondrial components and mitochondrial biogenesis [43]. The first pathway starts upon T3 binding to nuclear receptors α and β (TRα1, TRα2 and TRβ) and consists of modulating gene transcription [44]. Upon T3 binding, the TRs act as homo- or heterodimers (when associated with retinoic acid receptors), bind to specific sites in the regulatory regions of target genes, named thyroid response elements (TRE), and recruit chromatin remodelling complexes, which modify histones and lead to interchange between the open (transcriptionally active) and closed (transcriptionally silenced) chromatin state [44]. In the skeletal muscle the TRα1 isoform is involved in the thyroid-hormone-induced induction of mitochondrial biogenesis [45]. In the second pathway, T3 has a direct effect on the mitochondrion via the binding to a mitochondrial-localised receptor. This receptor is an alternative translation product of the TRα gene (p43) and its overexpression increase mitochondrial biogenesis and protein synthesis [43]. The third pathway involves intermediate factors that are synthesized via thyroid TRE, enter the nucleus, and regulate a second series of thyroid hormone target genes. Among such intermediate factors are the transcription factors nuclear respiratory factor 1 and 2 (NRF1, NRF2) and the coactivators PGC-1α. These regulation mechanisms are additionally modulated by many non-genomic actions, e.g., post-translational modifications, or direct binding of thyroid hormone or its derivative to target structures [43]. The ability of Vit E to reduce ROS tissue levels is due to its capacity to reduce NOX activity and mitochondrial H2O2 release. The first effect may be due to the ability of Vit E to reduce the plasma membrane translocation of p47phox, the cytosolic component of the enzyme [46,47]. Concerning the Vit-E-induced reduction in mitochondrial H2O2 release, it may be due to the ability of the benzene ring to influence mitochondrial O2 and H2O2 generation by preventing electron loss and, thus, regulating superoxide production and/or by removing superoxide once formed. [ 48]. These actions reduce the likelihood of ROS formation and the triggering of peroxidation chain reactions [33]. In agreement with our previous data [49], we also report the increased activity of antioxidant enzymes (GPX, GR, Catalase and SOD) in hyperthyroid rats, that, however, is not sufficient to contrast the occurrence of oxidative damage. In general, it appears that the activity of antioxidant enzymes is related to the extent of oxidative stress. In line with this, our data show that the reduction in oxidative stress markers induced by Vit E is accompanied by a reduction in the antioxidant enzyme activity. A similar relationship has been found in the ageing brain, where the increase in lipid peroxidation is accompanied by an increase in the activities of antioxidant enzymes [50]. These observations are in line with the evidence that, despite the increased activity of antioxidant enzymes, the susceptibility of skeletal muscle to oxidative insults remains higher in hyperthyroid rats, and that the administration of Vit E slightly reduces it. Therefore, the hyperthyroid skeletal muscle is more prone to damage and, possibly, to functional alterations. It has been proposed that mitochondrial dysfunction and endoplasmic reticulum (ER) stress are key mechanisms in the emergence of IR in skeletal muscle [51]. Mitochondria are the primary target of the ROS they self-produce. Accordingly, our data show that the increased hyperthyroidism-induced mitochondrial ROS release results in higher oxidative damage to mitochondrial lipids and proteins, associated with increased susceptibility to stress. Despite the increase in signs of oxidative stress and the occurrence of IR in hyperthyroid rats, ADP-stimulated respiration increases in according to the well-known metabolic-stimulating effects of thyroid hormone. Mitochondrial respiration rate, when detected in the whole homogenate, depends on both specific mitochondrial functionality and tissue mitochondrial content. The administration of Vit E to hyperthyroid rats slightly reduced ADP-stimulated respiration when it was detected in isolated mitochondria but not when detected in whole tissue homogenates. This discrepancy could be more apparent than real since in our study we used mitochondria obtained at 3000× g, such fraction represents the most functional mitochondria that at the same time are the most ROS producer [52]. Thus, it is plausible that the slight effect of Vit E on ADP-stimulated respiration could be brought to light only when the measurement was performed on 3000× g-isolated mitochondria fraction, since all mitochondria populations contribute to respiration detected in the whole homogenate. Another possibility explaining the above-cited discrepancies is that Vit E, by reducing ROS, could interfere with a ROS-sensitive factor involved in mitochondrial biogenesis. In the whole our results appear in line with the observation that an increase in mitochondrial oxidants, independent of mitochondrial dysfunction, is sufficient to induce IR [53]. Mitochondrial oxygen consumption increases during basal respiration in the muscle of hyperthyroid rats and is reduced by antioxidant treatment. These changes may depend on variations in the inner mitochondrial membrane proton conductance, which are partly dependent on lipid peroxidation extent [54]. Increased ROS production and protein carbonylation can affect protein unfolding and upregulate the protein unfolding response [55]. Mitochondria and the endoplasmic reticulum are structurally and functionally connected [56,57,58,59] and this close interconnection implies that ROS generated in mitochondria promote ER stress [60]. GRP78/BiP is an important ER chaperone protein critical for ER protein quality control [61]. During oxidative stress, an integrated stress response (ISR), a stress-adaptive pathway, is activated, with EIF2α representing a crucial node [55]. EIF2α is activated by phosphorylation and plays a relevant role in mediating translation attenuation during mitochondrial dysfunction and ER stress aimed at reducing protein influx into the two organelles [55,62]. Increased EIF2α activation associated with increased protein levels of the ER stress marker GRP78 BIP is found in hyperthyroid muscle and suggests that increased IR correlates with the onset of ER stress. Indeed, Vit E supplementation reduces both ER and ISR stress and IR, confirming the important role of oxidative stress in the development of IR. Furthermore, increased ROS production affects transcription of NRF2 target genes, including respiratory NRF1, which regulates mitochondrial gene expression [63,64]. The activity of these transcription factors is coordinated by PGC1-α coactivator, a member of the PGC1 coactivator family [65]. It has also been suggested that NRF1 could be directly activated by redox signalling [61]. Accordingly, we found that the NRF1 content increases in hyperthyroid animals and is attenuated by antioxidant supplementation. We also measured the phosphorylation level of JNK, one of the most studied factors in IR obesity models [66]. The inhibition and total or partial deletion of JNK may reduce the occurrence of IR in non-alcoholic fatty liver disease induced by a high-fat diet [67]. Indeed, activated JNK phosphorylates the insulin receptor [68] and the insulin receptor substrate (IRS) [69,70] on serine and threonine residues. In this way, it inhibits the physiological pathway of insulin through steric hindrance. We found that in hyperthyroid rats, JNK phosphorylation is significantly increased compared to control animals and is attenuated by antioxidant supplementation. Thus, our results agree with the observation that endogenous ROS act as potent inducers of JNK [71], confirming the correlation between increased oxidative stress and JNK phosphorylation. To define if, and to what extent, Vit E perturbs the expression levels of crucial genes involved in metabolic processes/pathways relevant for skeletal muscle functionality in hyperthyroid rats, we focused on specific markers of glucose transport (e.g., Slc2a1 and Slc2a4), glucose/lipid homeostasis, insulin signalling (e.g., Ppara, Pparg, and Cd36) and pro-inflammatory response (e.g., Il1b). All the above-mentioned markers were detectable at reasonable levels for a bona fide analysis. In hyperthyroid muscle, all the genes studied were repressed except for Il1b which was upregulated, in line with the reduced insulin sensitivity of T3-induced hyperthyroid rats [22]. We also disclosed that Vit E in hyperthyroid rats efficiently induced the expression of muscular Slc2a1 and Slc2a4, overall suggesting the beneficial effects of Vit E supplementation on glucose uptake in the skeletal muscle of diseased animals. Moreover, in T3-treated rats, the Vit E administration also counteracted the strong upregulation of Il1b expression and the reduction in Pparg. In line with the anti-inflammatory activity of the transcription factor Pparγ and its capacity to induce multiple genes involved in glucose and lipid uptake/transport [72,73], the Vit-E-mediated induction of Pparg in hyperthyroid rats is mirrored by the increase in its target lipid transporter Cd36, and also may contribute, at least in part, to the Vit-E-mediated induction of glucose transporters in hyperthyroid rats. Finally, whereas we recently demonstrated the beneficial effect of Vit E supplementation on liver Ppara [22], we did not confirm this effect in the skeletal muscle, possibly due to the most relevant role of this nuclear receptor being in liver-associated processes [74]. ## 5. Conclusions In conclusion, we demonstrate that skeletal muscle plays a relevant role in IR in hyperthyroidism. Moreover, we suggest that oxidative stress is crucial for IR development in the hyperthyroid condition. 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--- title: Antioxidant Enzyme Activity and Serum HSP70 Concentrations in Relation to Insulin Resistance and Lipid Profile in Lean and Overweight Young Men authors: - Anna Lubkowska - Wioleta Dudzińska - Waldemar Pluta journal: Antioxidants year: 2023 pmcid: PMC10044875 doi: 10.3390/antiox12030655 license: CC BY 4.0 --- # Antioxidant Enzyme Activity and Serum HSP70 Concentrations in Relation to Insulin Resistance and Lipid Profile in Lean and Overweight Young Men ## Abstract Oxidants are generated by all cells during normal oxidative respiration, and as long as they are under the control of appropriate mechanisms, they act as intracellular signaling molecules participating in complex functions. Oxidative stress can also affect insulin levels in the body. The production of reactive oxygen species by-products can lead to insulin resistance. Heat shock proteins (70 kDa) protect cells from the damaging effects of heat shock but also oxidative stress. The aim of the study was to investigate the serum concentration of HSP70 in young, non-obese but overweight men (BMI ≤ 30 kg/m2) and to assess its association with the insulin resistance, lipid profile and antioxidant system of red blood cells. Fifty-seven young men were examined and divided into two groups: lean men ($$n = 30$$) and men overweight ($$n = 27$$). A statistically significant difference was observed in the BMI ($p \leq 0.007$), HSP70 concentration ($p \leq 0.000$), serum insulin concentration ($p \leq 0.000$), HOMA-IR ($p \leq 0.0001$), superoxide dismutase ($p \leq 0.02$) and glutathione peroxidase ($p \leq 0.05$) between the studied groups. There was a negative correlation between the concentration of HSP70 with the insulin level (r = −0.50; $p \leq 0.0004$) and with the HOMA-IR (r = −0.50; $p \leq 0.0004$). These changes were associated with an increase in the activity of antioxidant enzymes. Our findings suggest that measuring the extracellular concentration of HSP70 can be an important indicator in disorders of glucose homeostasis. ## 1. Introduction Insulin resistance associated with chronic inflammatory conditions result in increased oxidative stress, modification of cellular proteins and a reduced cellular defense system [1]. Oxidative stress is defined as an imbalance in the redox status due to the excessive production of reactive oxygen species (ROS), which include both the free radicals and their non-radical intermediates. The first of these are defined as forms containing one or more unpaired electrons. This incomplete electron shell confers their high reactivity. Although free radicals can be produced from many elements, the most important in the biological system are those formed with the participation of oxygen and nitrogen. The most common free radical under physiological conditions is the superoxide anion (O2•−), generated mainly by mitochondria. Superoxide detoxification requires the superoxide dismutase enzyme (SOD), which converts O2•− to hydrogen peroxide (H2O2). This one, in turn, is detoxified into water by catalase (CAT) and glutathione peroxidase (GPx). Important in antioxidant enzyme activity is cooperation, because an imbalance in the O2•− and H2O2 concentrations can result in the formation of superoxide anion hydroxyl ion (OH•), which is much more dangerous [2]. Oxidants are generated by all cells during normal oxidative respiration. Under strict regulation, ROS act as intracellular signaling molecules, contributing to complex functions such as blood pressure regulation, cognition and the immune response [3]. On the other hand, their excess disrupts many important cellular processes and functions, including cell proliferation and differentiation, inflammation or fatty acid peroxidation. Moreover, they can cause oxidative damage to DNA, proteins and membrane lipids. Persistent oxidative stress can lead to health disorders, including diabetes, neurogenerative syndromes and even cancer [4]. The relationship between oxidative stress and insulin resistance has long been recognized. Research in this area has shown a strong correlation between the body’s oxidative stress level and the presence of insulin resistance. The impairment of insulin sensitivity may be caused by hyperglycemia and hyperlipidemia-induced ROS or the production of ROS by-products. As a result, structural and functional changes of the insulin molecule occur, ultimately leading to a reduction in its bioactivity and activation of redox-sensitive cell signaling pathways, which interfere with insulin, signaling and the transport of glucose cells [3]. Heat shock proteins 70 kDa (HSP70) are the most ubiquitous molecular chaperones in the human body, which are located in all the cellular compartments. The scope of activities of HSP70 is very broad and covers the folding of both nascent and misfolded proteins, protein assembly, the regulation of their activity and the degradation of and prevention from dismantling protein aggregates [5]. In this way, HSP70 protects cells not only from the damaging effects of heat shock but also oxidative stress (OS) [6]. In addition, HSP70 are very good therapeutic target for viral infections, as we have described in our previous work [7]. This multifaceted action makes HSP70 an interesting research target. Experimental evidence shows that there is a correlation between HSP70 and the redox status. On the one hand, both oxidative stress and antioxidants appear to regulate HSP70 expression [8,9]. On the other hand, reducing the HSP70 expression may increase reactive oxygen species production and the oxidation of mitochondrial proteins [10]. Moreover, HSP70 reduces cellular damage caused by oxidative stress in kidney cells [11] and increases the activity of glutathione peroxidase and glutathione reductase (GR) in response to hypoxic and ischemia [12]. Data on the concentration of HSP70 in lean and overweight Caucasian subjects and its possible association with the insulin resistance, lipid profile and antioxidant system of red blood cells are insufficient. Against this background, our aim was to investigate the serum concentration of HSP70 in young, non-obese men (body mass index (BMI) ≤ 30 kg/m2) and to assess its association with the insulin resistance, lipid profile and antioxidant system of red blood cells. ## 2.1. Participants The study involved 57 healthy men, without diabetes, aged 22 to 26 years and taking no medications. All subjects were students of the General Tadeusz Kościuszko Military Academy of Land Forces. Volunteers were divided into two groups: lean and non-obese but overweight, according to their body mass index (BMI). According to the WHO criteria for Western populations, a slim person is defined by a BMI of 18.5–24.9 kg/m2, and a person overweight by a BMI of 25–29.9 kg/m2. The groups were homogeneous in terms of diet and daily physical activity, due to the scheduled joint activities, accommodations and food provided by the Military Academy. The exclusion criteria for participation in the study was: metabolic diseases, diabetes, hypertension, and BMI < 18.5 or ≥30 kg/m2. According to the Declaration of Helsinki, each participant gave written consent before participating in the study. The local ethics committee approved the study (Pomeranian Medical University; Ref. KB-$\frac{0012}{54}$/10). ## 2.2.1. Biochemical Parameters of Venous Blood Venous blood was drawn by qualified medical personnel from each of the volunteers after overnight fasting between 6.00 a.m. and 8.00 a.m. after a 10 min rest in a sitting position from the antecubital vein using Vacutainer tubes (Sarstedt, Nümbrecht, Germany) and separated into two tubes: one for biochemical analysis of the serum (4.9 mL) and the other to determine the blood counts (1.2 mL anticoagulated with 1 g/L K2 EDTA). The HSP-70 protein levels were determined using commercially available enzyme-linked immunosorbent assay (ELISA) kits (EIAab, Wuhan, China). The standard curve had a range of 0.2–10 ng/mL, the intra-assay: <$8\%$ and inter-assay: <$10\%$. The serum levels of apolipoprotein A (ApoA) and apolipoprotein B (ApoB) were determined using commercially available ELISA kits (EIAab, Wuhan, China), according to the instructions given by the manufacturer. The detection limits for the lipoprotein tests were 0.8 and 892.9 µmol/L, the intra-assay: <$8\%$ and inter-assay: <$10\%$. The serum level of interleukin 3 (Il-3) was determined using commercially available ELISA kits from R&D Systems (Abingdon, UK), according to the instructions given by the manufacturer. The detection limits for the interleukin 3 test were 31.2–2000 pg/mL, the intra-assay: <$8\%$ and inter-assay: <$10\%$. The insulin levels were determined by the ELISA method using reagent kits (DRG Medtek, Warsaw, Poland). The detection limit for insulin was 1 mU/L. The insulin resistance IR (HOMA-IR) was calculated according to the formula: fasting plasma glucose (mmol/L) × fasting plasma glucose (mmol/L)/22.5. To determine the extracellular hemoglobin, haptoglobin, total bilirubin, total protein, albumin, uric acid, glucose, total cholesterol, HDL cholesterol and triglycerides, a spectrophotometric method was used. The concentration of the LDL cholesterol fraction was determined by a direct method. After the determinations, the obtained lipid profile was supplemented by the calculation of the TG:TCh, TCh:HDL and LDL:HDL ratios. The GSHtotal, GSHreduced, GSSG and GST concentrations in the hemolysate samples were determined by the colorimetric method (OxisResearch, Portland, OR, USA). SOD, CAT, GPx and GSSG-R activity were also measured with a BIOXYTECHH kit (OxisResearch, Portland, OR, USA) using a UV/VIS Lambda 40 (Perkin-Elmer, Wellesley, MA, USA) spectrophotometer. SOD: sensitivity: 0.1 U/mL, specificity: $97\%$ and coefficient of variation: lower than $4\%$; CAT: sensitivity: 1.71 U/mL, specificity: $89\%$ and coefficient of variation: lower than $2\%$; GPx: sensitivity: 6 U/L, specificity: $94\%$ and coefficient of variation: lower than $4\%$, GSSG-R: sensivity:0.14 U/L, specificity: $94\%$ and coefficient of variation: lower than $4\%$ and GSH/GSSG: sensitivity: 5 mmol/L, specificity: $95\%$ and coefficient of variation: lower than $2\%$. The enzyme activity and glutathione concentration were calculated per 1 g of erythrocyte hemoglobin. After the determinations, the GSH:GSSG ratio was calculated. ## 2.2.2. Statistical Analysis To determine the sample size necessary to assure appropriate statistical precision, the GPower 3.1.9.4 program was used. The established effect size was 0.8, α error probability was 0.05 and power was 0.8. The non-central parameter δ was 2.88. The total sample size was 52 and actual power 0.807. The obtained results were statistically analyzed using MS Excel and Statistica 13.3 software (Statistica PL, StatSoft, Kraków, Poland). The normality of the distribution was checked using the Shapiro-Wilk test. The test results showed that the distributions of the examined values deviated from the normal distribution; therefore, in detailed statistical analyses, non-parametric tests were used. The results were presented as the middle value of the distribution—median, lower quartile value (Q25) and upper quartile value (Q75). In order to demonstrate the significance of the differences, the non-parametric ANOVA Kruskal–Wallis rank test and the Mann–Whitney U test were used. The significance level was assumed at $p \leq 0.05.$ To prove whether the observed correlations were statistically significant, the Spearman’s rank correlation coefficient significance test was used. ## 3.1. Anthropometrical and Biochemical Parameters in the Study Groups According to the WHO guidelines, the participants were divided into two groups: lean men ($$n = 30$$) and men overweight ($$n = 27$$). A statistically significant ($p \leq 0.007$) difference was observed in the BMI between the studied groups. Highly statistically significant ($p \leq 0.000$) differences were demonstrated in HSP70 concentrations (median 0.8 ng/mL for the lean group and 0.4 ng/mL for the group overweight) and in the serum insulin concentrations (6.7 mU/mL for the lean group and 10.0 mU/mL for the group overweight). Moreover, a difference was observed in the HOMA-IR, which was significantly higher in men overweight (median 1.3 mM for the lean group and 2.0 mM for the group overweight, $p \leq 0.0001$) (Figure 1, Figure 2 and Figure 3). No differences were found for the other biochemical parameters. These results are summarized in Table 1. ## 3.2. Erythrocyte Antioxidant Enzyme Activity and Glutathione Concentration in Study Groups The analysis of antioxidant enzyme activity showed a statistically significant difference between the concentration of SOD (median 762 U/gHb for the lean group and 872 U/gHb for the group overweight, $p \leq 0.02$) and GPX (median 6.7 U/gHb for the lean group and 8.3 U/gHb for the group overweight, $p \leq 0.05$). No differences were found for other antioxidant enzyme activity. These results are summarized in Table 2. ## 3.3. Relationships between HSP70 Level and Concentration of Insulin Analyzing the relationships between HSP70 level and concentration of insulin showed that is a negative correlation with the insulin level (r = −0.50; $p \leq 0.0004$) and the HOMA-IR (r = −0.50; $p \leq 0.0004$) (Figure 4 and Figure 5). ## 4. Discussion The conducted research was aimed at investigating the serum concentration of HSP70 in young, non-obese men (BMI ≤ 30 kg/m2) and to assess its association with the insulin resistance, lipid profile and antioxidant system of red blood cells. In our study, we observed a negative correlation between the HSP70 level and concentration of insulin (r = −0.50; $p \leq 0.0004$). The HSP70 concentration was higher in the lean group (median 0.8 ng/mL) compared to the group overweight (median 0.4 ng/mL) at the same time with a lower insulin concentration (median 6.7 vs. 10.0 mU/mL). This may prove the protective role of HSP70 against insulin resistance. The analysis of the activity of antioxidant enzymes showed a higher concentration of SOD in the group overweight (median 872 vs. 762 U/gHb) and GPX (8.3 vs. 6.7 U/gHb). We have investigated the possible link among erythrocyte antioxidant enzyme activity (SOD, CAT, GPX, GST and GSSG-R); GSHtotal; GSHreduced and GSSG concentrations with a GSH:GSSG ratio and the level of HSP70 in serum in two groups of young, physically active men, with differences in the BMIs between the groups (lean vs. overweight). According to our results, alterations of the antioxidant enzymes related to body mass are not uniform. The interesting finding from the present study is the lower erythrocyte activity of SOD and GPX, with an accompanying higher concentration of serum HSP70 in the group of men with a lower BMI (lean group). The human body’s defense mechanisms against oxidative stress are complex and involve cellular and extracellular antioxidant systems regulated at multiple levels. The cellular defense against ROS generated during oxidative metabolism utilizes antioxidant enzymes [13], including superoxide dismutase, catalase and glutathione peroxidase. SOD catalyzes the dismutation of superoxide anion (O2−) to hydrogen peroxide (H2O2) in the first step of the defense mechanism, which is followed by CAT and GPX1 independently converting H2O2 to water. An increase in the SOD catalytic activity produces an excess of H2O2 that must be efficiently neutralized by either CAT or GPX1 (using GSH as a thiol donor); otherwise, H2O2 reacts with O2− produced in the Haber–Weiss reaction hydroxyl radical OH, which is more dangerous [14]. The physiological role of GPXs is mediated by its involvement in the redox regulation of cellular functions. Our results regarding higher SOD and GPx activity in overweight men are consistent with some of the literature data. However, it should be noted that the results of studies on the activity of SOD and GPX, depending on the nutritional status, are, in some cases, convergent but not in others. Vávrová et al. showed altered erythrocyte activities of antioxidant enzymes in patients with metabolic syndrome (MetS) with central obesity who had higher activities of SOD and GR than in healthy subjects, though the activity of GPX1 was not significantly changed [15]. Karaouzene et al. [ 2011] demonstrated that the SOD levels were differentially associated with obesity in young and old obese subjects. The response to oxidant damage differs according to age, because the maturation of antioxidant enzymes is related to aging [16]. Erdeve et al. evaluated the antioxidative Cu/Zn-SOD response to obesity-related stress in obese children and stated that a high-caloric diet may induce mitochondrial oxidative metabolism and cause electron leakage from a mitochondrial respiratory chain in the obese, which leads to an increase in the SOD level [17]. The altered expression and activity of the glutathione peroxidases have been observed along with obesity in human and animal studies. Mice with the elevated expression of a major antioxidant selenoprotein (GPX1) showed hyperglycemia, hyperinsulinemia, elevated body fat accretion and plasma leptin and reduced insulin sensitivity. It is postulated that the most plausible mechanism could be the effect of GPX1 overexpression on the intracellular H2O2 tone. Normal or minimal levels of intracellular ROS or H2O2 are required for sensitizing insulin signaling. The overexpression of GPX1 may accelerate the quenching of the intracellular H2O2 burst after insulin stimulation, resulting in less inhibition of the protein–tyrosine phosphatase activity and, subsequently, attenuated phosphorylation of the insulin receptor [18]. Rupérez et al. found that GPX activity was found to be positively and significantly correlated with blood pressure, adipocyte fatty acid-binding protein and high-sensitivity C-reactive protein. Moreover, the GPX variant GPX1-7 genes: rs757228, rs8103188, rs445870 and rs406113 were associated with prepubertal childhood obesity [19]. Chen et al. observed a significantly positive association between increases in erythrocyte GPX1 activity and levels of insulin resistance in normal pregnant women [20]. The erythrocyte and serum GPX activity were appropriately $26\%$ and $22\%$ higher in obese Brazilian women and the Central Mexican population compared to the controls, being associated with insulin sensitivity and the atherogenicity index. GPX activity was characterized by a considerable increase in obese and centrally obese diabetic subjects in parallel with oxidative stress markers [21,22,23]. The cellular response to oxidant damage may differ from cell to cell because of different quantities and activities of antioxidant enzymes. The antioxidant enzyme levels increase to prevent the destruction of tissues in a state of oxidant damage and also during compensatory adaptation to oxidative stress during the development of obesity [24]. Although, in our study, no association was found between the components of the antioxidant response and serum HSP70 level, it should be noted that significantly lower values for the 70 kDa heat shock proteins were recorded in the overweight group of men. We decided to analyze the serum HSP70 level according to the BMI and antioxidant activity, due to the fact that the studies carried out so far have shown a wide range of extracellular HSP70 activity both in triggering the signaling of the proinflammatory cascade and in blocking it (in the case of excessive activation of the immune system) [25,26,27]. Extracellular HSP70 is still a topic of interest for researchers who are looking for relationships between the protein concentration and the occurrence of diseases, inflammation and pathology, as well as in relation to the nutritional status and the aging process. Martínez de Toda and De la* Fuente concluded that HSP70 plays a key protective role in the cell aging process. In addition, it can be a biomarker of the rate of aging and the lifespan [28]. The response of cells to stress involves the induction of the synthesis of HSP. A variety of cell types can release HSP70. It is suggested chaperone secretion takes place both in functioning and dying cells, and its impact involves various receptors [29]. The release of HSP70 from dying cells can be a signal of danger, while secretion from living cells is a signal of a proper response to stress. Extracellular HSP70, by inducing the release of proposal cytokines (e.g., TNFα, IL-6, IL-1b or Toll-like 2 receptors), can stimulate the HPA axis. This results in the increased secretion of both glucocorticoids and other adrenal steroids—powerful anti-inflammatory agents. Remarkably, glucocorticoids, at certain levels, can lead to an increase in IL-6 production. Therefore, it can be argued that the interactions of HSP70–glucocorticoid–IL-10 may be an important element of the anti-inflammatory mechanism [30]. Our results suggest that this mechanism may be less effective in people overweight. Chronic inflammation associated with overweightness and obesity can be compared to the oxidative–inflammatory theory of aging proposed by Fuente and Miquel [31]. They assumed that the chronic state of oxidative and inflammatory stress is the cause of age-related changes—in particular, those affecting the nervous, endocrine and immune systems. In the literature, you can find information on the decrease in HSP70 concentration with increasing age [32,33]. According to our results, Islam et al. demonstrated that the HSP70 concentration is inversely correlated with the BMI, percentage body fat, waist circumference and insulin resistance [34]. The proposed reason for the lower serum HSP70 levels in young men overweight could be a compromised expression of specific heat shock proteins such as HSP70, impaired synthesis of intracellular HSP and/or heat shock factor 1 (HSF-1)-dependent induction. It has been demonstrated that reduced activity of the anti-inflammatory HSP70 pathway correlates with nonalcoholic fatty liver disease (NAFLD) progression and with markers of oxidative stress in the obese patient, paralleled by similar reductions in HSF1 and insulin resistance [35]. On the other hand, HSP70 can be released from cells by an active mechanism that is independent of de novo HSP70 synthesis or cell death. Assuming the same amount of intracellular HSP70 synthesis, the active release mechanism by which HSP70 enters the circulation could be defective. The mechanisms for the secretion of HSP70 are complex and incompletely understood. HSP70 release involves transit through an endolysosomal compartment and is inhibited by lysosomotropic compounds. Moreover, the rate of HSP70 secretion correlates with the appearance of the lysosomal marker LAMP1 (lysosome-associated membrane proteins) on the cell surface, further suggesting the role of endolysosomes in the extracellular ATP regulatory role [36]. Recent findings demonstrated that the disruption of lipid rafts on a cell membrane abrogates the release of HSP70 from living cells [37]. Our study also showed that the serum concentration of HSP70 in men overweight was significantly lower than in those with normal body weight and that the decrease in HSP70 concentration was accompanied by an increase in insulin resistance. It is widely known that, in obesity, chronic low-grade inflammation and a disturbed balance between oxidative stress and the antioxidant defense system are significant in the inhibition of the insulin receptor signaling cascade and strongly associated with insulin resistance and type 2 diabetes [38]. There is also a known relationship between impairment of the heat shock response and diabetes and insulin resistance. In patients with type 2 diabetes, decreased HSP72 gene expression in muscles is correlated with decreased tissue insulin sensitivity [39,40]. These observations were also confirmed in cell and animal models [41,42]. A decreased expression of intracellular HSP was also linked with the metabolic syndrome, which is known to be proceeded by insulin resistance [43]. On the other hand, the authors of other studies showed that HSP72 expression in the subcutaneous adipose tissue in a diabetic obese group was reduced compared to nondiabetic obese subjects, whereas, in nondiabetic obese subjects, a significantly higher expression of this protein was observed compared to lean subjects [44]. Therefore, it seems that obesity without diabetes may trigger an increase in HSP expression in adipose tissue. On the contrary, Di Naso et al. [ 35] observed decreased levels of HSF1 / HSP70 in the liver and adipose tissue of obese patients in the course of NAFLD. Additionally, the serum concentration of HSP70 in obese patients with NAFLD turned out to be significantly lower compared to the non-obese control group [45]. These divergent results may be related both to different clinical profiles of patients and to different criteria for their inclusion, which makes the comparison difficult and imprecise. Despite some experimental studies on intracellular HSP70 expression, there is very little data on measuring the serum HSP70 concentration and determining its association with insulin resistance. While the intracellular HSP levels are lowered in diabetes and correlated with insulin resistance, in many studies, the levels of extracellular HSP72 (serum/plasma) are elevated in type 1 and type 2 diabetes [46,47,48] and correlated with oxidative damage and stress [47], disease duration [46] and with the CRP levels, monocytes and TNF-α [49,50]. It has also been reported that the serum HSP72 levels are elevated in women with long-term diabetes compared to men and do not decrease after hypoglycaemic therapy in women with newly diagnosed diabetes, but they do decrease in men [46]. In one study on patients with type 1 diabetes, an increase in HSP72 was observed in diabetic ketoacidosis, which was significantly reduced after treatment [47]. In contrast, another study showed immeasurable levels of HSP70 in the serum of patients with type 1 diabetes, with and without microvascular complications [51]. Thus, again, the discrepancy in the results makes the underlying mechanisms for altering the extracellular concentration of HSP70 remain inconclusive. While it is still unclear how and from where HSP70 is released into the circulation, our study shows that the serum levels of HSP70 in men overweight without metabolic disease are significantly lower compared to men with a normal body weight and that there is a decrease in the HSP70 levels accompanied by an increase in insulin resistance. Our results are clearly supported by studies carried out in a group of healthy African American men and women [34], in which an increase in the BMI, percentage of adipose tissue, waist circumference and insulin resistance were accompanied by a significantly reduced concentration of HSP70 in the blood serum, which is correlated with insulin resistance. Thus, our findings are consistent with the hypothesis that insulin resistance may contribute to the reduction of HSP70 levels [52,53]. Significantly lower HSP72 protein expression in skeletal muscle was associated with increased obesity and decreased insulin sensitivity in healthy subjects. The relationship between HSP72 protein expression and insulin sensitivity is explained by adiposity [54]. This hypothesis is consistent with the rodent data for which heat treatment and overexpression of HSP72 have been shown to protect against high-fat diet-induced insulin resistance. Heat treatment resulted in the decreased activation of Jun NH2-terminal kinase (JNK) and inhibitor of κB kinase (IKK-β), stress kinases implicated in insulin resistance and upregulation of HSP72 and HSP25, proteins previously shown to inhibit JNK and IKK-β activation, respectively [55]. Similarly, the induction of HSP72 and HSP27 by heat in human monocytes of obese individuals resulted in the dampening of IKK-β and JNK stress kinase activation and improved insulin signaling [56]. Although we only assessed statistical differences in the circulating HSP70 levels for overweight and insulin resistance, our findings are scientifically supported and warrant further investigation of possible mechanisms. The main limitation of this study is its cross-sectional nature, which makes it impossible to determine the direction of causality. Prospective studies are needed to confirm and improve the current results. Second, we had no data on participants’ eating and physical activity habits that could influence insulin resistance, overweight/obesity and the HSP70 protein. ## 5. Conclusions To our knowledge, this study is the first to show a clear negative correlation between the insulin resistance and serum HSP70 concentration levels in men overweight. These changes were associated with an increase in the activity of antioxidant enzymes. These observations are noteworthy, because disturbances in the glucose homeostasis (decreased sensitivity of peripheral tissues to insulin) are an important predisposing factor for the development of many metabolic diseases in the future, including type 2 diabetes, metabolic syndrome and polycystic ovary syndrome. Hence, our findings suggest that measuring the extracellular concentration of HSP70 (which is relatively easy to assess) can be an important indicator under such conditions. 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--- title: 'A Sex-Specific Comparative Analysis of Oxidative Stress Biomarkers Predicting the Risk of Cardiovascular Events and All-Cause Mortality in the General Population: A Prospective Cohort Study' authors: - Martin F. Bourgonje - Amaal E. Abdulle - Lyanne M. Kieneker - Sacha la Bastide-van Gemert - Stephan J. L. Bakker - Ron T. Gansevoort - Sanne J. Gordijn - Harry van Goor - Arno R. Bourgonje journal: Antioxidants year: 2023 pmcid: PMC10044882 doi: 10.3390/antiox12030690 license: CC BY 4.0 --- # A Sex-Specific Comparative Analysis of Oxidative Stress Biomarkers Predicting the Risk of Cardiovascular Events and All-Cause Mortality in the General Population: A Prospective Cohort Study ## Abstract Oxidative stress plays a pivotal role in cardiovascular (CV) disease, but current biomarkers used to predict CV events are still insufficient. In this study, we comparatively assessed the utility of redox-related biomarkers in predicting the risk of CV events and all-cause mortality in male and female subjects from the general population. Subjects ($$n = 5955$$) of the Prevention of REnal and Vascular ENd-stage Disease (PREVEND) population-based cohort study were included. Blood homocysteine, gamma-GT, HDL cholesterol, bilirubin and protein-adjusted free thiol (R-SH, sulfhydryl groups) levels were quantified at baseline and were prospectively analyzed in association with the risk of CV events and all-cause mortality. After adjustment for potentially confounding factors, protein-adjusted R-SH and homocysteine levels were significantly associated with the risk of CV events in men (HR 0.63 [0.40–0.99], $$p \leq 0.045$$ and HR 1.58 [1.20–2.08], $$p \leq 0.001$$, respectively). Protein-adjusted R-SH and HDL cholesterol levels were significantly associated with the risk of all-cause mortality in men (HR 0.52 [0.32–0.85], $$p \leq 0.009$$ and HR 0.90 [0.85–0.94], $p \leq 0.001$, respectively), while the same was observed for bilirubin and homocysteine levels in women (HR 0.68 [0.48–0.98], $$p \leq 0.040$$ and HR 2.30 [1.14–3.76], $p \leq 0.001$, respectively). Lower levels of protein-adjusted R-SH were robustly associated with an increased risk of CV events and all-cause mortality in men. Our results highlight the value of R-SH levels in cardiovascular risk assessment and their potential significance as being amenable to therapeutic intervention, while reaffirming the importance of other oxidative stress-related biomarkers, such as homocysteine, HDL cholesterol and bilirubin. ## 1. Introduction Oxidative stress plays an important role in the pathogenesis of many conditions, such as aging, cardiovascular disease, diabetes, and metabolic-associated fatty liver disease [1]. Understanding the role of oxidative stress in these diseases could be paramount to the detection, treatment and prevention of disease. It is defined as an imbalance between oxidants and antioxidants in favor of the oxidants, leading to a disruption of redox signaling and control, and/or molecular damage [2]. Reactive oxygen species (ROS) play a pivotal role in the response to hypoxia, inflammation, and various physiological systems, such as the regulation of immunity, differentiation, longevity, and autophagy of cells [3]. Oxidative stress is a key effector mechanism in the pathophysiology of numerous inflammatory and hypoxic conditions, and is closely associated with systemic inflammation, which can culminate into oxidative damage across all levels of biological organization [4]. It has been reported to play a role in several risk factors of cardiovascular disease, such as hypertension, obesity, diabetes, metabolic syndrome, dyslipidemia, and peripheral arterial disease [5]. Various plasma and serum markers of oxidative stress have been studied in health and disease, in order to determine their relation to disease and disease severity and to examine their predictive value. Among these are serum free thiol groups (sulfhydryl groups, R-SH), which are considered to be representative biomarkers of systemic oxidative stress [6]. Free thiols play a pivotal role in extracellular antioxidant systems, and possess potent antioxidant activity [7]. Reduced levels of serum free thiols arise from rapid oxidation by high amounts of ROS, and can be indicative of an unfavorable redox status, whereas higher concentrations of serum free thiols are indicative of a more favorable redox status. Systemic redox status has been shown to be associated with disease severity, and is a predictor of clinical outcomes in multiple conditions [8,9,10,11]. Homocysteine, a homologue of the amino acid cysteine, is also considered to be a representative systemic biomarker for oxidative stress [12]. High levels of homocysteine are especially considered to comprise an important risk factor for cardiovascular disease [13]. High-density-lipoprotein (HDL) cholesterol has been shown to strongly and inversely correlate with cardiovascular disease, and is known for its anti-inflammatory, anti-thrombotic, and anti-oxidative properties [14,15]. Bilirubin, a product of heme catabolism, is widely accepted as a biochemical indicator for the diagnosis of blood system diseases, as well as liver and bile diseases. In addition to this, bilirubin is considered to be a powerful antioxidant, and forms an important component of total antioxidant capacity in addition to free thiol groups [16]. While bilirubin is also known to exert cytotoxic effects in higher concentrations, concentrations in the physiological range only appear to exert antioxidant effects [17]. Gamma-glutamyl transferase (γ-GT) is an enzyme that catalyzes the breakdown of (among other molecules) glutathione, an important antioxidant molecule. Elevated levels of γ-GT have been linked to an increased risk of various diseases, such as diabetes and cardiovascular disease [18,19]. All these systemic biomarkers of oxidative stress have not only been shown to be cardinal in various disease conditions, but have also been shown to have value as predictors of health outcomes in the general population [20]. Currently it is unknown as to which of these biomarkers of oxidative stress is associated most strongly with the risk of CV events and all-cause mortality. Additionally, potential sex-specific associations between oxidative stress biomarkers and the risk of adverse health outcomes have not been properly investigated yet. Cardiovascular disease and all-cause mortality can vary between men and women in incidence, prevalence, etiology and morbidity, and to understand the underlying pathophysiological mechanisms, it is important to study potential sex-specific associations, as well as any potential combined associations [21,22,23]. In the present study, we aimed to determine as to which of the aforementioned systemic biomarkers, alone or in combination, associates best with the risk of cardiovascular events and all-cause mortality, in both male and female individuals from the general population. Therefore, we aimed to comparatively assess the utility of these five potential biomarkers in predicting the sex-specific risk of cardiovascular events and all-cause mortality. ## 2.1. Study Population The PREVEND (Prevention of REnal and Vascular ENd-stage Disease) study is a large-scale, prospective cohort study based in the city of Groningen, the Netherlands [24]. It was initiated in 1997 to investigate the relationship between albuminuria and the occurrence of renal and cardiovascular diseases. It collected data on a large number of variables from individuals living in Groningen who were between the ages of 28 and 75 years. A total of 85,421 people filled in a questionnaire and collected a urine sample, and a total of 40,856 subjects ($47.8\%$) completed both. Of these, participants with urinary albumin concentrations (UAC) > 10 mg/L ($$n = 7786$$) and a randomly selected control group with UAC < 10 mg/L ($$n = 3395$$) were invited to participate in subsequent study investigations at the research clinic of the University Medical Center Groningen (UMCG). The questionnaire featured data on demographic variables, history of cardiovascular disease, pregnancy history, and medication usage. This second screening program was completed by 8592 participants ($$n = 6000$$ with UAC > 10 mg/L and $$n = 2592$$ with UAC < 10 mg/L), which together formed the full PREVEND study cohort. Participants that were excluded were subjects who were pregnant, had Type 1 diabetes, or who had insulin-treated Type 2 diabetes. Another visit was initiated between 2001 and 2003 to collect a second set of serum samples from 6136 of these participants. For the present study, the data from this second visit were used as a baseline. Participants with cardiovascular (CV) events between the first and second visit ($$n = 181$$) were excluded from the study, since re-events were not registered for these individuals. This resulted in a total of 5955 participants included in this study. The study was reviewed by the Institutional Review Board (IRB) of the UMCG (MEC $\frac{96}{01}$/022). All eligible individuals gave written informed consent for their participation, and the study was performed according to the Declaration of Helsinki [2013] principles. ## 2.2. Data Collection All patients filled out a questionnaire pertaining to information regarding their demographics, lifestyle habits (e.g., smoking, alcohol consumption), health status (e.g., history of cardiovascular disease, diabetes), medication use, and anthropometric measurements (body height, weight, waist circumference). Blood pressure was measured each minute, for a total of 8 min, in the supine position in an automatic fashion (Dinamap XL Model 9300 series device, Johnson & Johnson Medical, Tampa, FL, USA). The average of the last two measurements was taken as the ultimate blood pressure. Alcohol usage was answered with the options “no”, “1–4 per month”, “2–7 per week” 1–3 per day”, or “>4 per day”. Smoking was distinguished between “never”, “former” and “current”. Waist circumference was measured on the bare skin at the natural indentation between the 10th rib and the iliac crest. Fasting venous blood samples were obtained, of which aliquots were stored at −80 °C and urine samples were stored at −20 °C until further analysis. Serum creatinine was measured enzymatically (Roche Modular, Roche Diagnostics, Mannheim, Germany). Serum cystatin C was measured using the Gentian Cystatin C Immunoassay (Gentian AS, Moss, Norway). Cystatin C was calibrated with known standards, according to the manufacturer’s instructions and following the guidelines of the International Federation of Clinical Chemistry Working Group for Standardization of Serum Cystatin C [25]. Triglycerides were measured enzymatically. Low-density lipoprotein (LDL) cholesterol was quantified by the Friedewald formula. Serum total cholesterol and glucose were measured with dry chemistry (Eastman Kodak, Rochester, NY, USA). Total protein levels were determined with spectrophotometry (Roche Modular, Roche Diagnostics, Roche, Mannheim, Germany). High-sensitive C-reactive protein (hs-CRP) levels were measured by nephelometry (Dade Behring Diagnostics, Marburg, Germany). In addition, 24 h urine samples were provided by participants for two days consecutively, after they were instructed both orally and in written fashion. UAE was measured in these samples, and the average was incorporated in the analysis. ## 2.3. Measurements of Oxidative Stress Biomarkers: Free Thiols, Homocysteine, Bilirubin, Gamma-Glutamyl Transferase, and HDL Cholesterol For serum free thiols, samples were stored at −80 °C until analysis to avoid any significant changes in stability. Serum free thiol concentrations were measured after applying minor modifications [26,27]. After thawing, serum samples were diluted 4-fold, using a concentration of 0.1 mol/L Tris buffer (pH 8.2). Freezing and thawing does not cause any auto-oxidation processes that could jeopardize our measurements. Using the Varioskan microplate reader (Thermo Scientific, Breda, The Netherlands), background absorption was measured at 412 nm, together with a reference measurement at 630 nm. Following this, 20 μL of 1.9 mmol/L 5,5′-dithio-bis(2-nitrobenzoic acid) (DTNB, Ellman’s Reagent, CAS-number 69-78-3, Sigma Aldrich Corporation, St. Louis, MO, USA) in 0.1 M phosphate buffer (pH 7.0) was added to the samples, and the absorbance was measured again after the samples were incubated for 20 min at room temperature. Final concentrations of serum free thiols were established by parallel measurements of an L-cysteine (CAS-number 52-90-4, Fluka Biochemika, Buchs, Switzerland) calibration curve (concentration range from 15.625 to 1000 μmol/L) in 0.1 M Tris/10 mM EDTA (pH 8.2). Intra- and interday coefficients of variation (CV) of all measurement values were below $10\%$. Lastly, serum free thiol concentrations were adjusted to total serum protein levels (measured according to standard procedures), by calculating the free thiol/total protein ratio (μmol/g of protein). This adjustment was performed as serum proteins harbor the largest number of free thiols, and, therefore, largely determine the levels of potentially detectable free thiols. Homocysteine concentrations were measured on a Roche Cobas analyzer (Roche Diagnostics). HDL cholesterol levels were determined by using a homogeneous method (direct HDL; Aeroset System; Abbott Laboratories, Abbott Park, IL, USA) [14]. Plasma total bilirubin was measured by a colorimetric assay (2,4-dichloroaniline reaction; Merck MEGA, Darmstadt, Germany), with the detection limit being 1.0 mmol/L [16]. Serum γ-GT levels were measured by an enzymatic colorimetric method (Roche Modular p; Roche Diagnostics, Mannheim, Germany) [19]. ## 2.4. Study Outcomes and Definitions The primary study outcomes were the occurrence of cardiovascular (CV) events and all-cause mortality. Both fatal and non-fatal CV events were considered, and comprised a group containing cases of acute myocardial infarction, acute or subacute ischemic heart disease, coronary artery bypass grafting, percutaneous transluminal coronary angioplasty, intracerebral hemorrhage, other intracranial hemorrhages, subarachnoid hemorrhage, stenosis and occlusion of precerebral or cerebral arteries, and other vascular interventions, such as carotid endarterectomy, aorta peripheral bypass surgery, or percutaneous transluminal femoral angioplasty. Outcome data were retrieved from the Dutch National Registry of all hospital discharge diagnoses (Prismant), and this information was classified according to the International Statistical Classification of Diseases (ICD-10) and the International Classification of Health Interventions [28]. The estimated glomerular filtration rate (eGFR) was calculated by using the combined creatinine cystatin C-based Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation [29]. Type 2 diabetes was defined as a fasting glucose concentration ≥ 7.0 mmol/L or the use of oral antidiabetics, following the American Diabetes Association (ADA) guidelines. Hypertension was defined as systolic blood pressure (SBP) of ≥140 mmHg, a diastolic blood pressure (DBP) of ≥90 mHg, or both, as well as the use of antihypertensive agents. Hypercholesterolemia was defined by serum total cholesterol levels of ≥6.5 mmol/L, serum HDL cholesterol levels of ≤0.9 mmol/L, or the use of lipid-lowering drugs. ## 2.5. Statistical Analysis Baseline demographic, clinical, and laboratory data of the study population are presented as mean ± standard deviation (SD), median (interquartile range, IQR), or as proportions n, with corresponding percentages (%). The assessment of normality was performed by a visual check of normal probability (Q-Q) plots and histograms. Differences between men and women for continuously distributed variables were tested using independent sample t-tests or Mann–Whitney U-tests, while for categorical variables, Chi-squared tests were performed, as appropriate. To identify the factors that were independently associated with serum free thiol levels, univariable and multivariable linear regression analyses were performed. Standardized beta (β) coefficients and corresponding p-values, derived from the linear regression analysis, were reported, in order to indicate the strength, direction, and statistical significance of the associations. Standardized β-coefficients represent the difference in biomarker levels per 1 SD increment for continuous variables, and the difference in biomarker levels in comparison to the specific reference group, in the case of categorical variables. Assumptions of residual normality and of homoscedasticity for linear regression were fulfilled. Biomarker levels were 2log-transformed prior to further analysis, in order to facilitate the results’ interpretation (expressed as per doubling). Survival distributions were created for tertiles of serum free thiol, homocysteine, gamma-GT, HDL cholesterol, and bilirubin levels by using Kaplan–Meier survival analysis. Survival time was calculated from baseline (at time of serum sampling) to the last visit, the occurrence of a cardiovascular (CV) event, death, or 1 January 2011 (end of follow-up). Cox proportional hazards regression analyses were used to assess the prospective sex-specific associations between the studied biomarkers and the risk of CV events, as well as all-cause mortality. Results from Cox proportional hazards regression models are expressed as hazard ratios (HRs). The proportionality of hazard assumption was fulfilled for all predictor variables. Multivariable Cox proportional hazards regression models were built, in order to adjust for potential confounding variables. The discriminative capacities of the Cox proportional hazards regression models were evaluated with Harrell C-statistics. Likelihood ratio (LHR) tests were used to investigate the potential incremental predictive value of selected biomarkers, in terms of clinical risk factors, with tests conducted separately for men and women. Data analysis was performed using SPSS Statistics 28.0 (SPSS Inc., Chicago, IL, USA), and data visualization was performed using RStudio (v.4.0.2) and Python (v.3.9.0, Python Software Foundation) using the pandas (v.1.2.3), numpy (v.1.20.0), matplotlib (v.3.4.1), and seaborn (v.0.11.1) packages. Two-tailed p-values of ≤0.05 were considered statistically significant. ## 2.6. Selection of Potentially Confounding Factors: The Directed Acyclic Graph (DAG) In order to determine potentially confounding variables that need conditioning in the prospective analyses of associations between biomarkers and study outcomes (CV events and all-cause mortality), a directed acyclic graph (DAG) was constructed (Figure 1). DAGs are causal models that serve as a theoretical basis for pre-defining the involved causal mechanisms that are hypothesized to underlie the variables at hand. The DAG depicts arrows that represent the hypothesized causal (direct) effects between variables, whereas the absence of such arrows represents the assumption of no such direct effect. In this study, we aimed to estimate the association between oxidative stress biomarkers and the risk of CV events and all-cause mortality, for which a distinct set of potentially confounding variables was identified and conditioned, in order to achieve an unconfounded effect estimate in the statistical analysis. Based on this DAG, the following variables warranted conditioning in the analysis: age, sex, smoking, total cholesterol, history of diabetes, and systolic blood pressure. ## 3.1. Baseline Cohort Characteristics Baseline population characteristics and laboratory parameters are described in Table 1, for the total population, as well as specifically for men and women. A total of 5955 research participants (2917 men and 3038 women) were included in the analyses. Baseline levels of protein-adjusted FT, homocysteine, γ-GT. and bilirubin were higher in men than in women ($p \leq 0.001$), whereas HDL cholesterol levels were higher in women than in men ($p \leq 0.001$). ## 3.2. Cross-Sectional Associations between Biomarkers and Baseline Characteristics Univariable and multivariable linear regression analyses were performed, in order to study the cross-sectional associations between oxidative stress biomarkers and relevant study population characteristics (Figure S1, Tables S1–S5). In multivariable analyses, age was negatively associated with protein-adjusted FT (St. β = −0.126, $p \leq 0.001$). BMI was negatively associated with homocysteine (St. β = −0.138, $p \leq 0.001$) and gamma-GT (St. β = −0.055, $$p \leq 0.036$$). Waist circumference showed a strong inverse association with HD cholesterol levels (St. β = −0.302, $p \leq 0.001$), while being positively associated with gamma-GT and homocysteine levels (St. β = 0.127, $p \leq 0.001$, and St. β = 0.145, $p \leq 0.001$, respectively). Renal function (eGFR) was positively correlated with protein-adjusted FT (St. β = 0.192, $p \leq 0.001$), but was inversely correlated with homocysteine (St. β = −0.234, $p \leq 0.001$). Triglycerides were positively associated with protein-adjusted FT and gamma-GT (St. β = 0.107, $p \leq 0.001$ and St. β = 0.156, $p \leq 0.001$), while being inversely associated with bilirubin and HDL cholesterol (St. β = −0.077, $p \leq 0.001$ and St. β = −0.315, $p \leq 0.001$). Remaining associations are presented in Tables S1–S5 and Figure S1. ## 3.3. Sex-Specific Prospective Associations between Oxidative Stress Biomarkers and CV Events and All-Cause Mortality Over an average follow-up of 7.7 (±2.0) years, 402 ($6.8\%$) CV events occurred. The highest rate of CV events was observed in participants who were within the lowest tertiles of protein-adjusted FT and HDL cholesterol levels, the second tertile of bilirubin levels, and the highest tertiles of gamma-GT and homocysteine levels. Kaplan–Meier survival analysis revealed significant differential survival distributions between tertiles of protein-adjusted FT (log-rank test, $p \leq 0.001$), bilirubin ($p \leq 0.05$), HDL cholesterol, gamma-GT, and homocysteine (all $p \leq 0.001$) (Figure 2). Cox proportional hazards regression analyses revealed that protein-adjusted FT, γ-GT, and homocysteine were significantly associated with the risk of CV events (all $p \leq 0.001$, Model 1, Table 2) in both sexes, while HDL cholesterol was also significantly associated with the risk of CV events in women (Model 1, $$p \leq 0.014$$). When adjusting for age, history of diabetes, systolic blood pressure, BMI, total cholesterol, current smoking, and hs-CRP levels (DAG-based confounding factors), protein-adjusted FT and homocysteine levels remained significantly associated with the risk of CV events in men (Model 4, hazard ratio (HR) 0.63 [$95\%$ CI: 0.40–0.99], $$p \leq 0.045$$ and HR 1.58 [1.20–2.08], $$p \leq 0.001$$, respectively), whereas none of the biomarkers were significantly associated with the risk of CV events in women after full adjustment for confounding factors (Figure 3). During follow-up, a total of 316 ($5.3\%$) participants died. Protein-adjusted FT, HDL cholesterol, γ-GT, and homocysteine levels were significantly associated with the risk of all-cause mortality in both sexes (Table 2, Model 1, all $p \leq 0.05$). However, when these associations were adjusted for the selected confounding factors, only protein-adjusted FT and HDL cholesterol remained significantly associated with the risk of all-cause mortality in men (Model 4, HR 0.52 [0.32–0.85], $$p \leq 0.009$$ and HR 0.90 [0.85–0.94], $p \leq 0.001$, respectively), while bilirubin and homocysteine were significantly associated with all-cause mortality in women (Model 4, HR 0.68 [0.48–0.98], $$p \leq 0.040$$ and HR 2.30 [1.14–3.76], $p \leq 0.001$, respectively). Interaction analyses revealed significant effect modifications for the associations between protein-adjusted FT, as well as bilirubin, and the risk of all-cause mortality by sex (both $p \leq 0.05$), with the strongest associations in males being for protein-adjusted FT levels, and those in females being for bilirubin (Figure 3). ## 3.4. Incremental Value of Oxidative Stress Biomarkers over Clinical Risk Factors Subsequently, we aimed to evaluate the added value of the biomarkers over a base risk model containing the selected confounding factors (derived from the DAG) in predicting the risk of CV events (Table 3). In general, the addition of the biomarkers did not substantially improve model discrimination across both sexes. In men, the greatest increment was observed after adding homocysteine (ΔC-statistic: 0.005, $p \leq 0.01$), followed by protein-adjusted FT (ΔC-statistic: 0.002, $p \leq 0.05$). The addition of both homocysteine and protein-adjusted serum free thiols did not yield an improved model discrimination when compared to either one of these biomarkers (LHR Chi-square: 2.35, $$p \leq 0.125$$). For bilirubin, HDL cholesterol, and γ-GT, no significant model discrimination or model fit was observed. In women, none of the biomarkers significantly improved model discrimination or model fit. ## 4. Discussion The current study indicates significant associations between protein-adjusted FT, bilirubin, HDL cholesterol, γ-GT, and homocysteine as oxidative stress biomarkers, as well as highlights the risk of CV events and all-cause mortality in individuals from the general population. Baseline levels of protein-adjusted FT, homocysteine, γ-GT, and bilirubin were higher in men than in women ($p \leq 0.001$), whereas HDL cholesterol levels were higher in women ($p \leq 0.001$). After adjustment for potentially confounding factors, protein-adjusted FT and homocysteine levels were significantly associated with the risk of cardiovascular (CV) events, though only in men. After adjustment for selected confounding factors, only protein-adjusted FT and HDL cholesterol levels remained significantly associated with the risk of all-cause mortality in men, while bilirubin and homocysteine remained significantly associated with all-cause mortality in women. These results confirm the strength of protein-adjusted FT as being a reliable biomarker for oxidative stress, in the context of cardiovascular disease and all-cause mortality, while also highlighting the importance of other oxidative stress-related biomarkers, such as homocysteine, HDL cholesterol, and bilirubin. Combining both the protein-adjusted free thiol and homocysteine levels in a statistical model, however, did not yield a significantly different outcome. Previous studies have also found that serum free thiols positively associate with CV events and mortality in the general population, and that they comprise a useful biomarker for oxidative stress in the general population [30]. Additionally, total thiol levels have also been shown to strongly associate with CV events and all-cause mortality [31]. However, in our study, we found this association to only remain significant in men for both CV events and all-cause mortality, after adjusting for potentially confounding factors. Previously we have investigated the associations between this biomarker and CV events in the female population, where we also concluded that these associations lost their significance after correcting for age, suggesting that age-related factors play an important role in the associations between oxidative stress and the occurrence of cardiovascular disease, especially in women [20]. This sex-related difference could potentially be explained by menopausal differences. Menopause is believed to be accompanied by oxidative stress, which may at least partially be driven by reduced estrogen production, which has known antioxidant effects [32]. We hypothesize that this could lead to comparatively higher levels of FT in men than in women. Homocysteine is known to be associated with an increased risk of cardiovascular disease and mortality [31,32,33,34,35]. More recently, it has also been found to upregulate oxidative stress via enhancing GPX4 (glutathione peroxidase) methylation [12]. Another study found hyperhomocysteinemia to be an independent risk factor of coronary heart disease, but did not draw sex-specific conclusions [36]. Our findings of homocysteine being positively associated with mortality were confirmed by another study, though they did not examine sex-specific associations, whereas we found the association to only remain statistically significant in women, after correcting for potentially confounding factors [37]. Homocysteine concentrations are significantly higher in men than in women, which could relate to changes in renal function and creatinine concentrations [38]. Moreover, homocysteine is reduced by estrogen, both directly, through effects on homocysteine synthesis, and indirectly, through its effect on gene expression [39]. We theorize that this could explain why CV events were associated significantly with homocysteine levels in men only, as women might benefit from this estrogen-related reduction until menopausal age. Menopause appears to play an important role in regulating homocysteine concentrations in women, which could potentially explain why there was a significant association between homocysteine levels and mortality in women in our study [40,41]. Upon combining FT and homocysteine in a statistical model to predict CV events and mortality, we did not yield any significantly different new results. Further research into combining multiple biomarkers for the prediction of CV events and all-cause mortality could prove to be interesting if shown to increase the accuracy of prediction. After correcting for potentially confounding factors, we found HDL cholesterol levels to significantly inversely associate with only all-cause mortality in men. Several studies draw conclusions about HDL cholesterol and all-cause mortality. Total and small HDL particle concentrations strongly and independently predicted 3 month mortality in acute heart failure patients [42]. Interestingly, there is a paradoxical association of high HDL cholesterol levels with high mortality in the general population [43]. This association is U-shaped, meaning there appears to be an increased risk of all-cause mortality at both the lowest and highest concentrations of HDL cholesterol [44]. A different study attributed the paradoxical association to genetic variations in certain mutations that were previously associated with an increased risk of coronary heart disease, as well as high concentrations of HDL cholesterol [45,46]. In a pooled analysis of 37 prospective cohort studies, researchers further supported the U-shaped association of both extremely high and low HDL cholesterol levels with an increased risk of all-cause mortality [47]. Finally, pharmacologically increasing levels of HDL cholesterol does not seem to reduce CV events in substantial trials of the three agents they investigated [48]. A previous PREVEND study also showed a modest log-linear inverse association between circulating total bilirubin levels and cardiovascular events that was independent of established risk factors [16]. However, they did not find evidence of sex differences significantly modifying the bilirubin–CV event association, which is similar to our study, where we did not find any significant associations after correcting for potentially confounding factors. We did, however, demonstrate an inverse association between bilirubin and all-cause mortality, but only in women. Other researchers also found a strong negative association between plasma bilirubin levels and both total and cancer mortality, albeit they did not investigate sex-related differences [49]. After correcting for potentially confounding factors, γ-GT seemed to associate, in both men or women, with neither CV events nor mortality in our study. However, this biomarker has been found to positively correlate with both CV events and all-cause mortality in the past [50,51,52]. This difference in conclusions may be caused by differences in study populations, as another study with data from the PREVEND study came to a similar conclusion as ours, in that γ-GT correlates significantly with CV events only until correction for potentially confounding factors [19]. Further large-scale research would be required to untangle this. One of the core strengths of the present study is the size of the study population. The PREVEND study features information on dozens of phenotypic variables from thousands of people. Furthermore, this study was of a prospective nature, encompassing almost 10 years of follow-up, granting insight into the development of cardiovascular disease and rate of mortality during this period. Additionally, our study investigated five different biomarkers of oxidative stress within the same population, which allowed us to better compare the strength of the individual biomarkers than studies that may only look at one or two at a time. However, any potential limitations of our study should also be taken into account. The data used in this study contained a vast majority of study subjects of Caucasian ethnicity, which makes it difficult to draw any conclusions that can be applied to other ethnicities. A similar caution about the generalizability should be applied to other data points that may be related to the geographical location of the PREVEND study population. As with most databases that are partially survey-based, the self-reported nature of some variables could lead to over- or underestimation by participants. In addition, the study endpoint’s definition of CV events did not include heart failure (HF) as an outcome, since it is pathophysiologically distinct, and thus requires further study. Furthermore, the parameters regarding the biomarkers we investigated in our study were limited by previously generated biomarker data and, thus, only a few were eligible for analysis. In this regard, the potential value of γ-GT and total bilirubin levels as oxidative stress biomarkers needs to be cautiously observed in future studies, since their levels may be impacted by the presence of hepatobiliary disease. There was an insufficient volume of collected from the study participants that could otherwise have been used to extend our panel of oxidative stress markers. It is possible that because of this, we might have missed alternative redox biomarkers that could have an equal or better predictive value in relation to CV events and all-cause mortality than those we investigated. An unbiased approach, such as investigating a combination of key components of the redox metabolome, would be preferable, as read-outs of multiple redox-regulated metabolic pathways would be combined. However, such “redox metabolomics” approaches are still constrained by several (mostly methodological) issues, which would have to be further explored first [53]. Additionally, further research into possibilities for therapeutic modulation of redox biomarkers could contribute to a decrease in oxidative stress, with all the health benefits that come with that. For example, serum free thiols are amenable to nutritional or therapeutic intervention, and may be of use in the therapeutic modulation of redox status in various conditions [54,55]. ## 5. Conclusions In conclusion, we demonstrated that both protein-adjusted FT and homocysteine associate significantly with the risk of CV events and all-cause mortality. 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--- title: Dysregulation of Serum MicroRNA after Intracerebral Hemorrhage in Aged Mice authors: - Dominic Robles - De-Huang Guo - Noah Watson - Diana Asante - Sangeetha Sukumari-Ramesh journal: Biomedicines year: 2023 pmcid: PMC10044892 doi: 10.3390/biomedicines11030822 license: CC BY 4.0 --- # Dysregulation of Serum MicroRNA after Intracerebral Hemorrhage in Aged Mice ## Abstract Stroke is one of the most common diseases that leads to brain injury and mortality in patients, and intracerebral hemorrhage (ICH) is the most devastating subtype of stroke. Though the prevalence of ICH increases with aging, the effect of aging on the pathophysiology of ICH remains largely understudied. Moreover, there is no effective treatment for ICH. Recent studies have demonstrated the potential of circulating microRNAs as non-invasive diagnostic and prognostic biomarkers in various pathological conditions. While many studies have identified microRNAs that play roles in the pathophysiology of brain injury, few demonstrated their functions and roles after ICH. Given this significant knowledge gap, the present study aims to identify microRNAs that could serve as potential biomarkers of ICH in the elderly. To this end, sham or ICH was induced in aged C57BL/6 mice (18–24 months), and 24 h post-ICH, serum microRNAs were isolated, and expressions were analyzed. We identified 28 significantly dysregulated microRNAs between ICH and sham groups, suggesting their potential to serve as blood biomarkers of acute ICH. Among those microRNAs, based on the current literature, miR-124-3p, miR-137-5p, miR-138-5p, miR-219a-2-3p, miR-135a-5p, miR-541-5p, and miR-770-3p may serve as the most promising blood biomarker candidates of ICH, warranting further investigation. ## 1. Introduction Stroke is one of the most severe health issues that plagues the healthcare system. Intracerebral hemorrhage (ICH) is the second most common type of stroke and has a higher risk of mortality and morbidity rates than other stroke types [1]. Notably, there is no effective treatment for ICH [2,3,4,5]. Therefore, preclinical and clinical research on this disease is essential. ICH arises in the form of blood vessel rupture in the brain, resulting in the accumulation of blood in the brain parenchyma and the development of hematoma [6]. ICH often causes severe brain damage that is categorized into primary and secondary brain injuries. The mass effect of the hematoma mostly contributes to primary brain damage, whereas the oxidative and inflammatory signaling pathways [7,8], induced by blood components such as thrombin, hemoglobin, hemin, and iron, are responsible for secondary brain damage [9,10]. In contrast to primary brain damage, secondary brain damage persists for a longer period of time, which could contribute to both acute and long-term neurological outcomes [11]. Hence, the molecular regulators of secondary brain damage are considered potential targets for therapeutic intervention [12]. However, a detailed mechanistic understanding of the molecular events underlying secondary brain injury after ICH is lacking [13]. This represents a significant gap in the literature and reflects on the lack of defined therapeutic targets. MicroRNAs (miRNAs), short non-coding RNAs, comprise a group of regulatory molecules that modulate the expression of genes, which play critical roles in cellular processes such as inflammation and apoptosis [14,15]. Many studies have identified the changes in miRNA expression in ischemic stroke [16], while there remains a significant gap in our knowledge of their dysregulation in ICH, particularly in the elderly. Notably, circulating miRNAs undergo dysregulation in response to pathological conditions [17] and can be found in a remarkably stable form in serum or plasma [18]. Therefore, miRNAs could serve as non-invasive diagnostic and prognostic blood biomarkers. Specifically, diagnostic blood biomarkers may help distinguish ICH from ischemic stroke, while prognostic blood biomarkers may be able to predict mortality or poor outcomes after ICH. Aging is characterized by the accumulation of degenerative processes. MiRNAs contribute to aging [19] and have regulatory roles in neurodegeneration [20,21]. Moreover, aging is listed as the most profound risk factor for cardiovascular and neurological diseases [22]. Notably, ICH incidence and mortality rates increase with aging [23,24,25], but the precise role of aging in the pathophysiology of ICH remains largely unknown. Therefore, it is highly required to characterize the molecular level changes that occur after ICH in aged subjects, as it may help develop novel strategies for the diagnosis and management of ICH. Though preclinical animal models of ICH are invaluable tools for studying disease pathophysiology, miRNA dysregulation post-ICH was mostly studied in young animal subjects [17]. Moreover, aging is associated with miRNA expression level changes in mice and humans [26,27,28,29]. Hence, the objective of this study is to identify circulating miRNAs that are dysregulated after ICH in aged mice, as it may help characterize the pathophysiology of ICH in the elderly. ## 2.1. ICH Induction All animal studies were performed according to the protocols approved by the Institutional Animal Care and Use Committee, in accordance with the NIH and USDA guidelines. Intracerebral hemorrhage was induced in aged male C57BL/6 mice (18–24 months), (Jackson Laboratories, Bar Harbor, ME, USA), as previously reported by our laboratory [2,30,31,32,33]. Briefly, mice were anesthetized with isoflurane and positioned prone on a stereotaxic head frame (Stoelting, Wood Dale, IL, USA). Using a high-speed drill (Dremel, Racine, WI, USA), a burr hole (0.5 mm) was made 2.2 mm lateral to the bregma, and a small animal temperature controller (David Kopf Instruments, Los Angeles, CA, USA) was used to keep the body temperature at 37 ± 0.5 °C. Employing a Hamilton syringe (26-G), 0.04 U of bacterial type IV collagenase (Sigma, St. Louis, MO, USA) in 0.5 μL phosphate-buffered saline (phosphate buffered saline; pH 7.4 (PBS) was injected with the stereotaxic guidance 3.0 mm into the left striatum to induce ICH [2]. After removing the needle, bone wax was used to seal the burr hole and the incision was stapled. Sham mice underwent the same surgical procedure, but only PBS (0.5 μL) was injected, which served as the experimental control. ## 2.2. Neurobehavioral Analysis Mice were analyzed for neurobehavioral deficits, as previously reported, using a 24-point scale [33,34,35], which estimates sensorimotor deficits. The neurobehavioral analysis consisted of six different tests: circling, climbing, beam walking, compulsory circling, bilateral grasp, and whisker response. Each test was graded from 0 (no impairment) to 4 (severe impairment) and the sum of the scores on all six tests established a composite neurological deficit score. ## 2.3. Serum Collection Blood was collected from deeply anesthetized mice and allowed to clot, undisturbed, at room temperature. Then, the clot was removed by centrifugation at 1500× g for 10 min in a refrigerated centrifuge. The supernatant or serum was collected and stored at −80 °C. Before miRNA isolation, the serum was thawed and centrifuged, and the supernatant was used for miRNA isolation. ## 2.4. miRNA Isolation miRNA isolation procedure was performed using the miRNeasy Mini Kit (Qiagen, Hilden, Germany, catalogue. No: 217004), according to the manufacturer’s instructions, with some modifications. Briefly, the TRIzol LS reagent was added to mouse serum (0.75 mL TRIzol per 0.25 mL serum). This was followed by the addition of chloroform (0.2 mL chloroform per 0.75 mL of TRIzol), and centrifugation at 12,000× g at 4 °C for phase separation. The aqueous phase was transferred to a new tube and $100\%$ ethanol (1.5 volumes of the sample) was added and mixed thoroughly and transferred to the RNeasy Mini spin column to elute the miRNA, according to the manufacturer’s instructions. ## 2.5. miRNA Sequencing RNA quality and quantity were assessed by the Agilent 2100 bioanalyzer (Agilent Technologies, Santa Clara, CA, USA). Purified small RNA samples were processed for cDNA library preparations using the QIAseq miRNA Library kit (Qiagen, catalogue. No: 331502). Briefly, 15 ng of purified small RNA was ligated with a 3′ adaptor and 5′ adaptor, and converted to cDNA using RT primer with integrated unique molecular indices (UMI), to enable the quantification of individual miRNA molecules. The cDNA products were purified, enriched with PCR, and purified using QMN Beads (Qiagen, catalog. No: 331502) to create the final cDNA library. The prepared library was examined by a bioanalyzer and Qubit (Thermo Fisher, Waltham, MA, USA), to test the quality and quantity of the sequencing library, respectively. The libraries were pooled with the correspondingly identified bar codes for each sample and run on the NextSeq500 sequencing system using a 75-cycle paired-end protocol. BCL files generated by the NextSeq500 were converted to FASTQ files for downstream analysis. Reads that passed quality control with individual UMI counts were aligned to the murine reference miRNA sequences using a web-based tool, GeneGlobe Data Analysis Center of QIAGEN, which also performed differential expression analysis and generated a volcano plot, and a hierarchical clustering heatmap. ## 2.6. Statistical Analysis Statistical analysis was performed using GraphPad Prism software and the student’s t-test was used for two-group comparisons. $p \leq 0.05$ was taken as statistically significant. ## 3.1. Serum microRNA Isolation and Analysis after ICH ICH was induced in the striatum of aged, male C57BL/6 (18–24 months) mice, using the collagenase injection method. For miRNA analysis, we collected whole blood from mice on day 1 post-ICH, an acute time point, which exhibited profound neurodegeneration [36] and had significant predictive values in the patient prognosis [37]. Serum microRNA was then isolated, as described in the methods, and subjected to RNA sequencing using the Agilent 2100 bioanalyzer (Agilent Technologies). The serum miRNAs from sham animals served as the experimental controls and the schematic representation of the overall experimental design is depicted (Figure 1). The analysis of RNA sequencing data, using QIAGEN GeneGlobe Data Analysis Center, identified 1960 miRNAs, out of which 28 miRNAs exhibited a significant difference ($p \leq 0.05$) in their expression between ICH and sham (Table 1, Figure 2 and Figure 3). Among those, the serum levels of 20 miRNAs were found to be significantly increased ($p \leq 0.05$) and the serum levels of 8 miRNAs were significantly decreased ($p \leq 0.05$) after ICH in comparison to sham (Table 1). Soon before collecting the blood samples for miRNA analysis, the animals were subjected to neurobehavioral analysis to confirm the ICH induction. Notably, ICH animals exhibited profound neurobehavioral deficits in comparison to sham ($p \leq 0.01$; Figure 4). ## 3.2. Functional Annotation of Differentially Expressed microRNAs Many of the dysregulated miRNAs identified in this study play roles in various pathological conditions, as shown in Table 2. Notably, miR-122-5p, miR-9-3p, miR-9-5p, miR-137-3p, miR-1298-5p, miR-219a-2-3p, miR-384-5p, miR-124-3p, miR-34b-5p, miR-200b-3p, miR-135a-5p, miR-133b-3p, miR-1199-5p, miR-451a, miR-138-5p, miR-146a-5p, miR-200b-5p, and miR-483-5p have roles in neuroinflammation, oxidative stress, and apoptosis, which are critical processes associated with secondary brain damage after ICH. ## 3.3. The Comparative Analysis of miRNAs To determine the clinical significance of our observations, we compared our results to the study reported by Wang et al. [ 75], which enabled the comparison of dysregulated serum miRNAs after ICH in aged mice with human-brain-enriched miRNAs and plasma miRNAs after ICH in young rodents, as demonstrated in Figure 5. We found that miR-124-3p, miR-138-5p, and miR-137-5p, which are differentially expressed in the aged mouse serum after ICH, are also enriched in the human brain tissue, implicating their potential role in ICH-associated brain injury or recovery. Also, the miR-135a-5p level was increased in the blood of both aged mice and young rats after ICH, while miR-219a-2-3p, miR-770-3p, and miR-541-5p were differentially dysregulated in aged mice and young rats after ICH. This discrepancy could be due to differences in the age of the animal models or species that were used in preclinical studies. Therefore, further research is needed to validate these findings as these microRNAs could serve as invaluable molecular targets for the diagnosis and management of ICH that occurs in the elderly. ## 4. Discussion We identified 28 significantly dysregulated miRNAs in the serum of aged mice after ICH. In comparison to a previous study by Wang et al., [ 75], miR-124-3p, miR-137-5p, miR-138-5p, miR-219a-2-3p, miR-135a-5p, miR-541-5p, and miR-770-3p could be the most potential candidates to be tested for their roles post-ICH in the elderly. Wang et al. [ 75] demonstrated the dysregulation of plasma miRNAs in a young rat model of ICH. They focused primarily on miR-124 as a biomarker for ICH, but we also found increased serum levels of miR-138-5p and miR-137-5p in aged mice after ICH. A recent study documented an increased serum level of miR-137 after traumatic brain injury in patients [45]. Moreover, miR-124-3p, miR-137-5p, and miR-138-5p are human-brain-specific miRNAs [75], further demonstrating their potential to serve as serum biomarkers or therapeutic targets for intracerebral hemorrhage, warranting further investigation. Overall, the functional roles of miR-124-3p, miR-137-5p, miR-138-5p, miR-219a-2-3p, miR-135a-5p, miR-541-5p, and miR-770-3p in various pathological processes/states and their possible functions in the pathophysiology of ICH are discussed. ## 4.1. miR-124-3p We found a significant increase in the level of miR-124-3p in the serum of aged mice after ICH compared to sham. This observation is consistent with a previous study, where the miR-124 level was found to be significantly increased in the plasma of ICH patients in the acute phases of injury, suggesting that miR-124 may serve as a biomarker for the diagnosis of ICH [75]. Functionally, miR-124 plays a key role in iron metabolism and neuronal cell death after ICH, and its inhibition reduced brain injury after ICH in aged mice [76], implicating the detrimental role of miR-124 after ICH. Consistently, high serum miR-124 levels were correlated with poor neurological scores in aged ICH patients [76]. Since miR-124-3p is one of the most abundant brain-specific microRNAs, studies are required to elucidate whether brain injury leads to its release into the blood plasma or serum after ICH. In young rats, miR-124 was significantly elevated in the plasma and brain tissue, in a collagenase-injection mouse model of ICH, during the acute phase of the injury [75]. By contrast, in a blood-injection mouse model of ICH in young mice, miR-124 expression was found to be decreased in the perihematomal region of the brain [77]. Moreover, miR-124 attenuated ICH-induced inflammatory brain damage in young mice by modulating microglia polarization, implicating the neuroprotective role of miR-124 after ICH [77]. The underlying cause of this discrepancy in its expression after ICH and its function could be the difference in the ICH model and the age of animal subjects. Hence, further investigation is highly needed. Altered serum expression of miR-124 is associated with various brain injuries. To this end, miR-124-3p was not detectable in healthy volunteers, but its increased level was observed in the serum of patients with severe traumatic brain injury [70]. Moreover, serum miR-124 is significantly enhanced in patients with acute ischemic stroke, where its expression positively correlated with infarct volume and degree of brain damage, as assessed by the National Institutes of Health Stroke Scale [145]. Overall, apart from considering miR-124-3p as a potential biomarker candidate for ICH, its precise functional role in the pathophysiology of ICH requires further validation. ## 4.2. miR-137-3p Our findings show the increased level of miR-137-3p in the serum of aged mice after ICH in comparison to sham. Upregulation of miR-137-3p inhibited neuronal death, parthanatos, a type of programmed cell necrosis associated with ICH [52,146,147,148,149]. In addition, upregulation of miR-137-3p resulted in neuroprotective effects by decreasing neuronal nitric oxide synthase-positive cells and the death of motor neurons after avulsion injury to the spinal cord in rats [53]. Furthermore, an increased level of miR-137 is observed in the serum after traumatic brain injury [45]. Given the role of miR-137-3p in neuronal death and oxidative damage, further studies are warranted to explore its potential as a therapeutic target for ICH. ## 4.3. miR-138-5p As per the current study, miR-138-5p levels were found to be significantly increased in the serum of aged mice after ICH compared to sham. Notably, breast cancer cell-derived miR-138-5p has been shown to inhibit M1 polarization and promote M2 polarization of macrophages [111]. It has also been proposed as a potential blood biomarker of Parkinson’s disease [114]. Furthermore, miR-138-5p downregulated NLRP3 inflammasome and its downstream gene targets in lipopolysaccharide-treated rat microglia [112]. Overall, given the role of miR-138-5p in macrophage polarization and inflammation, further studies are required to elucidate its functional role after ICH. In line with dysregulated plasma miRNAs after ICH in rats [75], levels of miR-135a-5p were increased in the serum of aged mice after ICH, but miR-219a-2-3p, miR-541-5p, and miR-770-3p were differentially dysregulated in aged mice and young rats after ICH. Based on their potential roles in ICH pathology in association with their altered expression, their functions in various pathological conditions are discussed. ## 4.4. miR-135a-5p M2 microglia-derived extracellular vesicles contained elevated levels of miR-135a-5p, which reduced neuronal autophagy and ischemic brain injury in mice by inhibiting inflammasome signaling [92], suggesting its role in neuroprotection. In contrast, exercise decreased miR-135 levels in adult neural precursor cells and miR-135a-5p inhibition stimulated neurogenesis in the dentate gyrus of aged mice [150]. As well, miR-135a-5p expression in the hippocampus is increased in temporal lobe epilepsy in children [91]. miR-135a-5p mediated proapoptotic effect by inducing cellular apoptosis and reduced cell survival in temporal-lobe epilepsy [91]. Furthermore, its inhibition protected glial cells against epilepsy-induced apoptosis [151]. The miR-135a-5p expression level was significantly decreased in the serum samples of atherosclerosis patients and a mouse model of atherosclerosis [90]. Moreover, overexpression of miR-135a-5p induced a cell cycle arrest and apoptosis, and inhibited the proliferation and migration of vascular smooth muscle cells [90]. Additionally, miR-135 is a tumor suppressor and has been shown as a diagnostic biomarker of colorectal cancer [93]. Overall, given its conflicting roles, further studies are highly needed to establish its function after ICH. ## 4.5. miR-219a-2-3p Upregulation of miR-219a-2-3p in tissue samples has been linked to anti-inflammatory responses, possibly by modulating NK-kB singling and promoting neuroprotection after spinal cord injury [60]. Serum-derived miR-219a-2-3p has also been shown to be a potential biomarker for traumatic brain injury in mice [58], as well as a peripheral blood biomarker for lung cancer in patients [59]. Given its potential as a biomarker in traumatic brain injury and its roles in neuroprotection and anti-inflammatory responses after a neural injury, miR-219a-2-3p needs to be explored further for its possible roles after ICH. ## 4.6. miR-541-5p Upregulation of miR-541-5p has been linked to hepatocellular carcinoma [127]. miR-541 also contributes to the modulation of telomerase activity [152] and tumor suppression in non-small cell lung cancer [153]. Apart from that, its functional role after a brain pathology remains enigmatic, requiring investigation. ## 4.7. miR-770-3p miR-770-3p is a biomarker for aging, as its expression was found to be increased in the serum of aged mice in comparison to young mice [126]. Apart from that, the role of miR-770-3p remains largely understudied. Therefore, further research is vital to determine its functions after ICH. Though the study reveals several novel candidate miRNAs, some of the identified candidates could be related to ICH irrespective of age, and some could be related to ICH in the context of aging. Therefore, further studies are warranted to identify the age-dependency of those candidates. Moreover, owing to the complexity of aging, the identified candidates, whether related to ICH in an age-dependent or -independent manner, require characterization in aged animal subjects to elucidate their possible roles in apoptosis, neuroinflammation, oxidative stress and secondary brain damage after ICH. ## 5. Conclusions Herein, we identified seven candidate serum miRNAs, miR-124-3p, miR-138-5p, miR-137-3p, miR-219a-2-3p, miR-135a-5p, miR541-5p, and miR-770-3p, which may have roles in the pathophysiology of ICH in the elderly, warranting further investigation. Among those, miR-124-3p, miR-138-5p, and miR-137-3p may have the greatest potential, as they are human-brain-specific miRNAs and are also implicated in neuronal apoptosis and neuroinflammation. 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--- title: Breakage of Tapered Junctions of Modular Stems in Revision Total Hip Arthroplasty—High Incidence in a Consecutive Series of a Single Institution authors: - Oliver E. Bischel - Arnold J. Suda - Paul M. Böhm - Therese Bormann - Sebastian Jäger - Jörn B. Seeger journal: Bioengineering year: 2023 pmcid: PMC10044894 doi: 10.3390/bioengineering10030341 license: CC BY 4.0 --- # Breakage of Tapered Junctions of Modular Stems in Revision Total Hip Arthroplasty—High Incidence in a Consecutive Series of a Single Institution ## Abstract Background: Modularity in revision THA (RTHA) has become accepted during the last three decades. Nevertheless, specific risks of modularity of current revision devices such as breakage of taper junctions occur during follow-up. Data reporting failure rates are predominantly given by the manufacturers but independent data acquisition is missing so far. Questions/Purposes: 1. What time-related risk of breakage of taper junction between neck and body of an established modular revision device can be expected in a consecutive single institutional series and a mid-term follow-up? 2. Are there specific factors influencing breakage in this cohort? Materials and Methods: A retrospective analysis was performed of a consecutive series of 89 cases after femoral revision using a tapered modular revision stem. Mean follow-up period was 7.1 (range: 3.0–13.7) years. Breakage of stem as failure criteria of the implant was investigated with a Kaplan–Meier analysis. Results: Breakage of taper junctions occurred in four patients during follow-up showing a time-depending implant survival of 94.2 ($95\%$ CI: 88.6–$100\%$) after 13.7 years. Implant survival of stems with lateralized necks of 87.4 ($95\%$ CI: 75.6–$100\%$) after 13.7 years was significantly lower compared to the standard offset variant with $100\%$ after 13.5 years (log rank test $$p \leq 0.0283$$). Chi square test also revealed a significantly higher risk of breakage of lateralized necks compared to standard offset pieces ($$p \leq 0.0141$$). Three of four patients were obese with a mean BMI of 37.9 kg/m2. Grade of obesity (grade 1 or higher) had significant influence on risk of breakage. Survival of the implant was significantly lower in obese patients with at least grade 1 obesity compared to patients with a BMI < 30 kg/m2 (82.9 ($95\%$ CI: 64.9–$100\%$) after 11.6 years vs. 98.4 ($95\%$ CI: 95.3–$100\%$) after 13.7 years; log-rank $$p \leq 0.0327$$). Conclusions: Cumulative risk for failure of taper junctions was high in this consecutive single institutional cohort and may further increase during follow-up. As independent data acquisition in registries is missing, failure rate may be higher than reported data of the manufacturers. The use of lateralized offset necks in obese patients of at least grade 1 obesity showed a significantly higher risk of breakage. The use of monobloc revision devices may be an option, but randomized control trials are currently missing to establish standardized treatment protocols considering individual risks for both monobloc and/or modular implants. ## 1.1. Background According to current registry data, more than $10\%$ of all hip arthroplasty procedures had been revisions (German Arthroplasty Registry, Annual report 2021; https://www.eprd.de/en/downloads/reports, accessed on 17 January 2023). In addition, exchange of the stem occurred in nearly $50\%$ of all RTHA procedures. The use of tapered monobloc revision stems has shown to be reliable and safe with excellent mid to long-term results and conical designs may be advantageous compared to cylindrical monobloc implants [1,2,3]. Modular revision stems were introduced in RTHA approximately three decades ago and most currently available devices are combining both an approved tapered stem design and a modular built up. Survival rates of $94\%$ of modular devices with revision due to any cause as the end point have been published in short- to medium-term studies [4,5]. Modularity offers a certain reliability and flexibility to the surgeon. Insertion of the stem can be performed more easily without compromising primary stability. Reconstruction of patients’ hip biomechanics or addressing problems such as dislocation due to missing length or femoral head offset can be adapted intraoperatively or during follow-up without withdrawing the complete implant. Nevertheless, adverse effects such as corrosion, fretting, debris formation, disconnection and even breakage of the stem may appear [6,7,8]. ## 1.2. Rationale There are few data available of some manufacturers giving a mechanical failure rate of the junction of estimated $0.30\%$ [9]. Of the few data available from independent sources, such as current registries, revision due to implant fracture after former revision is $2.8\%$ (National Joint Registry for England, Wales, Northern Ireland and the Isle of Man, 16th Annual Report, 2019; http://reports.njrcentre.org.uk/2018, accessed on 17 January 2023), including all sorts of breakage including taper junctions. Consecutively, it is unclear what incidence of breakage has to be estimated in correlation to potential other failure reasons like infection, periprosthetic fracture or aseptic loosening in a mid- or long-term duration. Specific influences on breakage of taper junctions such as obesity, type of defect, offset or length of the neck segment have been shown in case reports, retrospective or in vitro investigations of modular implants [10,11,12]. The following questions were posed. What time-related risk of breakage of taper junction of an established modular revision device can be expected in a consecutive series and a mid-term follow-up? Are there any influencing factors for breakage of the taper junctions? ## 2. Materials and Methods A consecutive series of 130 revisions between 2003 and 2009 performed with the MRP® revision stem (Peter Brehm, Weissendorf, Germany) was evaluated retrospectively (Figure 1). A follow-up of more than three years as inclusion criteria for this study showed 89 patients (47 women and 42 men; 41 right and 48 left hip joints). Mean age at surgery was 66 (range: 37–85) years. Indication for surgery was aseptic loosening in 44 cases and periprosthetic fracture in 10 hip joints. A total of 35 septic cases required two-stage revisions. All patients were evaluated clinically and radiologically. Defect classification was carried out according to the classification of Paprosky [13]. Body mass index (BMI) was used to determine obesity. Statistic calculations were performed after division into the following groups: underweight, normal weight or preobesity vs. grade 1 or higher obesity (>30 kg/m2); underweight, normal weight, preobesity and obesity of grade 1 and 2 vs. obesity of grade 3 (>40 kg/m2). An ethical vote of the institutional ethics committee was obtained (S-$\frac{096}{2012}$). All patients had signed a consent form and anonymized data were used for evaluation. Statistical analysis was performed with JMP 10 for Mac (SAS Institute Inc., Cary, NC, USA). A time to event analysis was performed using the Kaplan–Meier method with removal of the stem for any cause, aseptic loosening and/or breakage of the stem, and worst case (removal of the stem for any cause and/or aseptic loosening and/or lost to follow-up) serving as failure criteria. A $95\%$ confidence interval was given to all survivorship data; the p-value for comparing survival curves was calculated with the log-rank-test. Associations or correlations between a continuous and/or discrete variable were tested by Student’s t-test, Paired t-test or Chi square test, depending on the underlying empirical distribution. All tests were two-sided and p ≤ 0.05 was considered significant. The data were evaluated descriptively using the arithmetic mean, SD, range and $95\%$ confidence intervals. ## Surgical Technique An anterolateral approach was used in 72 cases. Revision by a transfemoral approach after anterolateral joint exposure was performed in 17 hips. Allograft bone was used during operation at the femur for defect reconstruction of the tube in 17 cases. Strut-grafts with additional morselized allo- and/or autograft gained from the cup during reaming were implanted in four cases and morselized material only in 13. The MRP® revision system was used as a cementless device in all cases. A press-fit situation was approached for primary stability. All straight 140 mm ($$n = 26$$) and 200 mm ($$n = 3$$) stems were implanted after preparation of the femoral canal with rasps. All longer distal anchoring devices were used as curved stems (200 mm, $$n = 53$$; 260 mm, $$n = 6$$; 320 mm, $$n = 1$$) and preparation was performed with flexible reamers. ## 3. Results Mean follow-up period was 7.1 (range: 3.0–13.7) years and data were available for all revisions ($$n = 89$$). Five patients died after a mean duration of 7.2 (range: 3.3–11.6) years postoperatively. Data of these patients were included until their last follow-up. ## 3.1. Complications There were seven failures during follow-up, four due to breakage of taper junction and three infections. Preserving therapy was successful in another three infected hips. There were seven dislocations during follow-up and there was no influence of the used neck on the dislocation rate (standard neck $$n = 4$$; lateralized $$n = 3$$). Four were treated by open reduction and exchange of the cup. Another three cups were revised during follow-up due to aseptic loosening. One of them had a second cup revision after repeated loosening. ## 3.2. Risk Factors for Breakage of the Taper Junction Fracture of the taper junction occurred in four arthroplasties after a mean period after surgery of 4.3 (range: 2.8–5.5) years (Table 1). Experience of the surgeon showed no significant influence although two of the four failures occurred with one surgeon within the first seven revisions with this system. All but one case showed overweight with a mean of 37.9 (27.1, 32.7, 44.3 and 47.5) kg/m2. Obesity of grade 3 showed a significantly higher correlation of breakage (two out of four vs. two out of 85; Chi square test $$p \leq 0.0043$$). In all four cases, no lengthening piece but lateralized necks were used. Chi square test revealed significant higher risk with use of lateralized ($$n = 43$$) compared to standard necks ($$n = 46$$) ($$p \leq 0.0141$$). The type of postoperative defect according to the classification of Paprosky is given in Table 2. Postoperative defect showed no influence on stem breakage. At time of breakage all four patients showed a type 2 defect due to at least partial restoration according to Paprosky, but this was also not of significant influence on stem breakage during follow-up. ## 3.3. Survival Analysis Implant survival with stem breakage as the endpoint was 94.2 ($95\%$ CI: 89.6–$100\%$) at 13.7 years. Survival of the implant using a lateralized neck was significantly lower compared to systems built up with standard necks (Figure 2 and Table 3). BMI also had an influence on implant survival with breakage of the taper junction as the endpoint (Table 3). Experience of the surgeon, defect classification, defect regeneration or length or diameter of the distal anchoring piece had no influence on the risk of breakage of the stem. ## 3.4. Retrieval Analysis of Coupling Two of the four explants with breakage of the taper junction could be examined in vitro. The two explants showed identic failure patterns. Disconnection of the fractured proximal piece of one stem was performed by a universal material testing machine (MTS 858 Mini Bionix, MTS Systems, Eden Prairie, MN, USA). Traverse speed was 0.008 mm/sec. The force to disconnect the broken stem piece out of the neck was 15.06 kN. Photographs were made before (Figure 3) and after disconnection (Figure 4; twentyfold optical enlargement). Figure 5 shows the distal part of the broken taper after disconnection. The crack originated laterally and progressed in the medial direction, which is evident from the bright deposits (red arrow) in the area of crack initiation as well as occurring rest lines (yellow arrows). Residual fracture caused by overload is located medially, opposite from the crack origin. X-rays of the patient before and after revision of the broken device are given in Figure 6 and Figure 7. ## 4.1. Background and Rationale The use of modular endoprostheses has become popular in RTHA and after tumor resection for reconstruction of bone defects. Since its introduction nearly three decades ago, only a few studies have been published with a large cohort of patients and at least a mid-term follow-up duration [3,4,5,14,15,16,17]. One of the advantages of modularity may be the reconstruction of patients’ biomechanics with femoral head offset and leg length. In addition, an intraoperative or postoperative adaptation with exchange of the proximal components may be performed easily, while the well-fixed distal anchoring part can be left in situ. ## 4.2. Cumulative Risk of Stem Breakage Nevertheless, wear, fretting, cold welding and corrosion of the taper junctions of modular devices are described and have to be taken into account as recent publications have shown [6,18,19,20,21]. The risk for breakage due to the mentioned reasons may increase over the years, as it is a creeping phenomenon. This finding is also derivable from current studies dealing with identic modular devices, showing a decreasing survival through the years due to stem breakage [22]. There were four mechanical failures in this cohort by means of breakage of taper junctions between stem and neck after a mean of 4.3 (range: 2.8–5.5) years after implantation. Compared to the available data, an absolute rate of $4.5\%$ (4 out of 89) is higher than the described risks of $1.4\%$ (1 out of 70) or $3.6\%$ (6 out of 165) of comparable modular devices [11,23] and is not reported with the same implant in a comparable study [24]. There is one recent publication of the manufacturer of this implant, giving an absolute rate of mechanical failure by means of junction fracture of $0.3\%$ ($\frac{113}{37600}$) [9]. In the annual report of 2019 of the National Joint Register (NJR), stem breakage of $2.8\%$ as an indication for rerevision of the stem is published (National Joint Registry for England, Wales, Northern Ireland and the Isle of Man, 16th Annual Report, 2019). As a detailed analysis is not available, it remains unclear whether a monobloc or modular device cracked or in the latter the body or taper junctions failed. Nevertheless, an absolute number of $4.5\%$ in this study suggests a higher rate of junction cracks than reported even of the investigated system. Cumulative, and therefore, time-dependent risk of stem fracture may be more meaningful, as absolute numbers to estimate this mechanical problem but are not given in the current literature. A cumulative risk of $5.8\%$ after 13.7 years in this cohort is approximately as high as the failure risks for aseptic loosening or infection and should, therefore, cause major concerns to surgeons dealing with RTHA. Due to our data, central and independent registration of specific mechanical failure reasons is mandatory to estimate individual risks of each implant dependent on patients’ characteristics such as bone defect, BMI or offset variant. Additionally, correlation between other failure reasons, such as infection or aseptic loosening, may be possible. Finally, estimation of implant-specific risks must be known to develop an algorithm in patients undergoing femoral RTHA. ## 4.3. Lateralized Neck and Obesity As a Risk Factor Modularity and especially the possibility of different offset options have been proposed to have positive influence on functional outcome and complications such as dislocation. A dislocation rate of nearly $8\%$ ($\frac{7}{89}$) in this cohort corresponds with the rate of current literature of between 9 and $10\%$ [25,26]. In addition, the dislocation rate was not influenced by the used offset variant. We agree with Regis et al. that the use of modular necks with different offset variants alone may not lower the dislocation rate [27]. Adaption of leg length and offset in primary THA does not necessarily result in better functional results. We have observed a significantly higher risk of breakage of the taper junction in stems built up by a lateral offset neck ($$n = 4$$) compared to a standard variant ($$n = 0$$). Obesity of at least grade 1 had also had a significant influence on risk of breakage increasing with the grade of obesity (grade 1:3 out of 26 vs. 1 out of 63; grade 3:2 out of 4 vs. 2 out of 85). Due to these findings, the decision for a lateralized neck should be made carefully in obese patients of at least grade 1 (>30 kg/m2). Although not of significant influence, a defect of type 2 or 3 A according to Paprosky at time of operation was present in all fractured cases. This may be another factor to be considered when dealing with modular systems in RTHA. Factors such as a dislocation rate and risk of revision due to implant related complications in addition to overall implant survival may lead to the decision pro or contra modularity with use of a standard or lateralized offset [28]. ## 4.4. Further Factors Influencing Risk of Breakage and Technical Considerations Furthermore, a possible failure reason may be repeated connection of taper junctions as performed in one patient in this study (Table 1). This may be necessarily performed in patients presenting with instability to lengthen the system and/or adapt offset by exchanging the neck and/or inserting a lengthening piece. Especially in infections treated with a preservative regimen, e.g., vacuum therapy with instillation, risk for failure may increase as a couple of revisions with disconnection of the mobile parts of the system at any time are usually performed to clean the components mechanically until definitive restoration of the system by new parts at latest revision. Postoperatively, two patients showed a type 3A situation with a partial bone restoration during follow-up, leading to a type 2 defect according to Paprosky in all four cases at time of breakage. As bone restoration occurred within a couple of months, type 2 defects were present for a longer period of time in all four cases. Minor defects, such as a type 2 defect with rigid and osseointegrated fixation of the distal stem component and a defect situation beginning near the junction area up to the neck more proximally, may give a maximum mechanical stress to the junction area leading to fatigue stress after initial repeated bending. The two explants of this cohort showed identic failure patterns. The disconnection force exceeded 15 kN. Due to this finding and as other obvious failure reasons are missing, a technical error during implantation is unlikely, as the taper connection was tight. Repeated bending leading to fatigue stress at the junction may be the main reason for failure, although signs of corrosion were present. This is in accordance with the findings of Lakstein et al. [ 11] or Fink [10]. Due to these findings, an operative technique when using modular systems should be carefully adapted and a situation described above should be avoided. The use of longer necks leading to a more distal location of the junction with better bony support around it as well as additional bone grafting is proposed [12]. Nevertheless, a bony restoration is difficult to achieve especially after transfemoral approach or extended trochanteric osteotomy with highly deficient bony lids at the proximal femur. The use of (massive) allografts may also leave some space between bone and neck with persisting instability due to delayed or only at least partial ingrowth. A greater diameter with massive junction body and longer connection length may be the reason for a potentially lower risk of breakage in tumor devices. In contrast, the diameter of the junction is generally smaller and the length has been shortened during time at different revision devices [19]. Therefore, adaptation of length and diameter of the taper junction and/or a hardening of the material may also be an option but should be investigated in further studies [11,29]. A lateralized offset combined with obesity of at least grade 1 and a type 2 or type 3A defect at time of implantation (decreasing to a type 2 defect at breakage due to defect regeneration) are the main risk factors in this cohort. The use of monobloc devices especially in cases with limited bone defect of type 1 or 2 according to Paprosky may be an option and implants like the Wagner SL® (Zimmer Biomet, Warsaw, IN, USA) revision device may get certain a renaissance [2,30,31]. Nevertheless, breakage may also occur with monobloc devices with ingrown stems at the isthmus and a defect situation more proximally due to the same biomechanical reasons. As a potential increase may occur during follow-up due to mechanical stress for both monobloc and modular devices, further long-term data and research is necessary to assess the pros and cons of modularity in relation to monobloc devices within the context of defect reconstruction in revision hip or tumor arthroplasty. To prevent junction breakage, especially the technical challenges mentioned above have to be examined in detail and opposed to monobloc options, which has existing challenges due to its straight design. In the case of a broken stem element or revision of a well-integrated, distally fixed stem, exchange is challenging and even impossible without a transfemoral approach and further potential excessive bone damage. ## 4.5. Limitations The included number of patients is relatively low and the follow-up period is limited. Nevertheless, a consecutive series with 89 patients in a medium-term follow-up seems to be sufficient to answer the questions asked. As comparable studies with a relevant number of patients are missing, it remains unclear what cumulative fracture risk of other modular devices can be expected. We could not detect a negative selection of patients in this cohort. ## 5. Conclusions Although intra- and postoperative adaption to patient’s characteristics is easier to perform with a modular device, breakage of taper junctions appears and the risk may rise during follow-up. The use of lateralized offset necks of this implant in patients with an at least grade 1 obesity shows a significantly higher risk of breakage compared to the standard offset variant. There is a higher rate of junction cracks in this consecutive single institutional series than previously reported by the manufacturer of this system. Due to the lack of independent data sources such as registries or even long-term follow-up studies, the exact number of junction failures of used modular systems is difficult to estimate. Although technically more demanding, monobloc devices may be an option in patients that are at risk (type 2 defect, obesity and high offset). ## References 1. Bedair H.T.M., Choi H.R., Mayle R., Bashyal R., Abbot D., Eberhardt J., Sporer S., Della Valle C.. A comparison of modular tapered versus modular cylindrical stems for complex femoral revisions. J. Arthroplast. (2013) 28 71-73 2. Bohm P., Bischel O.. The use of tapered stems for femoral revision surgery. Clin. 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--- title: Whole-Exome Sequencing in Family Trios Reveals De Novo Mutations Associated with Type 1 Diabetes Mellitus authors: - Mira Mousa - Sara Albarguthi - Mohammed Albreiki - Zenab Farooq - Sameeha Sajid - Sarah El Hajj Chehadeh - Gihan Daw ElBait - Guan Tay - Asma Al Deeb - Habiba Alsafar journal: Biology year: 2023 pmcid: PMC10044903 doi: 10.3390/biology12030413 license: CC BY 4.0 --- # Whole-Exome Sequencing in Family Trios Reveals De Novo Mutations Associated with Type 1 Diabetes Mellitus ## Abstract ### Simple Summary Type 1 diabetes mellitus (T1DM) is a chronic autoimmune condition in which the immune system destroys insulin-making cells in the pancreas. Many advances have been made in the past decade to understand the pathophysiology of T1DM. With an estimated heritability risk of $50\%$, the strong genetic component plays an important role in the discovery of novel disease pathways and identification of new targets for therapeutic purposes. In this study, we aim to identify new (de novo) genetic markers for T1DM patients by sequencing the genes of the affected individual and their parents (trio family). This is a powerful approach to identify causal mutations for inherited diseases, such as T1DM, to improve our understanding of the condition. With 13 trio families, we identified 32 new (de novo) genetic mutations. Of these, 12 variants that were linked to T1DM, and the remaining 20 variants were linked to endocrine, metabolic, or autoimmune diseases. The findings of this study have allowed us to identify the genetic markers associated with the development of T1DM, to be able to improve diagnosis through therapeutic advancements. ### Abstract Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease characterized by insulin deficiency and loss of pancreatic islet β-cells. The objective of this study is to identify de novo mutations in 13 trios from singleton families that contribute to the genetic basis of T1DM through the application of whole-exome sequencing (WES). Of the 13 families sampled for this project, 12 had de novo variants, with Family 7 having the highest number (nine) of variants linked to T1DM/autoimmune pathways, whilst Family 4 did not have any variants past the filtering steps. There were 10 variants of 7 genes reportedly associated with T1DM (MST1; TDG; TYRO3; IFIHI; GLIS3; VEGFA; TYK2). There were 20 variants of 13 genes that were linked to endocrine, metabolic, or autoimmune diseases. Our findings demonstrate that trio-based WES is a powerful approach for identifying new candidate genes for the pathogenesis of T1D. Genotyping and functional annotation of the discovered de novo variants in a large cohort is recommended to ascertain their association with disease pathogenesis. ## 1. Introduction Type 1 diabetes mellitus (T1DM) is a chronic autoimmune disease primarily characterized by insulin deficiency and loss of pancreatic islet β-cells [1,2]. T1DM accounts for 5–$10\%$ of total cases of diabetes worldwide, and is one of the most common endocrine and metabolic conditions occurring in childhood. The peak age of presentation of childhood-onset T1DM has a bimodal distribution, with the first peak between four to six years, and the second in early puberty [3]. The pathogenesis of T1DM has been suggested to be a continuum development, starting from the detection of autoantibodies before symptom onset to the progression of β-cell destruction, dysglycaemia, and hyperglycemia [4]. T1DM is a heterogeneous multifactorial condition, and the elucidation of its complex etiology is highly dependent on the interaction between numerous environmental factors precipitated by genetic susceptibility. Environmental influences, such as lifestyle, viral infections (especially respiratory such as rotavirus, cytomegalovirus, and mumps or coxsackie B and other enterovirus infections), and gestational events have been proposed as candidate etiological factors. In individuals with genetic predisposition, exposure to these environmental triggers leads to an autoimmune response, mediated by autoreactive CD4 and CD8 T-cells, resulting in the destruction of insulin-producing beta cells [5]. Family studies have revealed that T1DM is heritable, with an estimated $50\%$ risk. The concordance rate among monozygotic twins is reported to be $30\%$ within 10 years of diagnosis of the first twin, reaching as high as $65\%$ by 60 years of age [6]. A child of an affected mother has a lifetime rise between 1 and $4\%$, whereas a child of an affected father has a 3–$8\%$ risk, and where both parents are affected, the risk reaches up to $30\%$ [7]. Linkage and genetic association studies have identified >50 loci that contribute to susceptibility or resistance to developing T1DM, including its association with pancreatic beta cell autoimmunity (insulin gene (INS)), inflammatory-associated factors (interleukin-2 signaling pathway, B-cell and T-cell development, and cytokine signaling), human leucocyte antigen (HLA) genes, and shared genetic architecture with autoimmune diseases (APS1 and STAT3 poly-autoimmunity) [8,9,10]. Despite the rapid advancements in genetic methods, our ability to understand the pathogenesis of T1DM to improve therapeutic or diagnostic potential is lacking. The increasing availability of whole-exome sequencing (WES) platforms has substantially improved the identification and investigation of the role of inherited and de novo variants in T1DM-associated genes. In this study, we implemented WES to identify variants in a cohort of 50 samples comprising 13 case–parent trios from singleton families with T1DM in the UAE. ## 2.1. Study Participants and Recruitment Thirteen families of UAE nationality were recruited, with at least one member clinically diagnosed with T1DM, as per the American Diabetic Association guidelines. There was a total of fifty individuals, in which 14 were T1DM patients. One family had two children with T1DM, while the rest only had one. Supplementary Figure S1 details the pedigree structure of the participants. All participants were provided with a questionnaire that included details on demographic information and medical history, such as age, gender, weight, height, waist circumference, systolic and diastolic blood pressure, family history of diseases, consanguineous marriages, and smoking status. For the 14 T1DM patients, biochemical tests were carried out, reporting glycosylated hemoglobin (HbA1c), hemoglobin, and white blood cell count (lymphocyte, monocyte). Saliva samples were collected from all subjects using the Oragene OGR-500 kit (DNA Genotek, Ottawa, ON, Canada). ## 2.2. DNA Extraction and Library Preparation Genomic DNA was extracted from the buccal cells in the saliva samples using the prepIT®L2P system (DNA Genotek, Ottawa, ON, Canada). The extracted DNA aliquots were quantified using the DS-11 FX Fluorometer (Denovix Inc. Wilmington, DE, USA), and the quality of each sample was assessed through agarose gel electrophoresis. Using the protocol recommended by the manufacturer of the Illumina TruSeq Exome Library Prep kit (Illumina Inc., San Diego, CA, USA), the libraries were prepared from the cleaned and sheared genomic DNA (gDNA). The indexed paired-end libraries were then quantified using the Denovix DS-11 FX Fluorometer to determine the optimal loading concentration of gDNA, providing the adequate clustering density on the flow cell during library sequencing. The fragment size was confirmed using the Advances Analytical Fragment Analyzer (Ankeny, IA, USA). Upon ensuring correct sizes and repeating incorrect runs, the libraries were loaded into NextSeq 500 (Illumina Inc., San Diego, CA, USA) separately. ## 2.3. Bioinformatics and Data Filtering Pipeline The data analysis pipeline was designed based on the best practices recommended by the Broad Institute’s Genome Analysis Tool Kit (GATK) instructions, v4.0.6.0 [11]. After subjection to quality assurance, assuming default parameter, the raw reads of samples in the FastQ format files were checked using FastQC software, v0.11.5 [12]. The Trimmomatic tool version 0.33.0 was used to clip all the reads containing Illumina adapters with default parameters for paired-end sequencing. The reads were trimmed from the 3’ end [13]. Using the Burrows-Wheeler Aligner (BWA) v0.7.12 (BWA-MEM), alignment results were generated for each family member by mapping the raw reads to the human reference genome (GRCh37/hg19) [14,15] with reads of 76 base pairs (bps) in length. The sorted BAM files from all the sample lanes were merged, and the duplicate reads were removed using Picard version 2.9.4 tool’s commands SortSam, MergeSamFiles, and MarkDuplicates tools, respectively [16]. The samples’ mapping quality was checked using the Qualimap v2.2.1 tool and verified for an average coverage above 45X [17]. Using BaseRecaliberator from the GATK toolkit, the qualities of the mapped bases were improved, and the variant file from the dbSNP database was used to mark the sites of known variation [18]. The variant calling was performed using Haplotypecaller by GATK in GVCF mode. The results were jointly genotyped using the GenotypeGVCF tool, yielding the final raw variant file [19]. The GATK’s variant recalibration was performed to obtain both SNPs and INDELs before combining them into a single VCF file. Variant annotation was performed using snpEff v4.3p (i.e., frameshift, nonsynonymous, splice variant, etc.). SnpSift was used to annotate the variants using population allele frequencies from gnomAD v2.1.1, 1000 Genomes, and ExAC [20]. The resulting VCF file with the single-nucleotide variants/polymorphisms (SNVs/SNPs) and insertions or deletions (INDELs) that passed the GATK filtering were stored in a GEMINI (GEnome MINIng, v0.12.2) database after decomposing and normalization steps [21]. The variants were then loaded into the GEMINI database with the family’s pedigree file as the input file. The familial analysis was performed with the GEMINI de novo and autosomal recessive commands. Each variant was annotated by comparing it to several genome annotations from the GEMINI database, including the GnomAD, ENCODE tracks, UCSC tracks, OMIM, ClinVar, dbSNP, KEGG, and HPRD. The identified variants were then visually inspected using the IGV tool. The analysis was then focused on SNVs, SNPs, and INDELs that had minor allele frequency < $5\%$ using gnomad_all, which we defined to be nonsynonymous, splice site, or missense SNPs. The impact information comes from snpEff through the GEMINI annotation that internally uses SIFT and PolyPhen to predict functional annotation. Only the variants that were repeated in two or more families were included in the next step of the analysis. Variants were classified as de novo if they were present in the affected child, but not in either of their parents. ## 2.4. Biological Functional Analysis The clinical significance, disease associations, and linked phenotypes of the variants were determined using the ClinVar database, GeneCard, NCBI, and online databases [22]. Then, the search converged on genes with relations to type 1 diabetes and all its known pathways and related genes. The genome-wide association study (GWAS) of T1DM was cross-checked with all the variants before and after being filtered [23]. The Human Protein Atlas (http://www.proteinatlas.org (accessed on 1 December 2022)) and the Genotype-Tissue Expression v7 (GTEx) tool were utilized to understand the functional roles, regulatory landscape of gene expression, and splicing variation in a broad selection of primary human tissues. The NHGRI-EBI Catalog of human GWAS (www.ebi.ac.uk/gwas (accessed on 1 December 2022)) was used to identify if an SNP had been identified in a global population, or if they were distinctive to the Emirati population. ## 3.1. Demographic Factors Of the 50 samples collected from the 13 families, there were 14 probands and 36 unaffected family members. All families had samples from an unaffected mother and unaffected father, and a subset (9 families) had samples of an unaffected sibling (Table 1). The mean age of the probands was 11.86 ± 5.08 years old, while the family members ranged from siblings to parents, and had a mean age of 31.46 ± 14.85 years old. There were nine males and five females in the proband group, whereas for the family members, 19 were male, and 17 were female. The average body mass index (BMI) of the probands was 16.94 ± 2.77, with a waist circumference of 64.92 ± 8.41, whereas the healthy family members had an average BMI of 27.46 ± 5.52, with a waist circumference of 87.82 ± 16.48. Nine out of thirteen families ($69.2\%$) had consanguineous marriages. Family history of the disease was also collected from the parents, and nine out of thirteen ($69.23\%$) had a history of dyslipidemia, eight ($61.54\%$) had a history of hypertension, five ($38.46\%$) had a history of type 1 diabetes, and two ($15.38\%$) had a history of hypothyroidism. The average HbA1c percentage was $9.12\%$ ± $1.88\%$, which was higher than the normal range (<$6\%$) for healthy individuals. The rest of the variables (hemoglobin: 13.13 ± 1.40; white cell count: 6.95 ± 4.00; lymphocytes: 5.87± 7.30; monocytes: 2.33 ± 3.58) were all within regular ranges and, thus, had no further significance. The lifestyle of the sample families was assessed for smoking habits, revealing $100\%$ of the probands never smoked, and $25\%$ of the family members never smoked. ## 3.2. De Novo Variants Only 1186 variants with high impact passed the filtering pipeline, as detailed in Supplementary Tables S1 and S2. After eliminating the variants with less than 0.05 minor allele frequency (MAF) and variants that were not found in 2 or more families, we had 98 variants. Finally, we filtered out the genes with no biological relevance to T1DM or autoimmune pathways/diseases, and were left with 30 rare variants that belonged to 20 different genes (Table 2). The genes found to be related to T1DM were ten variants across seven genes (MST1, rs201139286; TDG, rs760400700, rs764159587; TYRO3, rs746533465, rs750893216, rs757748573; IFIHI, rs141469634; GLIS3, rs113076411; VEGFA, rs750060813; TYK2, 19-10475177-TA-T). There were twenty variants across thirteen genes that were linked to other forms of diabetes (type 2 diabetes (T2D), gestational diabetes, diabetes retinopathy, and nephrogenic diabetes insipidus) or other autoimmune diseases (BCR, rs372013175; CACNA1B, 9-140773611-G-GACGACACGGAGCCCTATTTCATCGGGATCTT; CNN2, 19-1036442-C-A; COLGALT1, 19-17666649-G-A; LAMA3, 18-21338476-T-G; LGALS9C, rs376412531; MBD4, 3-129155546-CT-C; MST1L, rs11260920; MUC6, rs368342230, rs376177791, rs754249101, rs761220536, rs766751467, rs766833662; PABC1, rs140822921; RNASEH2B, rs200320729; ZNF596, rs756701581). The MST1L gene located in 1p36.13 is involved in the regulation of macrophage chemotaxis, and mainly expressed in the kidney, liver, and pancreas tissue. This will trigger an inflammatory response, which initiates insulitis and pancreatic cell death, leading to the production of IFNγ, TNFα, IL-1β, and the amplification of beta cell death cycle [24,25]. The IFIH1 gene located in 2q24.2 is an innate immune receptor that plays a major role in sensing viral infection and in the activation of a cascade of antiviral responses, including the induction of type I interferons and proinflammatory cytokines. The IFIH1 gene has been associated with the pathogenesis of diabetes (type 1 and type 2) and multiple autoimmune diseases [23,26,27,28,29,30,31,32,33,34,35,36]. MBD4, located in 3q21.3, is involved in DNA glycosylase and endonuclease activity, and is mainly expressed in lymphocytes. It is associated with obesity, BMI, sclerosis, cancer, and autoimmune disease [37,38,39,40]. The MST1 gene located in 3p21.31 is involved in the regulation of T cell selection, and its deficiency restores normoglycemia, improves beta cell function and prevents the development of diabetes [24,27,28,41]. The VEGFA gene located in 6p21.1 is mainly expressed in thyroid tissue, and has been associated with severe retinopathy in type 1 diabetes, and glomerular microvasculature in diabetes, specifically due to islet vessel density, alteration in expression of genes regulating islet blood flow, insulin deficiency, and inflammation in intra-islet endothelial cells [42,43,44]. The PAPBC1 gene located in 8q22.3 encodes a poly(A) binding protein and is associated with tumor progression [45,46]. The ZNF596 gene located in 8p23.3 is highly expressed in the brain and cerebellum [47]. Characterized by lesions in the central nervous system disseminated in time and space [48], ZNF596 has been reported to be involved in the pathogenesis of multiple sclerosis [49]. The CACNA1B gene located in 9q34.3 is involved in the N-type voltage-dependent calcium channel, which can cause beta cell dysfunction and death, and lead to both types 1 and 2 diabetes [50]. The CACNA1B gene has been associated with acute lymphoblastic leukemia and myeloid leukemia through the regulation of immune functions and leukocyte chemotaxis [51,52]. The GLIS3 gene located in 9p24.2 encodes a nuclear protein that is involved in the expression and development of pancreatic beta cells, and has been associated with neonatal diabetes, fasting blood glucose, type 2 diabetes, and congenital hypothyroidism [53,54,55,56]. GLIS3 plays a role in the generation of pancreatic beta cell viability and susceptibility to immune and metabolic-induced stress, such as proinflammatory cytokines and glucose oxidation [57]. The MUC6 gene located in 11p15.5 is expressed in the stomach and pancreas tissue, is associated with hypertrophic cardiomyopathy, and is involved in enhancing innate immune reactivity [58]. Glycosylation of MUC6 is found to upregulate the IL-17 response, which is found to be related to other immune-mediated diseases [59]. The TYK2 gene located in 19p13.2 is associated with the cytoplasmic domain of type I and type II cytokine receptors, and is a component of both the type I and type III interferon signaling pathways [60,61]. The TYK2 gene has a critical importance in the etiology of autoimmune and inflammatory diseases, specifically type 1 and type 2 diabetes, due to its association with the pancreatic β-cell-specific suppression of cytokine response including IFN [61,62,63,64,65]. The TDG gene located in 12q23.3 is induced by β cells and inflammatory mediators that play a key role in initiating the autoimmune response. Given that TDG enzyme control activates DNA demethylation, TDG gene expression was significantly upregulated in the IFN-α–treated islets and lymphocyte cells [66]. The RNASEH2B gene located in 13q14.3 is involved in the activation of the interferon pathway, leading to the infiltration of lymphocytes and mononuclear cells, and local chronic inflammation [67]. The TYRO3 gene located in 15q15.1 regulates immunoregulation, plays an important role in the inhibition of the Toll-like-receptor-mediated innate immune response, and is an essential regulator of immune homeostasis [68,69]. LGALS9C, located in 17p11.2, is involved in cytoplasmic intracellular functions, and controls AMP-activated protein kinase in response to lysosomal damage, which is caused by diabetes, immune responses, and obesity [70]. This gene is also involved in attenuating T-cell expansion, tumor microenvironment, and chronic infections [71,72]. LAMA3, located in 18q11.2, is associated with the binding to cells via a high-affinity receptor through embryogenesis. While LAMA3 is not associated with T1DM and is mainly expressed in lung tissue, it is associated with autoimmune diseases, immunologic isotypes, immune cytolytic activity, and ovarian cancer [73,74]. The CNN2 gene located in 19p13.3 is involved in the structural organization of actin filaments, playing a role in smooth muscle contraction. While it is not associated with type 1 diabetes, it is expressed in fibroblast cells and linked to the innate immune system, myometrial relaxation, and contraction pathways [75,76]. COLGALT1, located in 19p13.11, encodes collagen β (1-O) galactosyltransferase 1 (ColGalT1), and is associated with musculoskeletal defects, cerebral small vessel disease, and congenital porencephaly [77,78,79]. While the COLGALT1 gene is not associated with T1DM, it is correlated with autoimmune diseases as it could potentially antagonize the innate immune response [80]. The BCR gene in 22q11.23 acts as a GTPase-activating protein that encodes a novel serine/threonine kinase activity, and can be considered as a candidate tumor suppressor gene involved in meningioma pathogenesis and chronic myeloid leukemia [76,81]. The gene is expressed in the brain, endocrine, thyroid, and lymphoid tissue, which may trigger the intracellular signaling pathways leading to the expression of genes required for immune response [82,83]. ## 4. Discussion This study has identified genetic variants associated with the development of T1DM from 13 Emirati case–parent trios. Of the 13 families sampled for this project, 12 had reported de novo variants, with Family 7 having the highest number (nine) of variants linked to T1DM/autoimmune pathways, whilst Family 4 did not have any variants past the filtering steps. Two of the families (3 and 8) were consanguineously related by the third degree, and were the only families that were associated with the CNN2 gene, linked to the innate immune system and autoimmune diseases. Gene MUC6 had the highest number of variants (seven) associated with nine families, with two variants present in four families, two variants present in three families, and three variants present in two families. Gene MUC6 has been identified as a loss of function (LoF) variant in the UAE population that is common in the local population (AF > $5\%$), and rare in global population (AF < $1\%$, as per the gnomAD exon catalog) [84]. MUC6 has been associated with prostate carcinoma in the UAE population, hence promoting tumorigenesis [84,85]. The gene ontology annotations related to this gene include extracellular matrix structural organization, which may lead to glucose-induced endothelial damage and metabolic disturbances [86]. Sanger sequencing is recommended to identify the association of gene MUC6 with T1DM in the Emirati population. In comparison to global population, there were ten variants of seven genes reportedly associated with T1DM (MST1, rs201139286; TDG, rs760400700, rs764159587; TYRO3, rs746533465, rs750893216, rs757748573; IFIHI, rs141469634; GLIS3, rs113076411; VEGFA, rs750060813; TYK2, 19-10475177-TA-T). In addition to T1DM, the following genes were associated with other conditions from global GWAS analysis: the MST1 gene was associated with multiple chronic inflammatory disease, including inflammatory bowel disease, Crohn’s disease, psoriasis, and ulcerative colitis [28,87]; the TYRO3 gene was associated with the blood pressure, metabolic syndrome, and microalbuminuria [88,89,90]; the IFIH1 gene was associated with psoriasis, systemic lupus erythematosus, chronic inflammatory diseases, autoimmune thyroid disease, and congenital hypothyroidism [23,26,27,28,29,30,31,32,33,34,35,36]; the GLIS3 gene was associated with type 2 diabetes, chronic obstructive pulmonary disease, and asthma [56,91,92]; and the TYK2 gene was associated with systemic lupus erythematosus, psoriasis, inflammatory bowel disease, rheumatoid arthritis, and type 2 diabetes [30,36,93,94]. While not associated with T1DM, there were twenty variants of thirteen genes that were linked to other forms of diabetes (type 2 diabetes (T2D), gestational diabetes, diabetes retinopathy, and nephrogenic diabetes insipidus) or other autoimmune diseases (BCR, rs372013175; CACNA1B, 9-140773611- G-GACGACACGGAGCCCTATTTCATCGGGATCTT; CNN2, 19-1036442-C-A; COLGALT1, 19-17666649-G-A; LAMA3, 18- 21338476-T-G; LGALS9C, rs376412531; MBD4, 3- 129155546-CT-C; MST1L, rs11260920; MUC6, rs368342230, rs376177791, rs754249101, rs761220536, rs766751467, rs766833662; PABC1, rs140822921; RNASEH2B, rs200320729; ZNF596, rs756701581), when compared to the global population. The following genes were associated with other conditions from GWAS analysis from global databases: the CACNA1B gene was associated with acute myeloid leukemia [51]; the LAMA3 gene was associated with ovarian cancer [74]; the MBD4 gene was associated with systemic sclerosis [38]; the MUC6 gene was associated with hypertrophic cardiomyopathy and peptic ulcer disease [58,95]; and the RNASEH2B gene was associated with rheumatoid arthritis and prostatic hyperplasia [96,97]. Given that T1DM is a multifactorial autoimmune endocrine disease, several susceptible genes may be shared across different conditions [23,98,99,100]. These findings increase our understanding of the genetic contribution and biology underlying T1DM development, and suggest overlapping genetic origins with autoimmune disease and other forms of diabetes. Several limitations affect the power of trio-based genetic analysis, including locus heterogeneity, sample size, pedigree structure, and genotype accuracy. Given the low sample size, widespread mutational recurrence and heterogeneity may be present, hence a larger sample size is required to confirm the de novo genetic findings. Family 5 had weak coverage and mapping quality in the father’s sample; therefore, variants identified in Family 5 should be considered with caution, which includes rs372013175 of BCR, 19-1036442-C-A of COLGALT1, rs200320729 of RNASEH2B, and rs746533465 of TYRO3. Additional functional studies in cell culture and animal models, as well as re-sequencing the identified genes in larger cohorts, are needed to assess the pathogenicity of the genes to T1DM. Since geographic isolation and consanguinity-driven genomic homozygosity may lead to the enrichment of founder mutations in specific ethnic groups, the presence of such rare mutations in our cohort from consanguineous families is not surprising. Therefore, further studies must be conducted in different ethnic groups to further understand the genetic landscape of T1DM. ## 5. Conclusions We have identified de novo genes that may play a role in the pathogenesis of T1DM by conducting whole-exome sequencing on 13 trios from singleton Emirati families with T1DM. As per published GWAS, all the identified genes were either associated with T1DM or were associated with an autoimmune disease. The susceptibility loci for T1DM are heterogeneous, hence further work around identifying such genes may play a robust role in the pathogenesis of T1DM. Future studies can also assess the association of genes with different modes of inheritance, such as autosomal recessive, autosomal dominant, and X-linked. This can aid in understanding the linkage to disease pathogenesis on a wider spectrum, and may offer more relationships between non-HLA genes and T1DM among the citizens of the UAE. 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--- title: Swietenine Alleviates Nonalcoholic Fatty Liver Disease in Diabetic Mice via Lipogenesis Inhibition and Antioxidant Mechanisms authors: - Kit-Kay Mak - Shiming Zhang - Jestin Chellian - Zulkefeli Mohd - Ola Epemolu - Albena T. Dinkova-Kostova - Madhu Katyayani Balijepalli - Mallikarjuna Rao Pichika journal: Antioxidants year: 2023 pmcid: PMC10044919 doi: 10.3390/antiox12030595 license: CC BY 4.0 --- # Swietenine Alleviates Nonalcoholic Fatty Liver Disease in Diabetic Mice via Lipogenesis Inhibition and Antioxidant Mechanisms ## Abstract Our previous studies have reported the effect of swietenine (a major bioactive component of Swietenia macrophylla seeds) in reversing and potentiating the effect of metformin in hyperglycemia and hyperlipidaemia in diabetic rats. Moreover, we reported that the anti-inflammatory effect of swietenine is mediated via the activation of nuclear factor erythroid 2-related factor 2 (Nrf2). This study evaluated the effect of swietenine and its mechanisms in nonalcoholic fatty liver disease (NAFLD) in high-fat diet/streptozotocin-induced diabetic mice. The effect was assessed by determining blood biochemical parameters (glucose, cholesterol, triglycerides, alanine transaminase (ALT), asparate transaminase (AST), alkaline phosphatase (ALP), glutathione (GSH), total antioxidant capacity (TAC), and malondialdehyde (MDA)) and liver biochemical parameters (liver index, cholesterol, and triglycerides). Hepatic lipid accumulation (initial causative factor in NAFLD) was determined by oil-O-red staining. Gene expression (qPCR) and immunohistochemical studies were performed to elucidate the mechanism of swietenine’s effect in NAFLD. The critical regulators (genes and proteins) involved in lipogenesis (ACLY, ACC1, FASN, SREBP1c, and ChREBPβ) and oxidative stress (Nrf2, NQO-1 and HO-1) pathways were determined. In mice fed with a high-fat diet followed by streptozotocin injection, the liver cholesterol, triglycerides, and lipids were elevated. These increases were reversed by the oral administration of swietenine, 80 mg/kg body weight, on alternate days for eight weeks. Gene expression and immunohistochemical studies showed that swietenine reversed the elevated levels of crucial enzymes of lipogenesis (ACLY, ACC1 and FASN) and their master transcription factors (SREBP1c and ChREBPβ). Furthermore, swietenine activated the Nrf2 antioxidant defense mechanism, as evidenced by the upregulated levels of Nrf2, NQO-1, and HO-1. It is concluded that swietenine shows beneficial effects in diabetes-induced NAFLD via inhibiting lipogenesis and activating the Nrf2 pathway. ## 1. Introduction The liver plays a central and crucial role in regulating glucose metabolism [1], fatty acid metabolism [2], and oxidative stress [3]. Dysregulated hepatic antioxidant status and metabolism of fatty acids and glucose cause liver damage, leading to nonalcoholic fatty liver disease (NAFLD) [4,5,6,7,8,9,10,11,12]. The development of fatty liver disease without excessive alcohol consumption is called NAFLD, one of the most common liver diseases accounting for nearly $30\%$ of liver disease worldwide [13,14,15,16,17,18,19,20,21]. NAFLD is a metabolic disease in which elevated plasma triglycerides and low-density lipoproteins are the hallmarks [22]. Patients with NAFLD have significantly increased mortality because of both hepatic (such as cirrhosis and hepatocellular carcinoma) and extrahepatic complications (metabolic syndrome, cardiovascular disease, and malignancy) [23]. The development of NAFLD strongly correlates with diabetes, as > $90\%$ of obese patients with diabetes also have NAFLD [24]. The continued production of triglycerides by the liver and the concomitant failure to suppress glucose production lead to hyperglycaemia, hyperlipidaemia, and hepatic steatosis [25]. Triglycerides are synthesized in the liver via the esterification of fatty acids, which have different origins. Hepatic fatty acids can result from local synthesis from acetyl CoA, but they can also result from direct uptake from plasma [26]. Abnormal fat deposition, increased hepatic enzyme activities, hepatic fibrosis, and liver cirrhosis are representative liver abnormalities in NAFLD associated with diabetes [25]. In diabetes, glycolysis provides carbons for de novo lipogenesis, which is under the control of various enzymes [27,28,29]; Adenosine triphosphate citrate lysase (ACLY), Acetyl-CoA carboxylase 1 (ACC1), Fatty acid synthase (FASN), carbohydrate response element binding protein β (ChREBPβ), and sterol regulatory element binding protein 1c (SREBP1c). Multiple studies have shown the protective role of nuclear factor erythroid 2-related factor 2 (Nrf2), a master regulator of antioxidative status, in the etiology and progression of NAFLD [30,31,32,33]. Currently, antidiabetic and antihyperlipidemic drugs are being used to prevent the symptoms of NAFLD [34]. No definitive pharmacological treatment has been approved for the treatment of NAFLD [35]. Streptozotocin is reported to cause liver toxicity, with similar characteristics observed in NAFLD [36]. There is evidence that ethnopharmacology, the scientific exploration of traditional medicinal plants, has provided lead compounds for treating various diseases [37]. In the literature, many plants and their bioactive compounds are reported to possess excellent antidiabetic [38] and antihepatotoxic [39] activities. Swietenia macrophylla seeds are used in traditional medicine to treat diabetes, and the bioactive compound responsible for the antidiabetic activity is swietenine, a nortetratriterpenoid [40]. Many scientific studies have demonstrated the antidiabetic activities of S. macrophylla [41,42,43,44]. We have recently reported that swietenine activates Nrf2 [40] and potentiates metformin’s antidiabetic activity [45]. This study aims to investigate the effect of swietenine on crucial regulators of de novo lipogenesis, fatty acid oxidation, and oxidative stress, which play a vital role in the development of NAFLD in streptozotocin-induced diabetic mice. ## 2.1. Animal Studies Our previous studies reported that swietenine at 40 mg/kg body weight showed a significant reversal of hyperglycaemia and hyperlipidaemia in streptozotocin-induced diabetic rats [45]. C57BL/6J mice were used as the experimental animals in this study. The dose was calculated by applying a correction factor (Km) [46,47]. The dose of swietenine used in this study was 80 mg/kg body weight. The experimental protocol was approved by International Medical University (IMU)-Joint Committee on Research and Ethics (Ref: IMU R$\frac{143}{2014}$). The animals were taken care of following IMU’s animal care guidelines. Male C57BL/6J mice (weight range 18–22 g, 6 weeks of age) were purchased from University Putra Malaysia and acclimatized for 2 weeks to the experimental conditions by housing in IMU animal house (maintained at $\frac{12}{12}$ h light/dark cycle, temperature 25 ± 3 °C, 45 ± $5\%$ humidity) with ad libitum access to a standard pellet diet (Altromin 1324, Altromin GmbH, Lage, Germany) and drinking water. Type2 diabetes was induced by feeding the mice with a high-fat pelleted diet ($70\%$ energy from $42\%$ fat, Altromin C 1090-70, Altromin GmbH, Lage, Germany) for 3 weeks, followed by a single intraperitoneal injection (60 mg/kg body weight, dissolved in freshly prepared 0.05 M citrate buffer, pH 4.5) of streptozotocin (Sigma Chemical Co., St. Louis, MO, USA). Blood glucose levels were measured one week after the streptozotocin injection. The mice whose fasting (overnight) blood glucose levels were greater than 150 mg/dL were divided into diabetic control (Group D, 10 mice fed with a high-fat diet for 8 weeks) and treatment group (Group S, 10 mice fed with a high-fat diet and oral administration of swietenine 80 mg/kg body weight on alternate days for 8 weeks). The normal control mice (Group N, 10 mice) were fed with a standard pellet diet for 8 weeks. At the end of the experiment, the mice were anaesthetised with ketamine and sacrificed by cervical dislocation. Blood was collected by cardiac puncture, and liver organs were excised for histological, gene expression, and immunohistochemistry studies. ## 2.2. Biochemical Studies The serum biochemical parameters: glucose, cholesterol, triglycerides, alkaline phosphatase (ALP), asparate transaminase (AST), and alanine transaminase (ALT) were performed on the ‘Siemens Dimension Xpand Plus integrated chemistry system’ with software version 10.1.2 (Siemens Healthcare Diagnostics, Inc.) as described in our previous paper [45]. Glutathione (GSH), malondialdehyde (MDA), and total antioxidant capacity (TAC) in serum were determined using respective assay kits from Sigma-Aldrich (Reduced Glutathione (GSH) Assay Kit (MAK364), Lipid Peroxidation (MDA) Assay Kit (MAK085) and Total Antioxidant Capacity Assay Kit (MAK187) following the manufacturer’s instructions. The mice’ body and liver weights were determined to calculate the liver index =liver weightbody weight×$100\%$. The cholesterol and triglycerides levels in liver homogenates were also determined. ## 2.3. Histological Studies The histology of the liver tissues was examined using Oil-O-Red (Abcam, #ab223796, USA) staining techniques. The Oil O Red stains lipid droplets bright red and is routinely used to determine lipid accumulation in the tissues [48]. The frozen tissues were cut into 5-μm thick sections, fixed with $4\%$ paraformaldehyde at 4 °C for 30 min, and then washed with phosphate buffer saline and $60\%$ isopropanol. The liver sections were stained with Oil Red O stain for 1 h at room temperature and then washed with $60\%$ isopropanol, followed by PBS. The tissue slides were cleared with xylene and were mounted with a coverslip using a DPX mounting medium (histological grade, # 06522, Sigma Aldrich, Saint Louis, MO, USA). The histology of the tissue sections was observed under Nikon Eclipse 80i Microscope (magnification, ×400). The results were analyzed in three randomly selected fields of view in each section using the panoramic scanner (3DHISTECH Ltd., Hungary). The average densities of collagen fibres (Masson’s trichome staining) and fat droplets (Oil O Red staining) were calculated. Oil O Red staining was carried out in the dark. ## 2.4. Real-Time Quantitative PCR (RT-qPCR) Assay The liver tissues were homogenized in liquid nitrogen, and total RNA was extracted ($$n = 10$$ per group) with QIAzol® (Qiagen, Austin, TX, USA) according to the manufacturer’s protocol. The concentration and purity of RNA were measured using an Ultra-Micro UV Visible Spectrophotometer (TECAN Infinite M200 Pro). RNA samples (100 ng) were dissolved in DNase/RNase-free water (Thermofisher, Waltham, MA, USA). ReverTra Ace® qPCR RT Master Mix Kit was used to synthesize first strand cDNAs according to the manufacturer’s instructions as follows: 37 °C for 15 min, 50 °C for 5 min, 98 °C for 5 min. The qPCR primers used in the present study (obtained from Integrated DNA Technologies, USA) were presented in Table 1. qPCR was performed using THUNDERBIRDTM Next SYBR ® qPCR Mix (Toyobo STC CO., LTD. Osaka, Japan) and an Applied CFX96 Touch Real-Time PCR Detection System (Bio Rad Laboratories, Inc., California, CA, USA). The PCR cycling conditions were 95 °C for 1 min, 40 cycles of 95 °C for 15 s, 60 °C for 25 s, and 72 °C for 45 s. The reference gene, β-actin, was used as a reference gene for the normalization of target gene expression, and the relative expression of genes was determined using the 2−ΔΔCt method [49]. ## 2.5. Immunohistochemical Studies The paraffin-embedded liver tissues were cut into 4-μm thick sections using a rotary microtome and allowed to float on warm water. The immunohistochemistry (IHC) protocol published in the literature elsewhere was followed [50,51]. The tissue sections were [1] transferred onto IHC microscope slides (FLEX, Agilent), [2] dried at room temperature, [3] deparaffinised with xylene, [4] hydrated with $100\%$, $95\%$, $70\%$, and $50\%$ ethanol sequentially, [5] incubated at room temperature in $3\%$ hydrogen peroxide (#H1009, Sigma Aldrich, Saint Louis, MO, USA) solution in methanol at room temperature for 10 min, [6] rinsed with PBS, [7] placed in staining dishes (Thermo Fisher Scientific, Waltham, MA, USA), [8] incubated with citrate buffer (10 mM, pH 6.0) at 95 °C for 10 min, [9] cooled down to room temperature, [10] washed with PBS, [11] incubated the with blocking buffer (100 μL, $10\%$ foetal bovine serum (Tico, Europe) in PBS) at 25 °C for 1 h, [12] incubated with the primary antibody (antibodies were purchased from Abcam), at 25 °C for 1 h, [13] washed with PBS, [14] incubated with biotinylated secondary antibody (Abcam (ab64256) at 25 °C for 1 h, [15] washed with PBS, [16] incubated in the dark with Streptavidin-Horseradish Peroxidase (HRP, # ab7403, Abcam, Cambridge, UK) conjugates at 25 °C for 1 h, [17] washed with PBS, [18] supplemented with freshly prepared 2,4′-dihydroxyacetophenone dioxygenase (DAB) substrate kit (Abcam (ab64238), [18] washed with PBS, [19] counterstained with haematoxylin for 5 min, [20] rinsed with distilled water, [21] dehydrated with $95\%$ and $100\%$ ethanol, [22] dipped in xylene, and [23] mounted with a coverslip using a mounting medium (Abcam (ab64320), USA). The slides were observed under the microscope (Nikon Eclipse Ts2-FL, Nikon Instruments Inc. New York, NY, USA)), and the colour intensity was quantified (6 images from random areas of interest at 400X from each tissue) using the software ImageJ Fiji (version 1.2; WS Rasband, National Institute of Health, Bethesda, MD, USA) following the protocol reported in the literature [52,53,54]. ## 2.6. Statistical Analysis Results are presented as mean ± standard deviation (SD) of six readings. The difference between the two groups was determined using one-way ANOVA followed by Dunnett’s multiple comparisons tests. GraphPad Prism version 9.0.1 for Windows, GraphPad Software, San Diego, CA, USA, was used for performing the statistical analysis. $p \leq 0.05$ is considered statistically significant. ## 3.1. Effect of Swietenine on Biochemcial Parameters The effect of Swietnine on serum and liver biochemical parameters is shown in Figure 1. The blood glucose, cholesterol, and triglyceride levels were elevated in diabetic mice (209.9 ± 7.71, 62.33 ± 6.22, and 158.7 ± 7.94 mg/dL, respectively) compared to control mice (78.36 ± 6.63, 40.67 ± 5.01, and 72.83 ± 4.31 mg/dL, respectively). Swieteine reversed the elevated blood glucose, cholesterol, and triglycerides levels to 98.67 ± 9.11, 49.00 ± 5.06, and 81.83 ± 3.55 mg/dL, respectively. The ALT, AST, and ALP levels in normal control mice were 28.83 ± 1.17, 70.08 ± 1.20, and 68.25 ± 3.95 IU/L, respectively. These levels were increased to 96.00 ± 4.94, 137.80 ± 5.19, and 169.00 ± 4.43 IU/L, respectively, in diabetic mice, and the increased levels were reversed by swietenine to 41.67 ± 6.05, 77.33 ± 5.24, and 76.00 ± 3.58 IU/L, respectively. The levels of antioxidant markers (GSH and TAC) in blood were lowered in diabetic mice (0.71 ± 0.18 and 143.90 ± 17.36 nmol/μL, respectively) compared to normal control mice (2.41 ± 0.21 and 364.10 ± 6.74 nmol/μL, respectively). Treatment with swietenine reversed the elevated levels of GSH and TAC to 2.02 ± 0.08 and 310.50 ± 17.65 nmol/μL, respectively. The level of oxidative stress marker, MDA, was increased in diabetic mice (7.98 ± 2.74 nmol/μL compared to 2.75 ± 0.11 nmol/μL), which was reversed upon treatment with swietenine (3.45 ± 0.49 nmol/μL). The liver index (%) was 2.06 ± 0.01 in normal mice, which was increased to 3.98 ± 0.25 in diabetic mice, and upon treatment with swietenine, it was reversed to 2.63 ± 0.26. The liver cholesterol and triglycerides levels were elevated in diabetic mice (18.00 ± 1.27 and 93.83 ± 5.71 mg/dL, respectively) from normal mice, in which the levels were 6.83 ± 1.17 and 20.50 ± 1.87 mg/dL, respectively. Swietenine treatment reversed the levels to 9.50 ± 1.05 and 26.83 ± 3.54 mg/dL, respectively. ## 3.2. Effect of Swietenine on Fat Accumulation NAFLD is the common cause of chronic liver disease under diabetic conditions. The effect of swietenine treatment on hepatic lipid homeostasis was assessed by quantifying the lipid content using Oil Red O staining. In diabetic mice (D), the neutral fat droplets were significantly increased (from $0.99\%$ ± 0.24 in normal mice to $29.29\%$ ± 5.94 in diabetic mice). Swietenine treatment reversed the elevated levels of neutral fat droplets (from $29.29\%$ ± 5.94 in diabetic mice to 10.30 ± 1.38). The results are shown in Figure 2, and these findings suggest that swietenine regulates hepatic lipid homeostasis in diabetes and helps prevent NAFLD development. ## 3.3. Effect of Swietenine on Lipogenesis Enzymes and Regulators Three enzymes, ATP citrate lysase (ACLY), acetyl CoA carboxylase isoform 1 (ACC1), and fatty acid synthase (FASN), are the key enzymes involved in the de novo lipogenesis [55]. The first step in lipogenesis is the conversion of citrate to acetyl-CoA, catalysed by ACLY. Then, ACC1 carboxylates acetyl-CoA to malonyl-CoA, from which fatty acids are synthesized by FASN [29]. ACLY is a crucial lipogenic enzyme that catalyzes an ATP-consuming reaction to generate acetyl-CoA from citrate, and acetyl-CoA is the critical building block for de novo lipogenesis [56]. ACC is a rate-limiting enzyme for de novo lipogenesis that catalyzes the synthesis of malonyl-CoA, a substrate for fatty acid synthesis and the regulator of fatty acid oxidation [57]. FASN catalyzes the de novo lipogenesis by synthesizing long-chain fatty acids from acetyl-CoA and malonyl-CoA [58]. Sterol regulatory element-binding protein-1c (SREPB1C) is a master transcription regulator of the enzymes involved in de novo lipogenesis. Its expression in diabetes is elevated in response to increased insulin levels [59]. Carbohydrate-responsive element-binding protein (ChREBPβ) is another transcription regulator of the enzymes involved in de novo lipogenesis. Its expression in diabetes is elevated in response to increased glucose levels [59]. qPCR and immunohistochemistry studies were carried out to study the effect of swietenine on genes, transcription factors, and proteins involved in de novo lipogenesis. The gene expression study revealed the upregulation genes of three key enzymes (ACLY, ACC1, and FASN) in diabetic mice, and swietenine treatment reversed the elevated levels (Figure 3A–C). The levels of ACLY, ACC1, and FASN in diabetic mice were 1.04 ± 0.13, 34.48 ± 5.22, and 9.77 ± 0.76, respectively, whereas these levers were reduced to 0.49 ± 0.08, 10.47 ± 0.82, and 3.35 ± 0.31, respectively, in swietenine-treated diabetic mice. In addition, the transcriptional lipogenesis regulatory genes (SREPB1c and ChREBPβ, Figure 3D,E) were upregulated in diabetic mice, and these levels were reversed upon treatment with swietenine. The levels of SREPB1c and ChREBPβ in diabetic mice were 17.07 ± 0.54 and 7.96 ± 0.56, respectively, whereas their levers in swietenine-treated diabetic mice were 9.46 ± 0.19 and 1.90 ± 0.14, respectively. Immunohistochemical studies revealed the upregulation of all three key enzymes (ACLY, ACC1, and FASN) involved in the de novo lipogenesis in diabetic mice, and swietenine treatment reversed the elevated levels (Figure 4). ACLY, ACC1 and FASN in diabetic mice were 3.88 ± 0.24, 7.15 ± 0.33, and 26.59 ± 2.82, respectively, whereas the levels in swietenine-treated diabetic mice were 1.13 ± 0.06, 3.02 ± 0.26, and 2.28 ± 0.19, respectively. In addition, immunohistochemical studies also revealed the upregulation of two transcriptional regulators (SREBP1c and ChREBPβ) in diabetic mice, which are reversed upon treatment with swietenine (Figure 4). SREBP1c and ChREBPβ levels in diabetic mice were 16.91 ± 1.53 and 48.84 ± 3.38, respectively, whereas the levels in swietenine-treated diabetic mice were 4.69 ± 0.57 and 16.22 ± 1.12, respectively. ## 3.4. Effect of Swietenine on Crucial Regulators of Oxidative Stress The transcription factor Nrf2 is a master regulator of adaptive response to oxidative stress and is reported to play a vital role in alleviating NAFLD [60]. The Nrf2 transcriptional targets include NADPH quinone oxidoreductase 1 (NQO-1) and heme oxygenase-1 (HO-1), which play roles in antioxidative responses that counteract the effects of oxidative stress [61]. Several reports have shown that the pharmacological activation of Nrf2 in the liver reduced liver lipid levels [62,63,64]. Our previous studies (in vitro) reported that swietenine activates Nrf2 [40]. In this study, qPCR and immunohistochemical analyses were carried out to determine the expression of Nrf2, NQO-1, and HO-1 genes and proteins. Gene expression studies revealed that the mRNA levels for all these three genes were elevated in diabetic mice, and their levels were further increased upon treatment with swietenine (Figure 5). The mRNA levels for Nrf2, NQO-1, and HO-1 in diabetic mice were 1.80 ± 0.48, 3.94 ± 0.74, and 1.85 ± 0.12, respectively, whereas their levels in swietenine-treated diabetic mice were 4.19 ± 0.40, 3.87 ± 0.39, and 3.82 ± 1.01, respectively. Immunohistochemical studies revealed the levels of Nrf2, NQO-1, and HO-1 in diabetic mice were upregulated (fold increase compared to control, Figure 6) by 2.53 ± 0.03, 1.59 ± 0.01, and 1.82 ± 0.02 whereas swietenine treatment further upregulated (fold increase compared to control) by 5.34 ± 0.05, 5.91 ± 0.34, and 5.39 ± 0.25, respectively. ## 4. Discussion The association between NAFLD and diabetes is bidirectional. Diabetes causes NAFLD and leads to nonalcoholic steatohepatitis (NASH), liver cirrhosis and liver cancer. In contrast, NAFLD increases the risk of diabetes development [65]. De novo lipogenesis and oxidative stress are the characteristics of NAFLD [66,67]. The key enzymes involved in lipogenesis are ACLY, ACC1 and FASN, and these enzymes are regulated by two master transcription regulators, SREBP1c and ChREPBβ [68]. In addition, lipogenesis and diabetes are also associated with oxidative stress, which is a crucial factor in the progression of NAFLD to NASH and HCC [69]. Nrf2 is a master regulator of the antioxidant defense system against the toxic effects of endogenous and exogenous oxidants. Many studies have highlighted the benefits of Nrf2 activators in diabetes and NAFLD [30]. In our previous work, we reported the antihyperglycaemic, antihyperlipidaemic, and antioxidant effects of swietenine and its synergistic effects with metformin in diabetic rats [45]. Moreover, we have reported that the anti-inflammatory effect of swietenine is mediated via Nrf2 activation [40]. Based on the above-said findings, we hypothesized that swietenine (at the dose of 80 mg/kg b.w.) exhibits beneficial effects in diabetes-induced NAFLD via reversing the 1) upregulated expression of critical enzymes involved in de novo lipogenesis (ACLY, ACC1, and FASN) and their transcription factors (SREBP1c and ChREPBβ), and 2) activation of the Nrf2 pathway. Feeding C57BL/6J mice with HFD followed by streptozotocin injection exhibited the symptoms of NAFLD; 1) elevated liver cholesterol, and triglycerides levels, 2) increased lipid accumulation, and 3) increased ratio of liver to body weight (liver index). Oral administration of swietenine (80 mg/kg b.w.) on alternate days for eight weeks reversed the symptoms of NAFLD in the liver. Gene expression and immunohistochemical studies have shown that swietenine down-regulates the critical enzymes (ACLY, ACC1, and FASN) of lipogenesis, the master regulators (SREBP1c and ChREPBβ) of lipogenesis enzymes, and critical regulators of antioxidant defense mechanism (Nrf2, NQO-1, and HO-1). Notably, we have previously shown that genetic or pharmacological Nrf2 activation downregulates fatty acid synthesis and upregulates fatty acid oxidation [70,71,72]. Moreover, our previous studies reported that swietenine was stable in liver microsomes [40], suggesting that the bioactivity observed in this study is because of the swietenine itself. Swietenia macrophylla seeds are used in folk medicine to maintain health and treat various diseases such as diabetes, hypertension, inflammation, sexual dysfunction etc. [ 73]. Various herbal supplements (such as coffee, oil, capsules, extract, etc.) containing S. macrophylla were developed and are available in the market. Some supplements have received a patent and approval from the Ministry of Health Malaysia https://news.utm.my/ms/$\frac{2021}{04}$/goswiet-after-7-years-of-research-swietenia-mahagoni-attracted-diabetic-consumer/ accessed on 7 February 2023). Many researchers in Malaysia are researching S. macrophylla seeds to explore their medicinal value. Our survey found that the general public suffering from diabetes consumes S. macrophylla seeds and prescription medicines together. Our previous study showed that swietenine potentiates the effect of metformin in diabetic rats [45]. In continuation of our previous studies [40,45,74] on the most bioactive compound of S. macrophylla seeds, swietenine, we have investigated the activity of swietenine in diabetes-induced NAFLD. Although our studies have shown promising bioactivity, there are limitations in the study design (because of time and financial constraints): [1] NAFLD is a multifactorial disease [75], and there is no single physiologically relevant animal model [76]. Thus, future studies must be carried out to confirm the bioactivity of swietenine in other animal models. [ 2] We could not be able to perform the pharmacokinetics studies in this study, and pharmacokinetics is the critical element to determine the dose and dosage of swietenine for its consumption. Thus, future studies must be carried to determine the pharmacokinetics of swietenine. [ 3] The general public consume S. macrophylla seeds either as-it-is or in powder form or capsule form and the seeds contain many bioactive compounds and nutrients in addition to swietenine (the bioactivity could also be contributed by those compounds and nutrients). Thus, future studies should be focused to determine the effect of whole seeds powder (we have attempted to determine the bioactivity of the seeds powder but we were unsuccessful because of challenges associated with administration of seeds powder to animals). [ 4] Since the people consume the seeds and did not report toxic effects, in our opinion, it is not advisable to assume its safety without confirming its safe use scientifically. Thus, detailed safety studies should be carried out to confirm its safe use for therapeutic interventions. ## 5. Conclusions From the results of this study, it is concluded that swietenine has shown encouraging beneficial effects in a diabetes-associated NAFLD animal model. Swietenine reversed the hyperglycaemia-induced lipogenesis and oxidative stress. Switenien reversed the elevated levels of blood glucose, cholesterol, and triglycerides in blood and liver, hepatic function markers (ALT, AST, and ALP) in blood, and regulated the oxidative stress markers (glutathione, total antioxidant capacity, and malonaldehyde). The lipogenesis inhibitory activity of swietenine was confirmed using histological studies (Oil-O-Red staining) and gene and protein (ACLY, ACC1, FASN, SREBP1c and ChREPBβ) expression studies. The ability of swietenine to upregulate the master regulator of oxidative stress (Nrf2) is also confirmed using gene and protein expression (NRF2, HO-1 and NQO1) studies. Thus, biochemical, gene expression and protein expression studies have demonstrated the bioactivity of swietenine in diabetes-induced NAFLD. However, future studies should be conducted to determine the bioactivity and pharmacokinetics of swietenine in other NAFLD animal models to confirm the activity of swietenine. 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--- title: Effect of Extracellular Matrix Stiffness on Candesartan Efficacy in Anti-Fibrosis and Antioxidation authors: - Tong Zhu - Jingjing Song - Bin Gao - Junjie Zhang - Yabei Li - Zhaoyang Ye - Yuxiang Zhao - Xiaogang Guo - Feng Xu - Fei Li journal: Antioxidants year: 2023 pmcid: PMC10044920 doi: 10.3390/antiox12030679 license: CC BY 4.0 --- # Effect of Extracellular Matrix Stiffness on Candesartan Efficacy in Anti-Fibrosis and Antioxidation ## Abstract Myocardial fibrosis progression and imbalanced redox state are closely associated with increased extracellular matrix (ECM) stiffness. Candesartan (CAN), an angiotensin II (Ang II) receptor inhibitor, has shown promising anti-fibrosis and antioxidant efficacy in previous cardiovascular disease studies. However, the effect of ECM stiffness on CAN efficacy remains elusive. In this study, we constructed rat models with three different degrees of myocardial fibrosis and treated them with CAN, and then characterized the stiffness, cardiac function, and NADPH oxidase-2 (NOX2) expression of the myocardial tissues. Based on the obtained stiffness of myocardial tissues, we used polyacrylamide (PA) gels with three different stiffness to mimic the ECM stiffness of cardiac fibroblasts (CFs) at the early, middle, and late stages of myocardial fibrosis as the cell culture substrates and then constructed CFs mechanical microenvironment models. We studied the effects of PA gel stiffness on the migration, proliferation, and activation of CFs without and with CAN treatment, and characterized the reactive oxygen species (ROS) and glutathione (GSH) levels of CFs using fluorometry and scanning electrochemical microscopy (SECM). We found that CAN has the best amelioration efficacy in the cardiac function and NOX2 levels in rats with medium-stiffness myocardial tissue, and the most obvious anti-fibrosis and antioxidant efficacy in CFs on the medium-stiffness PA gels. Our work proves the effect of ECM stiffness on CAN efficacy in myocardial anti-fibrosis and antioxidants for the first time, and the results demonstrate that the effect of ECM stiffness on drug efficacy should also be considered in the treatment of cardiovascular diseases. ## 1. Introduction Myocardial fibrosis, a common pathological process that occurs during the progression of various cardiovascular diseases, exhibits an imbalance between the production and degradation of extracellular matrix (ECM), which results in an increase in ECM stiffness [1,2], and the persistently high ECM in turn augments myocardial fibrosis progression [3,4]. For instance, ECM stiffness participates in the production of angiotensin II (Ang II) in cardiomyocytes, affecting the secretion of collagen in cardiac fibroblasts (CFs) [5] and regulating CF cytoskeleton through integrin and promoting their differentiation [6]. Moreover, ECM stiffness induces cellular oxidative stress by increasing the activity of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase (NOX), leading to imbalanced cellular glutathione (GSH) [7] and reactive oxygen species (ROS) [8]. Thus, considering the key role of ECM stiffness in myocardial fibrosis development, it is important to research the effect of ECM stiffness on the physiological functions of CFs. Mechanical and biochemical factors are interdependent in the biological processes of CFs differentiation [9]. For instance, mechanical signals of cardiac cell membrane tension induced by ECM stiffness can be transmitted to cells through integrin [10], and cardiac cells respond to ECM stiffness through mechanical signal transduction molecules on the cell membrane. AT1R, a key membrane protein in the renin-angiotensin system (RAS), is considered an important receptor for mechanical signaling [11], and its activation can lead to local contact formation and cell contraction. In addition, it has been reported that ECM stiffness can directly induce the up-regulation of AT1R expression without the involvement of angiotensin, indicating that AT1R also has the mechanical signal transduction effect [5]. Focal adhesion kinase (FAK), a widely expressed tyrosine kinase and a downstream component of an integrin-regulated signaling pathway, participates in the transmission of mechanical signals [12,13]. FAK phosphorylation and its consequent activation regulate several basic biological functions of cells, such as cell migration [14] proliferation [15], and adhesion [16], and are involved in cardiomyocyte hypertrophy [17] and cellular oxidative stress [18]. Thus, it is reasonable to speculate that AT1R and FAK may be involved in the process that ECM stiffness affects the progression of myocardial fibrosis. Mechanical properties of ECM not only regulate the biochemical behavior of cells but also affect the pharmacodynamics and therapeutic efficacy of drugs. For example, previous studies reported that increased bronchial wall stiffness reduced bronchodilator-induced bronchodilation [19], and increased ECM stiffness aggravated the multidrug resistance of tumors [20]. For cardiovascular diseases, previous reports mainly focused on the response of dynamic mechanics (e.g., blood shear force or tensile force) to the therapeutic effect of cardiovascular drugs, while understanding the effect of static ECM stiffness on the cardiovascular drugs efficacy in myocardial fibrosis is still limited. Candesartan (CAN), a classic angiotensin II (Ang II) receptor inhibitor, has been widely used as a first-line drug for treatments of coronary heart disease, heart failure, hypertension, and other cardiovascular diseases in clinic [21]. Many clinical studies have proven that CAN can provide prevention and regression of left ventricular hypertrophy and cardiac fibrosis, protect heart against ischemia-reperfusion injury and reduce myocardial damage during myocarditis [22,23], and improve oxidative stress state of heart [24,25]. Thus, it is essential to clarify the ECM stiffness effect on the efficacy of CAN. In this work, we first obtained the cardiac tissue stiffness by constructing rat models with three different degrees of myocardial fibrosis and treated them with/without CAN. Then, we cultured CFs with/without CAN intervention on the PA gels with different stiffness to mimic the ECM stiffness of CFs at three degrees of myocardial fibrosis. The therapeutic effect of CAN was evaluated by comparing the myocardial fibrosis index and oxidative stress before and after CAN treatment both in vivo and in vitro. Finally, based on the p-FAK and AT1R expression results, we discussed the possible mechanism of the ECM stiffness effect on the efficacy of CAN in myocardial fibrosis. Our study proves the interaction between ECM stiffness and CAN efficacy in myocardial fibrosis, which can provide useful insights into the anti-fibrosis and antioxidant efficacy of CAN from the biomechanopharmacology perspective. ## 2. Materials and Methods For any procedures not mentioned below, see supplementary data online. ## 2.1. Animals Male Sprague-Dawley rats (6–8 weeks old and weighing 250–300 g) were obtained from the experimental animal center of the School of Medicine of Xi’an Jiaotong University. The myocardial fibrosis model was constructed by subcutaneous injection of isoprenaline (ISO, 5 mg/kg/day, Sigma-Aldrich, St. Louis, MO, USA) once daily for 7–28 days. The treatment groups of rats received CAN (2 mg/kg/day, Sigma-Aldrich, St. Louis, MO, USA) by gastric gavage once daily for 28 days. The detailed treatment procedure of rats is presented in Table 1 and the rats were randomly divided into six groups. All experimental protocols were approved by the Biomedical Ethics Committee of the Medicine Department of Xi’an Jiaotong University (approval number: 2022-1515). In vivo experiments and animal management procedures were carried out according to the NIH Guide for Care and Use of Laboratory Animals [26]. ## 2.2. Assessment of Cardiac Functions of Rats The cardiac function of rats was assessed using an ultrasonic diagnostics instrument (IE33, Philips, Amsterdam, The Netherlands) equipped with an S12-4 linear array ultrasound transducer. ## 2.3. Staining Masson’s Trichrome Paraffin sections were stained with a Masson’s trichrome staining kit (Servicebio, Wuhan, China). The ratios of the stained fibrotic areas to the total ventricular areas were calculated and used as the collagen volume fraction. ## 2.4. Measurement of Young’s Modulus of Myocardial Tissue The myocardial tissues of rats were sliced into sections of approximately 500-μm thickness and glued on glass slides followed by immersion in phosphate-buffered saline (PBS). The stiffness of the slices (Young’s modulus, E) was measured using a Nanoindenter instrument (Piuma, Optics11, Amsterdam, The Netherlands) with a probe with a radius of 47 µm and a cantilever stiffness of 0.5 N/m. We randomly selected six locations on the whole ventricular wall in the tissue slice for measurements and indented the probe at each position 30 times (5 × 6 matrix) to measure the stiffness (Schematic diagram in Figure 1A). ## 2.5. Measurement of Brain Natriuretic Peptide (BNP) and Cardiac Troponin T (c-TnT) Blood samples of rats were collected from the abdominal aorta and centrifuged for 15 min at 4000 rpm to obtain the plasma. The contents of brain natriuretic peptide (BNP) and cardiac troponin T (c-TnT) in plasma were measured using a commercial enzyme-linked immunosorbent assay kit (Solarbio, Beijing, China) according to the manufacturer’s instructions. The absorbance of the plasma sample at 450 nm was recorded using a Spark 10 M Multimode Microplate Reader (TECAN, Männedorf, Switzerland). ## 2.6. Immunohistochemical Analysis of AT1R, p-FAK, FAK and NOX2 Paraffin sections of rat heart were incubated overnight with the anti-angiotensin II type-1 receptor antibody (AT1R, 1:100 dilution, Proteintech, 25343-1-AP, Chicago, IL, USA), the anti-focal adhesion kinase (phospho Y397) antibody (p-FAK, 1:800 dilution, Abcam, ab81298, Cambridge, UK), anti-focal adhesion kinase antibody (FAK, 1:800 dilution, Abcam, ab40794, Cambridge, UK) and NOX2 rabbit polyclonal antibody (1:400 dilution, Servicebio, Wuhan, China) at 4 °C and subsequently washed with PBS for three times. Then, the sections were incubated with second antibodies includes: IgG-horseradish peroxidase (HRP) (1:100; Dako, Wuhan, China; P0448, Copenhagen, Denmark) and Alexa Fluor-488 goat anti-rabbit antibody (1:500 dilution, Servicebio, Wuhan, China) at room temperature for 1 h. Finally, the sections were incubated with 4′,6-diamidino-2-phenylindole (DAPI). After dying, 3,3′ diaminobenzidine tetrahydrochloride (DAB) horseradish peroxidase Color Development Kit (Hat Biotechnology, Wuhan, China; IS015) was used for chromogenic development. Microphotographs were acquired and analyzed with fluorescence microscopy (ECLIPSE C1, Nikon, Tokyo, Japan). The analysis of the fluorescence area was performed using CaseViewer2.4 software. ## 2.7. Preparation and Characterization of Polyacrylamide (PA) Gels PA gels for culturing CFs were prepared using the procedure described in the previous literature [27,28]. First, a precursor solution containing $40\%$ (w/v) acrylamide monomers (Macklin, Shanghai, China), $2\%$ (w/v) N,N-methylene-bis-acrylamide (MBA) (Macklin), $10\%$ (w/v) ammonium persulfate (APS, Sigma-Aldrich) and tetramethylethylenediamine (TEMED, Sigma-Aldrich) were prepared. To prepare the PA gels with different stiffness (29.4 kPa, 67.7 kPa, and 125.5 kPa), the mass/volume concentrations of APS and TEMED were kept at $1\%$ and $0.1\%$ and the ratios of acrylamide (%)/MBA (%) were $\frac{10}{0.3}$, $\frac{10}{0.5}$ and $\frac{15}{0.9}$, respectively. Then, 50 μL of the prepared polymer solution was dropped onto the hydrophilic-treated glass bottom of a petri dish, and an 18 mm-in-diameter glass coverslip treated with dichlorodimethylsilane was carefully placed on the top of the solution. After the PA gel polymerization, the top coverslips were peeled off, and the remaining monomers and cross-linkers were removed by washing with PBS. Then, 1 mg mL−1 of the cross-linker N-sulfosuccinimidyl 6-(4′-azido-2′-nitrophenyl amino) hexanoate (Sulfo SANPAH, Thermo Scientific, Waltham, MA, USA) was added to the PA gels and photoactivated through ultraviolet light exposure for 10 min. The PA gels were then washed with 50 mM HEPES (pH 8.5) and incubated overnight in a solution of 50 μg mL−1 rat tail tendon collagen type I (Corning, Corning, NY, USA). The Young’s modulus of the PA gels was measured by the nanoindenter instrument (Piuma, Optics11, Amsterdam, The Netherlands). ## 2.8. Isolation and Purified Culture of Neonatal Rat Cardiac Fibroblasts Neonatal rat cardiac fibroblasts (NRCFs) were isolated from 1–3-day-old Sprague-Dawley rats following the NIH guidelines [26]. In Brief, chopped myocardial tissues were dispersed in 2 mg mL−1 collagenase type II enzyme solution at 37 °C for vibration digestion several times. The dissociated cells were suspended in DMEM/F12 (Corning, Corning, NY, USA). Then, the cells were obtained by filtering and centrifuging the mixed solution. The differential adhesion method was used to obtain NRCFs after 1 h. The purity of NRCFs was determined by vimentin protein staining and the NRCFs were extracted before each experiment. ## 2.9. Immunofluorescence Staining of α-Smooth Muscle Actin, p-FAK and AT1R CFs were penetrated and incubated with the following primary antibodies at 4 °C overnight: anti-alpha smooth muscle actin antibody (α-SMA, 1:800 dilution, Abcam, ab7817, Cambridge, UK), anti-focal adhesion kinase (phospho Y397) antibody (FAK, 1:800 dilution, Abcam, ab81298, Cambridge, UK), and anti-angiotensin II type-1 receptor antibody (AT1R, 1:100 dilution, Proteintech, 25343-1-AP, Chicago, IL, USA). Then, the cell samples were incubated with the following secondary antibodies in the dark at 37 °C for 2 h: Alexa Fluor-488 goat anti-mouse antibody (1:1000 dilution, Abcam, Ab150077, Cambridge, UK) or Alexa Fluor-594 goat anti-rabbit antibody (1:1000 dilution, Abcam, Ab150116, Cambridge, UK). Cell nuclei were stained using DAPI (1 μg mL−1, Sigma, D9542, St. Louis, MO, USA). Images of the cell samples were obtained using a laser scanning confocal microscope (FV3000 Olympus, Tokyo, Japan). ## 2.10. Western Blotting The expression levels of α-SMA, p-FAK, AT1R, collagen I (COL I, 1:1000 dilution, Abcam, ab270993, Cambridge, UK), collagen III (COL III, 1:1000 dilution, Sigma, c7805, St. Louis, MO, USA), and matrix metalloproteinase-2 (MMP-2, 1:1000 dilution, Abcam, ab92536, Cambridge, UK) were determined by western blotting (WB). All cell samples were lysed in RIPA lysis buffer (Solarbio, Beijing, China). The protein concentration was determined using a bicinchoninic acid (BCA) protein assay kit (Beyotime, Shanghai, China). The protein samples were loaded into the prepared SDS-PAGE separation gel ($8\%$ (w/v) acrylamide gel) and concentrated gel ($5\%$ (w/v) acrylamide gel), followed by electrophoresis under a constant voltage of 80 V. When the protein samples ran to the interface between the concentrated gel and the separated gel, the electrophoresis was converted to 120 V constant voltage until the marker ran to the bottom of the separated gel. Then, the target proteins are transferred to polyvinylidene difluoride (PVDF) membranes (Millipore Bedford, MA, USA) by electrophoresis under a constant current of 230 mA. After transferring the membrane, the PVDF membrane was immersed in the configured skim milk ($5\%$) for sealing. After sealing, the diluted primary antibodies were added to the PVDF membrane and incubated at room temperature for 2 h. After the primary antibody was well incubated, the secondary antibody was added and incubated at room temperature for 2 h. The immunoreactive bands were obtained using a chemiluminescence imaging system (ChemiQ 4800 mini, Ouxiang, Shanghai, China). The results were quantified using ImageJ software and normalized to those of GAPDH. ## 2.11. SECM Measurements of Extracellular GSH Levels Before SECM measurements, the CFs were incubated in an L15 culture medium (Solarbio, Beijing, China) containing 0.5 mM ferrocenecarboxylic acid (FcCOOH, Aladdin, Shanghai, China) for 30 min. All the SECM measurements were conducted using a commercial SECM instrument (ElProScan3, HEKA Elektronik GmbH, Harvard Bioscience Inc., USA) integrated with an inverted optical microscope (Olympus-IX53, Olympus Co., Ltd., Tokyo, Japan), and a typical three-electrode system with a 10 μm-in-diameter Pt disk electrode (i.e., Pt microelectrode) as the SECM probe and the working electrode, a platinum wire (0.5 mm in diameter) as the counter electrode and an Ag/AgCl wire (0.6 mm in diameter) as the reference electrode. The highest point of a single CF was determined using a line scanning method along the x-axis and y-axis on the cell surface with aid of an inverted optical microscope [7,29]. The Pt microelectrode was placed approximately 20 μm above the highest point of the CF. Subsequently, the probe biased at 0.5 V controllably approached the cell surface in the z-axis direction with a speed of 0.5 μm s−1 with recording the approach curve, from which the z-axis position of the highest point of CF was determined. For the cell length characterization, the longest distance in the x-axis or y-axis was selected as the length of CF. For the cell height characterization, the approach curves to the highest point of CF, and the surface of the PA gel next to the CF were recorded. The height of the CF was obtained by the difference in the absolute distances of the two approach curves. The obtained average heights and lengths of the CFs on the PA gels were inputted into the SECM simulation model (the specific data and description are provided in the Supplementary Materials). To quantitatively analyze the content of the GSH released by CFs on the PA gels, we used COMSOL multiphysics software (COMSOL Inc., Sweden) with finite element simulation to acquire the regeneration rate (k) of FcCOOH, which represent the outflow rates of GSH. The model geometry, simulation parameters, and boundary conditions of the developed simulation model are described in detail in the Supplementary Materials. ## 2.12. Statistical Analysis Statistical analysis was performed using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA). Statistics are presented as the mean ± standard error of the mean (SEM) for all quantitative data, with $$n = 5$$ for the animal experiments and n ≥ 3 for the cell experiments. Statistical significance was evaluated using two-way ANOVA followed by pair comparison with the Tukey test (ns, no significant difference, * $p \leq 0.05$, $p \leq 0.01$, * $p \leq 0.001$, and ** $p \leq 0.0001$). ## 3.1. CAN Treatment Results in Decreased Myocardial Stiffness and Collagen Content in Myocardial Fibrosis Rats The rat models with three degrees of myocardial fibrosis were established through subcutaneous injection of ISO for different days in rats. The heart weight index of the three groups of rats without and with CAN treatment has no significant difference (Supplemental Figure S1). After one, two, and four weeks of ISO injections, the measured Young’s moduli of the myocardial tissues are 26.4 ± 6.1, 67.9 ± 9.3, and 125.1 ± 25.3 kPa, respectively. After treatment with CAN, the Young’s moduli of the myocardial tissues are 17.6 ± 6.1, 52.1 ± 12.1, and 114.0 ± 4.8 kPa, respectively (Figure 1A). These results indicate that CAN can restrain the stiffening of fibrotic myocardium as induced by ISO, and the stiffness of myocardial tissue in the ISO-2 W group decreases most among the three groups with CAN treatment (Figure 1B). Since the increased stiffness of myocardial tissue has been proven to be mainly related to collagen deposition, we measured the collagen contents. From Masson’s trichrome staining results (Figure 1C), we can see that more collagen deposition forms in the myocardial tissue by prolonging the ISO induction time. With CAN treatment, the absolute contents of collagen deposition in myocardial tissues decrease by $2.40\%$, $2.73\%$, and $1.97\%$ in the groups of ISO-1 W, ISO-2 W, and ISO-4 W, respectively (Figure 1D). The decrease of collagen deposition in the ISO-2 W group is substantially higher than those of the ISO-1 W and ISO-4 W groups. That is, compared with the groups without CAN treatment, the ISO-2 W group has the most significant decrease in tissue stiffness and collagen content than those in the ISO-1 W and ISO-4 W groups. ## 3.2. CAN Treatment Improves the Cardiac Function of Myocardial Fibrosis Rats To quantify the cardiac function of myocardial fibrosis rats, we measured the echocardiography and plasma markers of heart failure and myocardial injury (BNP and c-TnT). From the ultrasonic indicators (Figure 2A), we observed that the ventricular cavity of myocardial fibrosis rats gradually increases, and the ventricular wall becomes thickened, while the cardiac systolic function gradually decreases with the prolonged ISO induction time. Additionally, the levels of BNP and c-TnT also increase with the extension of ISO induction time (Figure 2B). The ultrasound data and plasma marker levels of the rats treated with CAN are all ameliorated. The average values of LVIDd decreased by 0.16 ± 0.020, 0.38 ± 0.007, and 0.26 ± 0.012 mm, and the average values of IVSTd decreased by 0.04 ± 0.020, 0.38 ± 0.065 and 0.14 ± 0.038 mm, while the average values of LVPWd decrease by 0.18 ± 0.008, 0.46 ± 0.049 and 0.45 ± 0.010 mm. In contrast, the average values of EF and FS increase by $0.56\%$ and $0.6\%$ in the ISO-1 W group, $3.42\%$ and $5.3\%$ in the ISO-2 W group, and $3.03\%$ and $5.07\%$ in the ISO-4 W group. The improvements in the cardiac structure and function in the ISO-2 W group are the best among the three groups under CAN treatment. Furthermore, the decrease of the myocardial injury indexes in the ISO-2 W group is much better than the other two groups. Thus, we infer that CAN has different inhibitory effects on fibrosis in rats with different degrees of myocardial fibrosis. ## 3.3. CAN Treatment Decreases the Expressions of AT1R and p-FAK of Myocardial Fibrosis Rats The characterizations of the expressions of AT1R and p-FAK, two important mechanical signal transduction proteins, are important for understanding the role of mechanical signals in fibrosis. From Figure 3A, we can see that the AT1R expressions of the rat heart tissues are up-regulated with the extension of ISO induction time without CAN treatment, while the AT1R expressions in the groups with CAN treatment are down-regulated. Moreover, the inhibition efficacy of CAN in the ISO-2 W group is better than those in the ISO-1 W and ISO-4 W groups (Figure 3B). From Figure 3C, we can see that the expression of p-FAK gradually increases with the deterioration of the fibrosis degree in rats, which is similar to AT1R without CAN intervention, while the expression of p-FAK decreases after CAN intervention. The t-FAK expressions have no significant difference among the groups of ISO-1W, ISO-2W, and ISO-4W without and with CAN treatment (Supplemental Figure S2). Therefore, we further analyzed the p-FAK/t-FAK ratios of ISO-1 W, ISO-2 W, and ISO-4 W groups without and with CAN treatment, and the results showed that the reduction of the p-FAK/t-FAK ratio of the ISO-2 W group after CAN treatment is greater than those of ISO-1 W and ISO-4 W groups (Figure 3D). ## 3.4. CAN Treatment Ameliorates the Oxidative Stress State of Myocardial Fibrosis Rats Since NOX2 is the major source of ROS in the cardiovascular system [30], we checked the oxidative stress levels of rat hearts by measuring the NOX2 expression levels of rat heart tissues. The NOX2 expressions of the rat heart tissues are up-regulated with the extension of ISO induction time, while the NOX2 expressions in the groups with CAN treatment are down-regulated (Figure 4A), which decrease by $21.6\%$, $26.7\%$, and $18.2\%$ in the groups of ISO-1 W, ISO-2 W, and ISO-4 W, respectively (Figure 4B). CAN have the best therapeutic efficacy on the oxidative stress state of the rats in the ISO-2 W group among all three groups. The above animal experimental results indicate that the ISO-2 W group with CAN treatment has the best efficacy in myocardial fibrosis by decreasing the myocardial collagen content, plasma markers of myocardial injury, and oxidative stress state, and then improving the cardiac function. We, thus, hypothesize that the different CAN efficacy in myocardial fibrosis can be related to the different ECM stiffness of the damaged myocardial tissues. Next, we further used the in vitro cell models to confirm this. ## 3.5. CAN Treatment Inhibits CF Migration and Proliferation under Different ECM Stiffness An in vitro model of the cardiac mechanical microenvironment, which can mimic the structure and mechanical properties of natural heart tissues, has been widely used in cardiovascular disease research and drug screening [31,32]. PA gels have been used for the construction of in vitro cardiac mechanical microenvironment models due to their suitable biological properties and adjustable stiffness [33]. Based on the above in vivo results, we prepared the PA gels with a stiffness of 29.4 ± 4.1, 67.7 ± 5.3, and 125.5 ± 5.7 kPa as the culture substrates of CFs to mimic the early, middle, and late stages of the myocardial fibrosis rats, respectively (Supplemental Figure S3) and used the purified CFs for the subsequent in vitro experiments (Supplemental Figure S4). The CFs migration rates on the PA gels without CAN treatment become faster with increasing PA gel stiffness, and the CFs migration rates with CAN treatment are slower than those without CAN treatment (Figure 5A). The CFs migration rates on the PA gels with a stiffness of 29.4, 67.7, and 125.5 kPa decreased by $16.27\%$, $20.37\%$, and $15.43\%$, respectively (Figure 5B). In addition, from the EdU fluorescence images of the CF on the PA gels (Figure 5C), we observed that the EdU/Nucleus rate increases with increasing PA gel stiffness. The EdU/Nucleus rates on the PA gels decrease with CAN treatment, and the EdU/Nucleus rates on the PA gels with the stiffness of 29.4, 67.7, and 125.5 kPa decrease by $2.18\%$, $9.97\%$, and $3.84\%$, respectively (Figure 5D). These results indicate that the CFs migration and proliferation behaviors without CAN treatment are ECM stiffness-dependent, and CAN can inhibit the CF migration and proliferation induced by ECM stiffness. More importantly, CAN has the most significant inhibitory effect on the CF migration and proliferation rate on the 67.7 kPa PA gels than those on the 29.4 and 125.5 kPa PA gels. Therefore, we consider that the inhibitory effect of CAN on the CF migration and proliferation is affected by the ECM stiffness. ## 3.6. CAN Treatment Inhibits CF Activation and Collagen Protein Production under Different ECM Stiffness Upon tissue injury, CFs exposed to high strain or ECM stiffness undergo differentiation into myofibroblasts, in which α-SMA is the most used myofibroblast biomarker and represents the activation degree of CFs [34]. From the α-SMA fluorescence images, we observed that the α-SMA expressions of CFs on the PA gels gradually increase with increasing PA gel stiffness. With CAN treatment, the α-SMA expressions decrease (Figure 6A), and CAN shows better inhibition efficiency on the CF activation on the PA gels with 67.7 kPa than those on the 29.4 and 125.5 kPa PA gels (Figure 6B). The activated myofibroblasts are the main source of collagen in the fibrotic heart, causing a significant increase in type I and type III collagens [35]. In addition, MMP2 is also highly expressed in fibrotic tissues, which also leads to excessive collagen deposition in myocardial tissue [36]. We, thus, used western blotting to further characterize the expressions of α-SMA, type I and type III collagens, and MMP2 of the CFs (Figure 6C), which shows that the type I and type III collagens are up-regulated with the increased PA gel stiffness, accompanied with the increased expressions of α-SMA and MMP2. With CAN treatment, the expressions of α-SMA, MMP-2, type I, and type III collagens are all down-regulated. Additionally, according to the statistical analysis of the grey values of α-SMA, MMP-2, type I, and type III collagen with and without CAN treatment (Figure 6D), the decreased degrees of the grey values on the 67.7 kPa PA gels are more obvious than those on the 29.4 and 125.5 kPa PA gels. Based on the results of α-SMA and collagen production-related proteins, we can find that the increased ECM stiffness intensifies the CF activation and the collagen production, and a significant difference in the inhibition of CF activation and collagen production by CAN. It can thus be speculated that the inhibitory effect of CAN on CF activation and collagen production is also affected by ECM stiffness. ## 3.7. CAN Treatment Ameliorates Redox Imbalance of CFs under Different ECM Stiffness The occurrence and development of myocardial fibrosis are accompanied by the disruption of cellular redox balance [37]. ROS and GSH, the two typical and key cellular redox species play important roles in maintaining cellular redox balance [38]. Herein, we first performed the fluorescence staining experiments to characterize the intracellular NOX2 and ROS levels of the CFs on PA gels. The NOX2 and ROS levels of CFs gradually increase along with the increased PA gel stiffness, while the NOX2 and ROS levels of CFs decrease with CAN treatment (Figure 7A,C). Furthermore, the analysis of the NOX2 and ROS fluorescence intensity shows that the ROS levels produced by the CFs on the 67.7 kPa PA gels with CAN treatment decrease most significantly (Figure 7B,D). It indicates that CAN can effectively reduce the accumulation of oxidative substances of CFs induced by ECM stiffness, and CAN has the most obvious antioxidant function on the CFs on the 67.7 kPa PA gels. Moreover, GSH depletion by its efflux is also taken as a marker of oxidative stress and independently precedes ROS accumulation [39,40], that is, the GSH efflux is also a key biological indicator to evaluate the cell damage and apoptosis. We further characterized the dynamic GSH efflux process across the CFs using SECM. SECM, an electrochemical scanning probe microscopy with using a micrometer/nanometer-sized electrode as its probe to record the redox currents around living cells in a cell culture medium in an in situ, non-invasive and label-free manner [41,42], has been widely applied to monitor the levels of several chemical substances (e.g., GSH, oxygen, H2O2) released by living cells [43,44]. In the SECM experiments, we used FcCOOH as the redox mediator and applied the oxidation potential of FcCOOH at the probe to characterize the GSH efflux from CFs on PA gels. In principle, the oxidized [FcCOOH]+ at the probe diffuses to the CF surface and reacts with the GSH released by the CF to regenerate FcCOOH, which diffuses back to the probe surface and results in an oxidation current of FcCOOH. When the SECM probe approaches the CF surface, a pure negative feedback current can be obtained, which results from the hindering effect of CF on the FcCOOH diffusion to the probe surface. When approaching the SECM probe to the surfaces of CFs on the stiff PA gels (67.7 and 125.5 kPa), the lower probe oxidation currents of FcCOOH compared with those of the CFs on the soft PA gels (29.4 kPa) are obtained (Figure 7E). It can be due to the less GSH efflux generated from the CFs on the stiff PA gels, thus leading to a smaller recycle oxidation current of FcCOOH compared to those of CFs on the softer PA gels. Then, based on the recorded average heights and lengths of CFs on the PA gels with different stiffness (Supplemental Figure S5), we built a 2D axial simulation model of the SECM experiments using COMSOL Multiphysics software (Supplemental Figure S6). By fitting the obtained SECM approach curves with the theoretical ones, we can obtain the regeneration rate (k) of FcCOOH, which can represent the outflow rate of GSH. From the obtained average k values of the CFs on the PA gels through six repeated SECM measurements, we can see a decreasing trend of k values with the increased PA gel stiffness, indicating that the less extracellular GSH levels of CFs on the stiff ECM produced. Comparably, the average k values of CFs with CAN treatment are higher than those of CFs on the PA gels without CAN treatment. Based on the analysis of the difference in the average k values without and with CAN treatment (Figure 7F), we can obtain that the increase of the average k values of CFs on the 67.7 kPa PA gels are significantly higher than those on the 29.4 and 125.5 kPa PA gels. These results indicate that CAN can ameliorate the oxidative state of CFs induced by the ECM stiffness, thus reducing GSH consumption, and the GSH consumption state of CFs on the 67.7 kPa PA gels is ameliorated best compared to those on the 29.4 and 125.5 kPa PA gels. Furthermore, since GSH is the most important biological reductant in cells, it is also important to detect the intracellular GSH content of CFs. The result of the GSH assay kit shows that the intracellular GSH contents decrease with the increased ECM stiffness (Figure 7G). With CAN treatment, the intracellular GSH contents increase, and the intracellular GSH contents in the CFs on the 67.7 kPa PA gels increase most significantly. Additionally, the change in the intracellular GSH contents is consistent with the change in the GSH efflux. Based on the above obtained ROS and GSH level results, we conclude that ECM stiffness can lead to the oxidative stress of CFs, and CAN can alleviate the oxidative stress of CFs induced by ECM stiffness. Moreover, CAN presents the strongest antioxidant efficacy to CFs on the PA gels with a medium stiffness of 67.7 kPa. ## 3.8. ECM Stiffness-Dependent Anti-Fibrosis and Antioxidant Efficacy of CAN by Regulation of AT1R and p-FAK To evaluate the role of FAK in ECM stiffness-mediated activation of CFs, we characterized the p-FAK expressions of CFs. We can see from the p-FAK fluorescence images that the p-FAK expressions of CFs increase with the increase of the PA gels stiffness. To determine whether FAK is downstream of AT1R in ECM stiffness-induced CF activation, we blocked the signal transduction of CFs mediated by AT1R on the PA gels with different stiffness through angiotensin receptor inhibitor and measured the p-FAK expressions of the CFs again. The p-FAK expressions in CFs on the PA gels with a three stiffness with a CAN treatment are reduced (Figure 8A). Subsequently, from the analysis of the decreased levels of the p-FAK expressions without and with the CAN treatment, the p-FAK expressions of the CFs on the 67.7 kPa PA gels decrease most among the three groups (Figure 8B) Similarly, the western blotting results also confirm that ECM stiffness could promote FAK phosphorylation while blocking AT1R could reduce the level of p-FAK (Figure 8C,D). These results imply that AT1R may function as an important upstream molecule to mediate FAK-dependent CF mechanotransduction. Therefore, we speculate that the best CAN efficacy on CFs on the 67.7 kPa PA gels is due to the most obvious decrease of p-FAK in CFs on the 67.7 kPa PA gels, which may be related to the expression of AT1R. We further characterized the expression of AT1R under the combined effects of ECM stiffness and CAN treatment. The results of fluorescence images confirm that the mechanical regulation of ECM stiffness directly leads to the increase of AT1R expression. While CAN inhibits the AT1R expression, which is consistent with the pharmacological effect of CAN (Figure 8E). From the analysis of the changes in AT1R expression in CFs on the PA gels with different stiffness with CAN treatment in Figs. 8F, the decrease of the AT1R activities in CFs on the 67.7 kPa PA gels with CAN treatment is the most obvious. The western blotting results in Figure 8G,H also confirm the above changes in AT1R. Next, to study the role of FAK in ECM-mediated fibroblast activation and cellular oxidative stress. We treated CFs with the FAK inhibitor (PF-573228, 10 μM; MCE, HY-10461). The obtained fluorescence and western blotting results both show that the expressions of α-SMA, NOX2, and ROS significantly decreased after the PF-573228 intervention (Figure 9), indicating that FAK can regulate the activation and redox state of CFs. From the above results, we can conclude that the co-regulation of ECM stiffness and CAN efficacy lead to the difference in the AT1R expression of CFs on the PA gels with three stiffness, which is manifested by the most obvious decline of AT1R expression of CFs on the 67.7 kPA gels. The difference in the AT1R expression regulates the difference in the reduction of p-FAK, further leading to the different improvements of CF activation and oxidative stress state (Figure 10). ## 4. Discussion ECM is a linchpin of myocardial tissue with the functions of maintaining structural and functional integrity and providing the ambient microenvironment required for mechanical, cellular, and molecular activities in the heart. Myocardial fibrosis would inevitably result in profound changes in the composition and structure of ECM, such as collagen deposition, increased stiffness, and impaired contraction. The stiffness of native myocardial tissues at the adult stage is only 10–20 kPa, while the stiffness of myocardial tissue can increase from 30 to 90 kPa for post-myocardial infarction. Considering that the ventricular remodeling induced by ISO in rats is a classic model for studying myocardial fibrosis, we chose the ISO-induced rat myocardial fibrosis model in our case and we found that the myocardial tissue stiffness of myocardial fibrosis (125.5 kPa) is higher accompanied by the progressive deterioration of cardiac function, from which we can see a positively correlated relationship between the stiffness of myocardial tissue and the degree of fibrosis within the physiological and pathological ranges of fibrosis. This proves that the increase of ECM stiffness and the injury of cardiac function promote each other and the myocardium stiffness can be a valuable new metric for determining the cardiac dysfunction in patients with heart disease and reflecting the development and prognosis of myocardial fibrosis to a certain extent [45]. The concept of “biomechanopharmacology” has been introduced in 2002, which is a new discipline established by combining biomechanics/mechanobiology and pharmacology [46]. It not only focuses on the influence of biomechanical factors on pharmacological effects but also emphasizes the change in drug efficacy by changing biomechanical events [47,48]. For instance, phosphoinositide 3-kinases (PI3Ks) β isoform exerts a significant role under high shear stress by transferring the force of actin cytoskeleton in activated platelets to clotting fibrin through platelet integrin, thus inhibiting the action of anticoagulants [49]. Matrix stretching can activate transient receptor potential cation channel V4 (TRPV4), and preclinical animal studies have successfully shown that oral TRPV4 channel inhibitors can prevent pulmonary edema relevant to heart failure [50]. In our study, the myocardial tissues of rats with medium stiffness and the CFs on the 67.7 kPa PA gels present the most obvious declining trend of fibrosis after CAN treatment. The high ECM stiffness weakens the efficacy of CAN for anti-fibrosis, which may be due to that the mechanical signal promotes the progress of fibrosis through mechanical pathways (e.g., integrin). For the rats in the ISO-1W group, the degree of myocardial fibrosis is relatively mild, and the CAN efficacy relatively declines which might be due to the self-healing mechanisms of heart. The CFs on the 29.4 kPa PA gel show no significant increase in the stiffness-induced AT1R expression and a relative decline in the CAN efficacy for CFs on the 29.4 kPa PA gels. FAK, an enzyme widely existing in the cytoplasm, plays a vital role in various types of fibrosis [18]. Phosphatidylinositol 3-kinase/protein kinase B (PI3K/Akt) and ERK$\frac{1}{2}$, the downstream of FAK, are the two classical signaling factors that directly cause myocardial fibrosis [51,52], and can result in the α-SMA-positive myofibroblast diversity and the formation of various types of collagens. FAK also directly regulates the expression of α-SMA, and thus participates in the process of liver fibrosis [53]. the expression of AT1R can be induced by ECM stiffness, and the expression level of AT1R increases with the increase of ECM stiffness, demonstrating the activation effect of ECM stiffness on AT1R. For the group with CAN treatment, CAN leads to the decrease of AT1R expression, which shows the opposite effect to the increase of AT1R expression induced by ECM stiffness. It can be due to that CAN can selectively and non-competitively bind AT1R, resulting in the decreased expression of AT1R. In addition, our results also prove the upstream and downstream relationship between p-FAK and AT1R, i.e., AT1R is an upstream protein of FAK and mediates ECM stiffness-induced CF activation through FAK. Therefore, the resistance effect between the pharmacological efficacy of CAN and the ECM stiffness influence leads to the difference in AT1R expression of CFs on the PA gels with different stiffness, further leading to the change of downstream p-FAK expression, which is also the reason for the difference in the CAN efficacy. Our results also prove that AT1R can only regulate FAK to a certain extent, which can be due to that FAK as a downstream molecule is also affected by other membrane mechanosensitive proteins (e.g., integrins). However, the differences in drug efficacy caused by the complex mechanical regulation mechanisms and biochemical factors deserve further analysis and discussion. Oxidative stress injury is one of the main features of fibrosis. The elevation of NOX2 and ROS [54] and the reduction of GSH synthesis have been proven in organ fibrosis [55] and cardiovascular disease [56]. Moreover, the GSH efflux regulates the redox state in the extracellular microenvironment and cooperates with the intracellular redox status regulation system to maintain cellular redox homeostasis [57]. Our results confirm that ECM stiffness can directly lead to the accumulation of NOX2, and ROS and the reduction of intracellular and extracellular GSH. It is also noted that CAN has the most obvious antioxidant efficacy under the mechanical condition of medium stiffness. Some studies showed that ECM stiffness can directly activate NOX2 to produce ROS by affecting the cellular microtubule function [58]. Our experimental results also prove that ECM stiffness itself can regulate the expressions of NOX2 and ROS, which is regulated by FAK. Therefore, we consider that ROS changes may also be attributed to the FAK regulation by the ECM stiffness. In addition, the characterization results of GSH in our work show that both the intracellular GSH content and the GSH efflux decrease with the increased ECM stiffness, which is opposite to the change in the ROS level of CFs. Since the consumption of GSH is mainly related to the accumulation of oxidative stress substances inside and outside cells, we can thus speculate that the change trend of the intracellular GSH content and the GSH efflux of CFs may be related to the accumulation of ROS. However, the direct regulation of GSH by ECM stiffness needs further research. ## 5. Conclusions The main finding of this study is that ECM stiffness has a certain impact on the CAN efficacy in anti-fibrosis and antioxidative efficacy, and CAN shows the obvious antioxidative and anti-fibrosis efficacy at the medium-stiffness (67.7 kPa) range of ECM. First, we constructed the rat models with three different myocardial fibrosis degrees and confirmed the difference in the anti-fibrosis and antioxidant efficacy of CAN in the hearts with different stiffness. Then, using the in vitro ECM stiffness-mediated myocardial fibrosis models constructed on the stiffness-adjustable PA gels, the differences in the therapeutic efficacy of CAN on the functions of CFs under different ECM stiffness were identified. Finally, based on the p-FAK and AT1R expression results, we considered that the difference in CAN efficacy is related to the difference in AT1R-FAK activity co-regulated by ECM stiffness and CAN. 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--- title: Metformin Impacts Human Syncytiotrophoblast Mitochondrial Function from Pregnancies Complicated by Obesity and Gestational Diabetes Mellitus in a Sexually Dimorphic Manner authors: - Jessica F. Hebert - Leslie Myatt journal: Antioxidants year: 2023 pmcid: PMC10044921 doi: 10.3390/antiox12030719 license: CC BY 4.0 --- # Metformin Impacts Human Syncytiotrophoblast Mitochondrial Function from Pregnancies Complicated by Obesity and Gestational Diabetes Mellitus in a Sexually Dimorphic Manner ## Abstract Maternal obesity and gestational diabetes mellitus (GDM) are associated with placental dysfunction, small for gestational age (SGA) offspring, and programming of adult-onset disease. We examine how metformin, commonly used to treat type A2 GDM, affects placental metabolism as well as mitochondrial content and function. Syncytiotrophoblasts (STBs) were prepared from placentas of male and female fetuses collected at term cesarean section from lean (pre-pregnancy BMI < 25), obese (BMI > 30), and obese A2GDM women. Metformin treatment (0.001–10 mM) of STB caused no change in non-mitochondrial respiration but significant concentration-dependent (1 and 10 mM) decreases in basal, maximal, and ATP-linked respiration and spare capacity. Respiration linked to proton leak was significantly increased in STB of male A2GDM placentas at low metformin concentrations. Metformin concentrations ≥1 mM increased glycolysis in STB from placentas from lean women, but only improved glycolytic capacity in female STB. Whereas metformin had little effect on superoxide generation from male STB of any group, it gave a concentration-dependent decrease in superoxide generation from female STB of lean and obese women. Fewer mitochondria were observed in STB from obese women and male STB from lean women with increasing metformin concentration. Metformin affects STB mitochondrial function in a sexually dimorphic manner but at concentrations above those reported in maternal circulation (approximately 0.01 mM) in women treated with metformin for GDM. ## 1. Introduction In the United States, $39.7\%$ of women of reproductive age are obese (BMI > 30) [1], a significant health concern linked to an increased risk of hypertension and diabetes [2] and a greater risk for pregnancy complications. Obese women are twice as likely to develop preeclampsia and three times more likely to have gestational hypertension or gestational diabetes mellitus (GDM) [3,4]. GDM affects $7\%$ of pregnancies and is associated with increased circulating maternal glucose and insulin resistance, resulting in fetal hyperglycemia and increased fetal insulin secretion. Consequently, the risk of pregnancy complications and postnatal consequences for mother and offspring, including later onset of diabetes, obesity, and cardiovascular disease in both, is also elevated [5,6,7]. Babies from mothers with obesity and GDM are more likely to be born small for gestational age (SGA), which is further linked to the developmental programming of adult-onset disease [8,9]. Not all GDM pregnancies are complicated by obesity, nor do all obese women develop GDM. However, as obesity increases the risk of GDM and other health issues during gestation, studying these risks and the effects of current therapeutic interventions is essential in obstetric research, especially concerning their effects on placental function. The placenta is the primary regulator of pregnancy, producing hormones to direct maternal metabolism to mobilize substrates to support fetal growth, modulating nutrient, gas, and waste exchange, while also serving as a physical and immunological barrier between mother and fetus [10]. The placenta, particularly the trophoblast cells, has substantial metabolic activity and requires energy to sustain these critical functions. The two-tiered villous trophoblast layer is responsible for oxygen and nutrient uptake from maternal circulation and delivery to the developing fetus. Mononucleated villous cytotrophoblasts (CTB) form the underlying proliferative layer and fuse to form the outer layer of multinucleated syncytiotrophoblasts (STB), where contact with the maternal circulation and nutrient uptake takes place. In addition to nutrient transport, STB are responsible for the synthesis and secretion of substantial amounts of peptide and steroid hormones [11]. Hence, the placenta consumes a large proportion of substrates reaching it, with oxygen and glucose consumption six times that of the fetus per unit weight [12] while being only one sixth of the size. Measuring trophoblast mitochondrial respiration and glycolytic activity (the consumption of the primary fuels oxygen and glucose) is an effective index of placental metabolic activity. We have previously used the Seahorse extracellular flux analyzer (Agilent) to assess respiration in primary STB cultures and showed significantly reduced mitochondrial respiration with increasing maternal adiposity [13], with an even further reduction in STB from obese women with medication-controlled type A2 gestational diabetes (A2GDM) [14]. In addition, human STB showed a lack of fuel flexibility (the ability to switch between glucose, fatty acid, and glutamine as substrates for mitochondrial respiration) with increasing maternal adiposity and A2GDM, and with male fetal sex [5]. Male fetuses are well known to be at higher risk for adverse outcomes, including stillbirth and developmental programming of adult-onset disease. This is particularly true in pregnancies complicated by obesity and GDM [15,16,17], which are further associated with differences in gene expression in male versus female placentas [18,19,20], antioxidant defenses [21], mitochondrial respiration [22], and mitochondrial biogenesis [23]. For more than forty years, drugs, such as insulin, glyburide, and metformin, have been used to attempt to improve A2GDM outcomes [24]. The mechanism of action of metformin is unclear, but it is suspected to inhibit mitochondrial complex I, which reduces mitochondrial respiration and ATP production, concurrently inhibiting the synthesis of reactive oxygen species (ROS) and generation of oxidative stress [25,26]. In addition, metformin appears to increase AMPK and insulin sensitivity, which enhances glucose transport [27] (Figure 1). Although insulin and glyburide cross the placenta minimally, recent studies indicate that metformin concentrations in fetal cord blood range from half to nearly equal to that in maternal plasma [28,29]. Tarry-Adkins et al. found that while fetal plasma metformin concentration is roughly one-and-a-half times that in maternal plasma, metformin concentrations in both maternal and fetal plasma correlated to placental metformin concentrations [30]. When metformin is used to treat GDM, the concentration in maternal circulation is approximately 0.01 mM, but with putatively much higher mitochondrial accumulation [31,32,33,34,35]. Previous studies exploring metformin’s mechanisms of action employed much higher concentrations in human cell cultures (2 mM) [36], non-human cell cultures (0.1 mM) [25], and non-human-derived whole and partial mitochondria (100 mM) [26]. Therefore, the full range of metformin effects at a physiologically relevant concentration on syncytiotrophoblast metabolic function are unknown. As we have previously shown that maternal obesity and A2GDM are associated with reduced trophoblast respiration in a sexually dimorphic manner [13,14,37], given the potential ameliorative effect of metformin on mitochondrial activity, here, we investigate the effect of a range of metformin concentrations on mitochondrial respiration, glycolysis, and mitochondrial activity, as assessed by membrane potential and generation of ROS in STB from lean, obese, and type A2GDM women with either a male or a female fetus. ## 2.1. Ethical Approval and Study Participants Placentas were collected from the Labor and Delivery Unit at Oregon Health & Science University Hospital for the OHSU Placenta Repository per a protocol approved by the university Institutional Review Board (Study ID: 00016328) with informed consent from the patients. All tissues and clinical data were deidentified before researchers were given access. Placentas were collected from three groups of women with either a male or female fetus: lean (pre-pregnancy BMI 18.5–25), obese (pre-pregnancy BMI 30–45), and A2GDM (GDM controlled by insulin, matched for BMI with obese women). Patient, fetal, and placental characteristics are summarized in Table 1. Patients had no other pregnancy complications (e.g., preeclampsia, asthma, IUGR) and no reported co-morbidities, such as smoking or drug use. ## 2.2. Tissue Collection, Trophoblast Isolation, and Cell Culture Placentas were collected at term by cesarean section in the absence of labor immediately following delivery of the neonate. Villous tissue (∼60 g, in roughly 2.5 cm2 pieces) was collected from several random sites in the placenta, and primary cytotrophoblasts were isolated using a well-validated protocol [37,38,39,40]. Briefly, tissue was rinsed in PBS and trophoblasts scraped away from the chorionic plate and blood vessels before being digested three times in HEPES Buffered Salt Solution containing Trypsin and DNase at 37 °C. Cells collected by centrifugation underwent Percoll gradient purification, were counted, and kept frozen in liquid nitrogen in FBS/DMSO freezing media until culture. Syncytiotrophoblasts were created by culturing cytotrophoblasts in 96-well cell culture plates at a concentration of 100,000 cells/well for 72 h in Iscove’s Modified Dulbecco’s Medium (IMDM supplemented with $10\%$ FBS and $1\%$ penicillin/streptomycin). In vitro, cytotrophoblasts spontaneously fuse and differentiate to form multinucleated STB [13,40]. The identity and purity of syncytiotrophoblasts were confirmed, by light microscopy and immunofluorescence with cytokeratin 7 and DAPI [38,39,41] and the production of hCG upon syncytialization. Following syncytialization, STB were treated for an additional 24 h with 0–10 mM metformin in IMDM before Seahorse assays. ## 2.3. Mitochondrial Respiration Mitochondrial respiration was assessed in cells with or without metformin treatment using the Seahorse Bioscience XFe96 analyzer and the Mito Stress Test Kit (both Agilent) as previously described [5,38]. Data are expressed as the rate of oxygen consumption (OCR) in pmoles/min under basal conditions and following sequential injection of oligomycin (10 μM), carbonyl cyanide p-trifluoromethoxy-phenylhydrazone (FCCP; 10 μM), and rotenone/antimycin A (10 μM) to measure ATP-coupled respiration, maximal respiration, spare capacity, and non-mitochondrial respiration. Data were normalized to total cellular DNA per well as quantified using the Quant-iT PicoGreen dsDNA Assay (ThermoFisher). A typical curve obtained from the Seahorse analyzer during a Mito Stress assay is illustrated in Figure 2A. ## 2.4. Glycolysis Glycolysis was assessed by Seahorse XF Cell Glycolysis Stress Test (Agilent), as previously described [42]. Briefly, following treatment with or without metformin, cells were serum-starved for one hour in XF Base Medium supplemented with 4 mM L-glutamine. The sequential glycolytic stress test injections contained glucose (100 mM), oligomycin (10 μM), and 2-Deoxyglucose (2DG; 500 mM; Sigma). Extracellular acidification rate (ECAR, mpH/min) was recorded for three cycles following each timed injection and normalized to total cellular DNA per well as quantified above. Figure 2B demonstrates a typical glycolysis stress analysis after treatment with glucose and inhibitors. ## 2.5. Mitochondrial Superoxide Production MitoSOX Red (Invitrogen) rapidly targets mitochondria in live cells and is oxidized by superoxide to produce red fluorescence. STB were stained with MitoSOX Red (5 μM) in HBSS at 37 °C and $5\%$ CO2 for 30 min. Cells were washed with warm HBSS three times. Fluorescence (510 nm excitation, 580 emission) was measured using a BioTek Synergy H1 hybrid plate reader (BioTek). Data were normalized to total cellular DNA per well, as measured with Quant-iT PicoGreen dsDNA Assay (ThermoFisher). ## 2.6. Quantification of Active Mitochondria MitoTracker Deep Red (Invitrogen) passively diffuses across the plasma membrane and accumulates in active mitochondria dependent on mitochondrial potential. Following the various treatments, STB were stained with MitoTracker Deep Red (200 nM) in Hank’s Buffered Saline Solution (HBSS) in an incubator at 37 °C and $5\%$ CO2 for 30 min. Cells were washed with warm HBSS three times. As above, fluorescence (644 nm excitation, 665 nm emission) was measured using a plate reader. Data were normalized to total cellular DNA per well as quantified above. ## 2.7. Statistical Analysis Data for each parameter measured in the Mito and Glyco stress tests at each concentration of metformin were normalized to values obtained in the same batch of cells with no treatment. Data are reported as mean +/− standard deviation. Comparisons between groups were performed by two-way ANOVA with Tukey post hoc test for multiple comparisons using GraphPad Prism. $p \leq 0.05$ was considered significant for these analyses. ## 3.1. Clinical Characteristics There were no significant differences in maternal age, gestational age at delivery, fetal weight, or placental weight between lean, obese, and A2GDM groups. By experimental design, women with A2GDM were BMI matched to obese women. Both groups had significantly higher BMI than lean women. The fetal/placental weight ratio of males from obese and A2GDM women was significantly lower than lean women ($p \leq 0.05$). Females from obese women had a smaller fetal/placental weight ratio compared to females from A2GDM women and lean women ($p \leq 0.05$). These findings are summarized in Table 1. ## 3.2. The Effect of Metformin on Mitochondrial Respiration Basal respiration, a measure of oxygen consumption in resting cells, was decreased in a concentration-dependent manner in STB after treatment with increasing amounts of metformin ($p \leq 0.001$ from 1 mM onward) in both male and female cells from all maternal conditions (Figure 3A). Although there was a decrease in basal respiration in STB from male placentas of obese mothers at lower concentrations of metformin, there was no significant difference between them and the trophoblast of male placentas of other conditions, nor between them and the trophoblast of obese females. Following the inhibition of ATP synthase with oligomycin (Figure 2), the resulting decrease in oxygen consumption reveals the proportion of basal respiration used to generate ATP. STB from all groups of patients, regardless of maternal condition or fetal sex, had decreased ATP-linked respiration after treatment with 1–10 mM metformin ($p \leq 0.05$ from 1 mM onward, Figure 3B). Metformin had a similar effect on maximal respiration, measured after administration of FCCP, which permeabilizes the mitochondrial membrane and permits the free flow of protons as ATP drivers. Regardless of maternal condition or fetal sex, STB of all groups showed a concentration-dependent decrease in maximal respiration following at least 1 mM metformin treatment ($p \leq 0.001$ from 1 mM onward, Figure 3C). Spare capacity, i.e., the difference between basal respiration and maximal respiration, representing the cell’s ability to increase respiration in response to stress, did not decrease significantly until treatment with 10 mM metformin in all patient groups (Figure 3D) due to the more gradual decrease in maximal respiration (Figure 3C) versus that of basal respiration (Figure 3A) with increasing concentrations of metformin. However, the spare capacity of STB of females from A2GDM women was increased vs. corresponding female STB of both lean ($52\%$, $p \leq 0.01$) and obese ($98\%$, $p \leq 0.001$) women at 0.1 mM metformin (Figure 3D). No significant change was observed with increasing amounts of metformin in non-mitochondrial respiration, the proportion of oxygen consumption that occurs outside of oxidative phosphorylation (Figure 3E). However, the difference between non-mitochondrial respiration and oxygen consumption related to ATP generation indicates oxygen consumption linked to proton leak from the electron transport chain. OCR related to proton leak in STB from obese and GDM women increased at low metformin concentrations compared to STB from lean women (Figure 3F). Indeed, male STB from A2GDM women had significantly increased OCR related to proton leak compared to females from A2GDM women ($150\%$, $p \leq 0.01$), as well as from males from obese women ($126\%$, $$p \leq 0.05$$) and lean women ($106\%$, $$p \leq 0.003$$) at 0.01 mM metformin. Males from lean women also had increased OCR related to proton leak compared to females from lean women after treatment with 0.001 to 0.1 mM metformin ($57\%$, $p \leq 0.05$ at 0.01 mM metformin). ## 3.3. Glyco Stress Assays Similar to measuring mitochondrial respiratory stress via oxygen consumption, glycolysis can be measured by the change in acidification of cell culture medium, i.e., the extracellular acidification rate (ECAR). Female STB from obese women and male STB from A2GDM women exhibited elevated non-glycolytic acidification (caused by processes other than glycolysis) with 1- and 10-mM metformin compared to untreated controls ($p \leq 0.01$) and to other maternal and fetal sex-matched groups ($p \leq 0.05$, Figure 4A). Metformin significantly increased glycolysis only in male (1 and 10 mM, $p \leq 0.05$, $p \leq 0.01$) and female STB (10 mM, $p \leq 0.05$) from lean but not obese or A2GDM women (Figure 4B). Only female STB from lean women had significantly increased glycolytic capacity (the ability to acutely increase conversion of glucose to pyruvate or lactate) at 0.001–0.1 mM metformin ($p \leq 0.01$). No significant differences in glycolytic reserve (the ability to respond to an increase in demand) were found between groups with increasing metformin concentrations (Figure 4C,D). ## 3.4. Determination of Superoxide Release Metformin treatment appeared to affect superoxide generation in a sexually dimorphic manner, as well as in relation to pathology. Female STB treated with increasing concentrations of metformin showed a significant reduction in superoxide generation when from lean women at 0.001–1 mM, and from obese women at 1–10 mM (both $p \leq 0.05$), vs. STB from corresponding male groups (Figure 5A). Increasing metformin concentrations had little effect on superoxide generation from STB of male placentas although superoxide generation in male STB of lean women was significantly greater at 0.001 mM metformin vs. male STB of obese or A2GDM women ($p \leq 0.01$, Figure 5B). Superoxide generation in female STB of the A2GDM group was resistant to the effect of increasing metformin, being significantly greater at 0.001–0.01 mM vs. lean or obese, but then fell with metformin concentrations above 0.1 mM (Figure 5C). ## 3.5. Quantification of Active Mitochondria Mitochondria activated by membrane potential decreased in male compared to female STB of lean women with increasing metformin concentrations. Metformin concentrations above 0.1 mM caused a reduction in mitochondrial activity in STB from obese women vs. no treatment with a more pronounced effect in males than females ($p \leq 0.01$) (Figure 5D). Any metformin treatment caused a reduction in mitochondrial activity in male STB from lean and obese women ($p \leq 0.05$ for lean, $p \leq 0.001$ for obese), a response that was not shared by male STB from A2GDM women (Figure 5E). Metformin treatment also resulted in significantly less mitochondrial activity in female trophoblast from obese women compared to lean or A2GDM women ($p \leq 0.01$ (Figure 5F)). ## 4. Discussion We investigated the effect of a commonly used anti-diabetic drug, metformin, on mitochondrial function in STB from lean, obese, and GDM women in vitro and report several novel findings. Metformin treatment significantly decreased basal, ATP-linked, and maximal mitochondrial respiration as well as spare respiratory capacity (maximal minus basal) of STB mitochondria in a concentration-dependent manner, regardless of patient type, but only at concentrations above 0.1 mM. Metformin at concentrations (approximately 0.01 mM) found in maternal plasma of treated women [31,32,33,34,35] did not improve basal (unstressed state) respiration, ATP-linked respiration, maximal respiration, or spare capacity, but neither did it disrupt these functions [33,34]. As higher local metformin concentrations may be found due to accumulation in the mitochondria [35], further exploration of the relationship between circulating plasma and placental tissue mitochondrial metformin concentrations is warranted. Interestingly, a recently reported study also showed inhibition of basal, ATP-linked, and maximal respiration in trophoblast but at slightly lower concentrations (0.01–0.1 mM) [30]. No improvement in respiration was observed with metformin. Metformin appears to impact STB in a sexually dimorphic manner, increasing spare capacity in female STB from A2GDM pregnancies, albeit at concentrations greater than those found in maternal plasma, but drastically increasing proton leak and uncoupled respiration in STB from males, especially in A2GDM pregnancies. Mitochondrial respiratory spare capacity is the ability of a cell to escalate oxidative phosphorylation in response to increased energy demand [43]. This may indicate that STB from female placentas from A2GDM women can better respond to demand for changes in energy requirements than males to increase their chances of survival in an adverse environment. Metformin may benefit STB via affecting increased proton leak: this response observed in male STB following metformin treatment may be due to elevated ROS and act as a protective mechanism in cells against oxidative damage [44]. Male STB from A2GDM women may use this mechanism to limit damage to mitochondrial function and the cell. A recent publication by Ionică et al. demonstrated a reduction in ROS in cardiac tissue isolated from a rat diabetic model following metformin treatment, further strengthening this theory [45]. Elevated extracellular acidification would be expected in these cells; however, surprisingly, acidification was not significant at the metformin concentrations where proton leak data was most significant. One explanation could potentially lie in the fuel flexibility of the placenta, the ability to switch between the use of different classes of substrates. Non-glucose substrates result in more cellular acidification: the fatty acid palmitate can cause 60–$100\%$ of acidification, whereas glucose only accounts for $34\%$ [31]. Male STB from A2GDM women are the least fuel-flexible, using primarily glucose rather than fatty acids to generate ATP, masking the acidification effect caused by proton leak [5]. A lack of fuel flexibility has also been observed as a programming consequence in mouse offspring following maternal metformin treatment [46]. Further explaining masking is another potential mechanism: metformin affects AMPK signaling. The increased AMP:ATP ratio caused by proton leak activates AMPK, which regulates several processes, including mTOR signaling and the phosphorylation of proteins involved in reducing inflammation and acidification [36,47]. Overall, metformin produces little glycolytic response in STB, despite the inhibitory effect on oxidative phosphorylation at 1 mM metformin and above. Metformin concentrations above 0.01 mM increased glycolysis but only in STB from lean women. In contrast, increased glycolytic capacity, in response to energy demand, occurred in the therapeutic range seen with metformin treatment exclusively in female STB from lean women, giving no beneficial effect to STB from pregnancies challenged by obesity or GDM. While metformin causes increased non-glycolytic acidification arising from lactate formation or glycogenolysis in STB from obese or A2GDM women, this occurs above the reported plasma concentration following treatment, so any in vivo effect would be dependent on metformin accumulation in mitochondria. Metformin concentrations in the therapeutic range reduced mitochondrial superoxide release in STB from female placentas of lean and obese women. In obese women, this may be partly due to reduced mitochondrial content compared to sex-matched groups and matches with the reduction in mitochondrial respiration parameters in these groups. We observed no change in STB from A2GDM women regardless of fetal sex in response to increasing metformin. However, at 0.1 mM metformin, female STB of all groups had reduced superoxide release versus controls. This may be due to cellular senescence reducing activity rather than apoptosis. MitoTracker identifies intact mitochondria, including those in senescent cells, while MitoSox identifies mitochondria actively generating superoxide [48]. It is also possible that more mitochondria were formed in STB as seen in endometrial tumor cells in vitro, where metformin increased mitochondrial biogenesis while impairing mitochondrial function [49]. In contrast, lean male trophoblast mitochondria produce higher superoxide concentrations compared to other groups in the therapeutic range of treatment. Metformin may be deleterious to non-A2GDM STB, so a confirmed diagnosis is critical before considering treatment. Previous research indicated that STB mitochondria from A2GDM women were swollen or destroyed [50]. However, our study shows that viable mitochondrial content remained relatively unchanged with metformin treatment. Therefore, metformin may help stabilize A2GDM STB mitochondrial function by maintaining the quantity present, even when A2GDM pathology or metformin treatment impact function. A major strength of this study was the use of primary human STB rather than cell lines and non-human models; furthermore, they were derived from cesarean delivery, which eliminates the effect of oxidative stress induced during labor. Animal studies also produced mixed results regarding metformin treatment efficacy. In mouse studies, the positive effects of metformin on GDM pregnancies include protecting neural cells against apoptosis and neural tube defects and reducing inflammatory reactions caused by angiotensin II, lipopolysaccharides, and cytokines [29]. However, metformin exposure during murine pregnancy also resulted in obese offspring in mice, with males having impaired glucose tolerance when given high-fat diets [46]. Given this, there is a concern for long-term fetal programming of metabolic syndromes following metformin treatment, amplified as we have shown that metformin may impact the placenta and, thereby, fetal development. Further strengths included comparing effects in male and female placentas, knowing that many aspects of placental function are sexually dimorphic, and a comparison of effects in trophoblast from lean, obese, and type A2GDM pregnancies where we have previously demonstrated varying degrees of mitochondrial dysfunction related to oxidative stress which metformin may impact. Human placenta samples were carefully selected from our repository for each group to exclude confounding factors, including asthma, preeclampsia, and multiple gestation. A2GDM patients received insulin as a treatment but were metformin naive. Ethnicity is also a future factor to consider in pregnancy outcomes: maternal obesity has been associated with placental inflammation in Black women [51,52], and our experimental population was primarily Caucasian. ## 5. Conclusions In conclusion, our data suggest that the effect of metformin varies based on maternal condition and fetal sex in vitro, with limited signs of improvement in STB from A2GDM women but potential harm in STB from lean women, particularly when the fetus is male. However, the majority of effects following metformin treatment in vitro were observed at concentrations exceeding those reached in maternal plasma in pregnant women treated with metformin to control their GDM. While it is reassuring that the deleterious effects of metformin on trophoblast respiration occur at concentrations exceeding those reported in maternal plasma in vivo, higher mitochondrial amounts may be reached via accumulation, which could lead to adverse effects and a compensatory glycolytic response, as observed in our assays at supraphysiologic metformin concentrations. 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--- title: Connections between Diabetes Mellitus and Metabolic Syndrome and the Outcome of Cardiac Dysfunctions Diagnosed during the Recovery from COVID-19 in Patients without a Previous History of Cardiovascular Diseases authors: - Cristina Tudoran - Renata Bende - Felix Bende - Catalina Giurgi-Oncu - Alexandra Enache - Raluca Dumache - Mariana Tudoran journal: Biology year: 2023 pmcid: PMC10044929 doi: 10.3390/biology12030370 license: CC BY 4.0 --- # Connections between Diabetes Mellitus and Metabolic Syndrome and the Outcome of Cardiac Dysfunctions Diagnosed during the Recovery from COVID-19 in Patients without a Previous History of Cardiovascular Diseases ## Abstract ### Simple Summary In this original article, we aimed to describe the immense influence of an augmented metabolic risk profile, such as the case of type 2 diabetes mellitus, metabolic syndrome, and obesity, on the evolution of a SARS-CoV-2 virus infection, with a focus on the cardiovascular abnormalities encountered in post-acute COVID-19 syndrome. We demonstrated that during the recovery from COVID-19, the above-mentioned pathologies, associated with an increased inflammatory burden, favor the development of various cardiac alterations—which are diagnosable by transthoracic echocardiography—in previously healthy individuals. At the 3- and 6-month follow-up, we observed that the echocardiographic parameters characterizing the left and right ventricular function, as well as the increased pressure in the pulmonary artery, had improved, which was not the case for diastolic dysfunction (mostly of type 3). These cardiac pathologies, such as the altered systolic and diastolic functions and/or the presence of pulmonary hypertension, could explain—at least partially—the development of long COVID-19 syndrome. Therefore, besides the usual post-COVID-19 assessments, patients with an increased metabolic risk profile should be supplementarily evaluated by a cardiologist, including by a comprehensive echocardiography, both during the acute infection as well as in the recovery period. ### Abstract [1] Background: Throughout the COVID-19 pandemic, it became obvious that individuals suffering with obesity, diabetes mellitus (T2DM), and metabolic syndrome (MS) frequently developed persisting cardiovascular complications, which were partially able to explain the onset of the long-COVID-19 syndrome. [ 2] Methods: Our aim was to document, by transthoracic echocardiography (TTE), the presence of cardiac alterations in 112 patients suffering from post-acute COVID-19 syndrome and T2DM, MS, and/or obesity, in comparison to 91 individuals without metabolic dysfunctions (MD); [3] Results: in patients with MD, TTE borderline/abnormal left (LVF) and/or right ventricular function (RVF), alongside diastolic dysfunction (DD), were more frequently evidenced, when compared to controls (p ˂ 0.001). Statistically significant associations between TTE parameters and the number of factors defining MS, the triglyceride-glucose (TyG) index, the severity of the SARS-CoV-2 infection, and the number of persisting symptoms (p ˂ 0.001) were noted. Significant predictive values for the initial C-reactive protein and TyG index levels, both for the initial and the 6-month follow-up levels of these TTE abnormalities (p ˂ 0.001), were highlighted by means of a multivariate regression analysis. [ 4] Conclusions: in diabetic patients with MS and/or obesity with comorbid post-acute COVID-19 syndrome, a comprehensive TTE delineates various cardiovascular alterations, when compared with controls. After 6 months, LVF and RVF appeared to normalize, however, the DD—although somewhat improved—did persist in approximately a quarter of patients with MD, possibly due to chronic myocardial changes. ## 1. Introduction Ever since the beginning of 2020, as the infection with a new variant of the severe acute respiratory syndrome (SARS-CoV-2) virus spread worldwide and progressed rapidly to an alarming pandemic, it was evident that the severity, prognosis, and mortality rates of COVID-19 varied largely among infected populations [1,2,3]. Surprisingly, a worse evolution, with a large spectrum of systemic complications—often requiring admission in intensive care units (ICUs) and resulting in fatal outcome—was observed, not only in elderly and frail patients that had multiple comorbidities, but also in younger, apparently healthy subjects, especially when they had associated metabolic dysfunctions such as visceral obesity, metabolic syndrome (MS), and type 2 diabetes mellitus (T2DM) [4,5,6]. Currently, it has been proven that theSARS-CoV-2 virus exerts its actions both directly, by binding on the cell surface receptors, but also through immunological mediated effects, by activating the innate and adaptive immunity. Therefore, the virus determines the release of large amounts of proinflammatory cytokines, namely interleukine-6 (IL-6) and interleukine-1β (IL-1β), but also of other acute phase mediators, such as ferritin and C-reactive protein, Figure 1. In some individuals, these immune responses can become exaggerated, resulting in an augmented release of cytokines—namely, the “cytokine storm” [7]. Another potential pathway is mediated via the macrophages, responsible for the initiation of the hypoxia-inducible factor (HIF-1α) [7,8]. Although the lungs represent the first target, it has often been suggested that cardiovascular implications are frequent in COVID-19, both in its acute stages as well as during recovery [9,10]. Initial myocardial damages, such as myocarditis, heart failure, or even necrosis, can be explained by the direct action of the virus on the myocytes and vessels, and the subsequent inflammation, endothelial dysfunction, and ischemia [11]. These pathophysiological processes may persist even during the recovery phase, explaining—at least partially—the development of an interstitial fibrosis, resulting in myocardial stiffening with left ventricular (LV) changes, and determining alterations in cardiac contraction and relaxation—frequently associated with the occurrence of heart failure, with a reduced (HFrEF), or preserved ejection fraction (HFpEF) [11,12], Figure 1. A preexisting enhanced pro-inflammatory risk profile, characterized by metabolic imbalance and augmented inflammatory processes—associated with high levels of IL-6 and IL-1β cytokines, alongside adipose tissue-derived TNFα and leptin in subjects with obesity, MS, and T2DM—contributes to an exacerbated immunologic response of “hyper-inflammation” during COVID-19, with deleterious effects [5,6,13], Figure 1. It has been proven that insulin resistance (IR), favored by elevated cytokine levels, represents the hallmark of T2DM, often long preceding its occurrence, but also characterizing MS and obesity [14,15,16]. In obesity, there is an augmented activity of the IL-6 receptor, which, despite elevated levels of circulating cytokines, results in an exacerbated inflammatory state—namely, the so-called meta-inflammation. Moreover, in this metabolic pathology, IR favors the infiltration of the adipose tissue with macrophages, particularly of the highly inflammatory type M1 subpopulation [17,18]. Therefore, lipotoxicity- and glucotoxicity-modulated IR tends to amplify the cardiovascular risk in patients with metabolic dysfunctions, thus favoring the development of systemic hypertension, and left ventricular hypertrophy, frequently associated with DD [19,20]. It, therefore, becomes easy to assume that, in the situation of an already exacerbated inflammatory background, an additional inflammatory burden, such as COVID-19, would trigger multiple systemic injuries, with a worse evolution [21,22,23]. Additionally, as already debated in the medical literature, in some patients recovering from this infection—especially when they have also been impacted by comorbid T2DM, MD, and obesity—the restoration of normal immunologic responses becomes deficient or/and delayed, further resulting in an immunologic depression [17,24,25]. These augmented immune responses may persist during the recovery phase, and, in some cases, a reactivation of the viral infection has been described via mechanisms that, in aggregate, could be responsible for the development of post-COVID syndrome, which is characterized by a persistence of a large spectrum of symptoms [26,27]. As expected, specifically cardiovascular complications tend to occur more frequently and have a worse evolution in individuals with an unfavorable metabolic risk profile, even when there is no pre-existing history of cardiac diseases or other health issues [22,26]. Even in patients who are free of any identifiable cardiovascular complications during the acute phase of COVID-19, subtle abnormalities may be diagnosed by means of TTE, which seem to persist long into the recovery [28,29]. Our objective was to document the presence of any cardiac alterations assessed by TTE in post-acute COVID-19 syndrome patients during their recovery from a mild/moderate infection. These individuals were identified to have an increased cardio-metabolic risk profile due to metabolic dysfunctions, but undiagnosed with cardiovascular diseases. Another aim was to highlight the potential connections between the severity and evolution of these TTE abnormalities, and several clinical and laboratory parameters characterizing the amplitude of the metabolic imbalance and COVID-19 consequences as well. ## 2.1. Study Population We evaluated 203 patients suffering with post-acute COVID-19 syndrome between March 2021 and March 2022 in the ambulatory services of our hospital. A longitudinal study was conducted; patients who met the study’s inclusion criteria were identified and subsequently followed for a period of 6 months from the first evaluation. The initial evaluation was performed using clinical criteria, laboratory tests, ECG, and TTE, and afterward, all subjects were reevaluated clinically and by TTE at 3 and 6 months, respectively. The patient sample was selected from a larger population of 483 COVID-19 convalescents who attended our medical services for various non-specific complaints, with the most frequent ones being reduced physical exertion capacity, persisting fatigue, palpitations, elevated blood pressure levels, chest discomfort or even pain, dyspnea, dry cough, sleep disturbances, foggy brain, and concentration issues. After a detailed clinical examination of all patients, they were diagnosed as having post-acute COVID-19 syndrome, based on the persistence of symptoms for more than 4 weeks after the onset of the acute infection, but for less than 3 months. Those found with various physical sequelae and/or significant abnormalities were referred for further investigations and appropriate interventions. Of the remaining subjects, we identified 283 individuals younger than 55 years who reported an adequate health status before contracting the SARS-CoV-2 infection, without any history or signs suggesting preclinical or clinical cardiovascular diseases, nor previous therapies for chronic diseases, but with an inappropriate cardiometabolic risk profile [30,31,32,33,34,35]. We offered them the opportunity to take part in our study and to undergo further medical examinations, including lab tests, electrocardiogram (ECG), and TTE, once we ensured they fulfilled our inclusion/exclusion criteria and accepted to sign an informed consent form. Patients were requested to provide a discharge letter or an ambulatory evaluation certifying this infection, confirmed by a positive result of real-time reverse transcriptase–polymerase chain reaction (RT-PCR) testing of pharyngeal and nasal swabs, with additional lab tests, pulmonary radiography or thoracic CT scans, ECG evaluation, as well as recent medical documents, containing a physical exam, ECG and TTE results (even in an abbreviated form), and laboratory data (lipid panel, fasting blood glucose and uric acid levels), to attest their previous health status. We required these results in order to confirm or exclude a baseline metabolic dysfunction. A subsample of 238 subjects agreed to take part in our study and were able to provide the necessary documents, while also fulfilling the inclusion criteria, which were as follows: (a) apparently healthy subjects, in the age range of 18–55 years, who were able to understand and sign the informed consent form; (b) the evidence of a recent mild/moderate SARS-CoV-2 infection, certified by a positive result of a RT-PCR assay with a baseline medical assessment, including laboratory blood tests, ECG, and chest radiography or CT scan, which allowed us to classify the severity based on the extent of the lung injury, as follows: 0–$15\%$ defining mild, and 15–$40\%$ moderate forms; (c) the availability of a recent medical evaluation (of less than one year) indicating a lack of chronic health issues, significant cardiovascular diseases, or therapies for various metabolic illnesses, even if they were occasionally found with elevated BBG, abnormal lipids panel, or if they were obese or overweight, meeting the criteria for MS. The exclusion criteria consisted of the following: (a) subjects not able or willing to sign the informed consent; (b) individuals older than 55 years, with an increased probability to have a significant underlying cardiovascular condition; (c) those recovering from a severe form of COVID-19 illness, with certified cardiovascular complications during the acute illness or those with asymptomatic forms or without a medical evaluation during the infection; (d) patients previously confirmed with cardiovascular diseases or being treated for a chronic disease or diagnosed during the initial assessment with a significant cardiac dysfunction; (e) subjects already registered and managed for T2DM; (f) a lack of recent medical assessments. ## 2.2. Study Procedures and Clinical and Laboratory Examinations (a) The borderline LV function (LVF) was appreciated from an apical 2-, 3-, and 4-chamber view by determining the following:−The Left Ventricular ejection fraction (LVEF), calculated according to the Simpson method (modified) formula (results under $50\%$ considered as pathological).−The MAPSE (lateral mitral annular plane systolic excursion), with values lower than 10 mm appreciated as pathological.−We assessed the Left Ventricular global longitudinal strain (LV-GLS) by speckle tracking, and automatically generated the ROI (region of interest) after tracing the Left Ventricular endocardial border, with manual adjustments as needed, in order to adjust the width of the LV wall [8,19]. An impaired LVF was represented by values lower than −$18\%$, while scores between −18 and −19 were considered as borderline values [9,11]. (b) The right ventricular function (RVF) and estimated systolic pulmonary artery pressures (sPAP) were also assessed from an apical 4-chamber view: −We measured the TAPSE (tricuspid annular plane systolic excursion), in M-Mode, at the lateral tricuspid valve annulus level, and considered values below 17 mm as abnormal.−From an apical view, we determined the FAC (fractional area change), and deemed any scores lower than $35\%$ as representative for a Right Ventricle dysfunction (RVD).−By using strain techniques and employing the same view, the RV global longitudinal strain was assessed; values lower than −$28\%$ certified an RVD [28,29].−We determined the sPAP by looking at the velocity of the peak tricuspid regurgitation (TRV) assessed by a continuous Doppler, while considering the pressure in the right atrium (RAP), appreciated in terms of the diameter of the inferior vena cava (IVC) as well as its respiratory differences. For this study, any resting sPAP values above 35 mm Hg were suggestive of a PH [36], with severities in the mild (35–44 mmHg) to either a moderate (45–60 mmHg) or severe range (above 60 mmHg). (c) The Left Ventricular diastolic dysfunction (DD) was measured with the following parameters:−The volume index of the left atrium (LAVI) was measured from an apical 4-chamber view, with scores above 34 mL considered pathological.−At the mitral valve level, we used a pulsed Doppler with a similar interpretation for recording mitral inflow and measuring the early peak diastolic velocity (E), as well as the late diastolic velocity (A); subsequently, an E/A ratio was calculated.−We used tissue Doppler imaging (TDI) at the septal and lateral mitral annulus levels to measure early (e’) and late diastolic velocity (a’); average and E/e’ ratios were subsequently calculated. Type I DD was indicated by an E/A ratio ≤ 0.8 and E ˂ 50 cm/s; a type III DD consisted of an E/A ratio above 2. Any E/A ratios lower than 0.8, alongside E values above 50 cm/s, or E/A scores of between 0.8 and 2, suggested a type II DD by at least two of the following three criteria: average E/e’ values above 14, LAVI over 34 mL/m2, and/or a TRV above 2.8 m/s. A type I DD was indicated when only one of the above criteria were present [11,42]. (d) We used standard views to assess the presence/amount of the pericardial exudation (PE), and/or of the width of the posterior pericardium (PT) [29]. To quantify the physical consequences of the SARS-CoV-2 infection based on the number of persisting symptoms and to evaluate the rehabilitation process, we used the Post-COVID-19 Functional Status (PCFS) assessment scale. This methodology was created to quantify the amplitude of functional limitations. Based on this assessment tool, the absence of symptoms/limitations is quoted as 0; discreet limitations of quotidian activities associated with few symptoms represents a 1; a slight limitation, but with more significant symptoms is scored as a 2; a moderate limitation, associated with the inability to perform usual activities due to persistent symptoms, but still capable to take care of themselves without someone’s help, is scored as a 3; a severe physical limitation due to severe symptoms requiring support for taking care of themselves is scored as a 4 [46]. ## 2.3. Statistical Analysis We used the MedCalc Version 19.4 (MedCalc Software Corp., Brunswick, ME, USA) and Microsoft Office Excel 2019 (Microsoft for Windows) for the statistical analysis, while for the demographic, anthropometric, and clinical data, patients’ descriptive statistics were used. The sample size determination was performed using α as the selected level of significance and Z 1-α/2 as the value from the standard normal distribution holding 1-α/2 below it. We used the following parameters: α = 0.05, therefore 1-α/2 is 0.975 and Z is 1.960. Using these parameters, a sample size of 134 or more subjects was defined as statistically significant. The distribution of numerical variables was tested using the Kolmogorov–Smirnov test and continuous numerical variables with normal distributions were presented as means with standard deviations (SDs); in case of variables with non-normal distributions, we employed median and interquartile ranges (IQRs); the categorical variables were communicated as frequencies and percentages. The Student’s t-test was utilized for group comparisons of continuous variables with normal distribution; for variables with non-normal distribution, we applied a Mann–Whitney U-test. We employed Pearson’s χ2-test for categorical variables group comparisons. We used Spearman’s correlation test to gauge the associations between the LV-, and RV-GLS, as well as the E/e’, PT (pericardial thickness), and several other demographic, anthropometric, laboratory, and echocardiographic findings. We deemed a p-value lower than 0.05 as significant for all statistical analyses. The uni-and multivariate regression analyses were used for the classification of any objective predictors of an occurrence of cardiac deviations. Three multivariate regression models were built using the Akaike criterion to assess the impact of several factors on the variance of continuous variables, and the model was validated based on the accuracy of prediction and R squared. In the final regression equations, the predictors were accepted according to a repeated backward-stepwise algorithm (inclusion criteria $p \leq 0.05$, exclusion criteria $p \leq 0.10$) to obtain the most appropriate theoretical model to fit the collected data. The Local Scientific Research Ethics Committee of the hospital approved our study ($\frac{206}{07.2020}$ and $\frac{297}{11.04.2022}$). ## 3. Results Our study was conducted on 203 patients, with ages ranging from 26–55 years old, and a mean age of 47.06 ± 7.65 years, including 82 men ($40.39\%$) and 121 women ($59.60\%$), all of whom were diagnosed with a SARS-CoV-2 virus infection 63 [56–70] days prior to attending this medical evaluation. In terms of any identifiable metabolic dysfunctions, they were allocated to three subgroups: group I included 46 patients with DM and MS, group II had 66 subjects with MS, while group III consisted of 91 individuals with normal weight or who were overweight, but without clinical MS or obesity. All the patients’ clinical characteristics are presented in Table 1, and all laboratory data are available in Table 2. TTE parameters, as analyzed during the first evaluation, can be seen in Table 3. Group I included 46 patients (16 men and 30 women), with an average age of 52 ± 3.54 years old; most of them had elevated BMI, with a median value of 30.96 [29.22–32.86] kg/m2, while 29 were estimated to pertain to the obesity category (24 of them in the 1st degree, 4 of them in the 2nd degree, and only one patient in the 3rd degree obesity category); 13 patients were overweight, with a BMI of between 25 and 30 kg/m2, and only 4 other patients were of normal weight. Although, on various occasions, our patients had elevated blood pressure and/or BBG values, they were not suitably diagnosed with T2DM and MS prior to their COVID-19 infection. All patients in this group had associated MS, with a number of defining factors of between 4 and 6, and an average of 5 criteria. During the acute SARS-CoV-2 infection stage, 31 patients suffered a pulmonary injury, affecting around 5 to $40\%$ of their lung parenchyma, with a median of $15\%$ [15–30], thus indicating more severe COVID-19 forms (14 subjects had moderate forms, 32 had mild forms). Consequently, most of them reported multiple persisting symptoms, the average number being of 6 [3.75–7], and also had higher PCFS levels, of 2 [1–3]. As expected, they had significantly higher values of laboratory parameters indicating metabolic dysfunctions than group III ($p \leq 0.0001$), while the differences, when compared with the data registered for group II, were significant for LDL-cholesterol and eGFR and lipid accumulation product (LAP), especially for the BBG and triglyceride-glucose (TyG) index ($p \leq 0.0001$), but not for the visceral adiposity index (VAI) (see Table 2). Regarding the presence of altered TTE parameters, when we performed a comprehensive echocardiographic assessment, we identified several patterns of cardiac abnormalities, even though these patients were not diagnosed with cardiac dysfunctions during the acute COVID-19 infection phase (based on an abbreviated TTE exam). Although all patients from group I had LVEF values above $50\%$ and their MAPSE was not lower than 10, by using strain techniques, we identified 27 patients with borderline LV-GLS values (from −18 to −19) and an LVEF below $60\%$. An RVD was identified in 16 patients, with 9 patients also showing slightly elevated PAPs. When referring to the presence of type 1 DD, this was identified in 19 subjects, with type 2 DD in 9 cases, and type 3 DD seen in 3 patients. A thickened pericardium, of between 3 and 4 mm was evidenced in 12 subjects, with one patient also showing a small amount of pericardial effusion (4.8 mm). Group II included 66 patients (29 men and 37 women), with a mean age of 51.07 ± 4.77 years, all diagnosed with MS, but without T2DM. Their median BMI was of 29.48 [27.49–31.32]. Of them, 30 had obesity (27 patients of a 1st degree, 3 patients of a 2nd degree), 29 were overweight, and 7 had a normal weight. Although none of them had T2DM, all were considered to have MS, as defined by at least 3 factors, with a median value of 4 [4–5]. During the acute phase of COVID-19, 42 patients had sustained pulmonary injuries, with a median value of $10.5\%$ [0–30], which would explain why only 20 subjects suffered from moderate forms of the disease while the remaining had mild forms. At the first presentation, they reported between 2 and 9 persisting symptoms, with an average of 5 [3–7], and a median PCFS scale value of 2 [1–3]. All their laboratory test results, TyG index, VAI, and LAP were significantly higher than those reported in group III ($p \leq 0.0001$) (as seen in Table 2). In terms of cardiac abnormalities, as identified by TTE, 33 patients had borderline LVF, certified by LV-GLS (−18 and −19), with 32 of them also showing LVEF values lower than $60\%$. Regarding the pattern of RVD, 14 patients had a reduced RV-GLS, while 11 also had elevated PAPs. The DD pattern was identified in 37 of patients (of whom, 20 patients had a type 1, 15 patients type 2, while 2 patients had type 3 DD). A thickened pericardium was detected in 12 patients, while one patient also showed a slight pericardial effusion. Group III included 91 younger individuals, with ages ranging from 26 to 55 (an average age of 41.67 ± 7.44 years), of which 37 were men and 54 women. Although none of them had T2DM, MS, or obesity, their median BMI was of 24.38 [22.56–26.8] kg/m2, thus 30 could be considered overweight, while 66 of them had at least one element that is included in the definition of MS; only 25 subjects were in the heathy ranges. Throughout the acute phases of the SARS-CoV-2 infection, the majority suffered mild forms of illness; 15 patients had moderate and 15 had mild lung injuries, so that the median value of the pulmonary impairment was $0\%$ [0–6]. Generally, they had less complaints, reported between 2 and 8 persisting symptoms, with a median of 3 [3–6], and had lower PCFS levels—namely an average of 1 [1–3]. Although more than half of them had at least one or two factors that define MS (mostly, lower HDL-cholesterol levels), their median laboratory values were within normal ranges (as seen in Table 2). In this subset of patients, significantly fewer cardiac abnormalities were assessed by TTE. For 21 subjects, we evidenced borderline LVF, with LV-GLS values of −18 and −19; 20 of them had an LVEF under $60\%$. Reduced RV-GLS values were seen in 6 subjects, with 3 of them also having slightly increased PAPs. A DD was determined in 20 patients (type 1 in 14 cases, and a type 2 in 6 subjects). Eight patients had slightly thickened pericardia, while one of them had a slight pericardial effusion. When analyzing the existence of statistical correlations between the main TTE patterns identified in our patients and several clinical and laboratory parameters, we noticed that the LV-GLS was moderately, but statistically, significantly correlated with the patients’ age, inflammatory markers (namely, CRP, PCFS levels), the number of elements defining MS (TyG, LAP, BMI), and the severity of lung injuries (p ˂ 0.0001) (Table 4). RV-GLS was strongly correlated with acute infection severity, namely with pulmonary damage, as assessed on the CCT scan, and with CRP levels (p ˂ 0.0001); moderate but statistically meaningful correlations appeared regarding the patients’ age, days since diagnosis, PCFS levels, and the number of factors defining MS, TyG, and LAP (p ˂ 0.0001). The E/e’ ratio was strongly correlated with the intensity of inflammation, as expressed by the initial CRP value, and only moderately—but significantly—with the PCFS levels, the severity of the pulmonary injury and time since diagnosis, the number of elements defining MS, the patient’s age, as well as their LAP, TyG, VAI, and BMI (p ˂ 0.0001) values. Pericardial thickness was moderately, but significantly, correlated with the severity of the acute infection, as expressed by the degree of pulmonary damage, and the initial CRP levels, and also with the number of days elapsed from initial diagnosis, and PCFS levels (p ˂ 0.001) (as seen in Table 4). Regarding the development of these abnormalities, as assessed by TTE, a meaningful progress could be noted in all study groups (see Figure 2). Thus, in group I, only eight subjects still had borderline LV-GLS at 3 months, while at 6 months these values had normalized; one patient had pathological RV-GLD, while seven had slightly elevated PAPs, however, the RVF normalized at 6 months, with two subjects still showing PAPs borderline values ($p \leq 0.0001$). Concerning the evolution of the DD, this was less favorable at 3 months. Overall, 24 patients still showed a DD pattern (14 patients of type 1, 8 patients of type 2, and 2 patients of type 3), while at 6 months, a DD was identified in 18 patients (in 10 cases, there was a type 1, in 7 cases, a type 2, while in 1 case, there still was a type 3). It is worth mentioning that its decline was not statistically significant ($$p \leq 0.0216$$) (as seen in Figure 1). For group II, there was a similar evolution. The LVF appeared to gradually recover, so that at 3 months, only four patients still had borderline values of LV-GLS that normalized by the 6-month evaluation ($p \leq 0.0001$). RVD improved significantly, so that after 3 months, only one patient still had pathological RV-GLS values, while two patients had slightly elevated PAPs, which disappeared after 6 months ($p \leq 0.0001$). Regarding the DD evolution, this persisted at 3 months for 30 patients (21 of whom had a type 1, 8 a type 2, and 1 a type 3); at the 6-month evaluation, there still remained 14 subjects with a notable DD (7 cases with a type 1, 6 patients with a type 2, and only 1 with a type 3). For group III, the 3-month evaluation showed that only one subject still had borderline LV-GLS values, which normalized at 6 months; the RVD and sPAP appeared to be in normal ranges already after 3 months ($p \leq 0.0001$), however, DD still was notable at 3 months for 17 patients (14 of whom showed a type 1 DD, while 3 patients showed a type 2 DD); at the 6-month evaluation, 5 patients still suffered from various DD (4 patients had a type 1, while 1 had a type 2 DD, $$p \leq 0.0016$$). In order to identify the independent predictors that could influence the initial and the six months’ values of LV-GLS, RV-GLS, PAPs, and DD frequency, we used the multivariate direct regression examination method for constructing regression models centered on the forward stepwise technique; the Akaike information criteria (AIC) was used for the selection of the most appropriate model. We excluded data regarding age, BMI, or gender from this analysis, considering that previous studies have already proven that they are strong negative predictors of the aforementioned parameters; therefore, they were considered confounding factors. Consequently, the following parameters were tested in the multivariate regression analysis: CRP, TyG index, BBG, the number of elements defining MS, LAP, and VAI. The independent predictors associated both with the initial and the six months’ values of LV-GLS, RV-GLS, PAPs, and DD frequency are summarized in Table 5. As visible in Table 5, from all factors included in our regression models, the highest statistical significance for both the initial and the values at 6 months for LV-GLS, RV—GLS, PAPs, and DD was found for the initial level of inflammation (as expressed by CRP serum concentrations), and especially for the TyG index levels (p ˂ 0.0001). ## 4. Discussion In individuals with metabolic dysfunctions, such as obesity, MS, and T2DM, a worse evolution of COVID-19, associated with increased morbidity due to severe complications—frequently requiring ICU admission—and a higher mortality rate, has been discussed in multiple studies, as well as some sizable meta-analyses, ever since the beginning of 2020 [30,31,32,33]. Initially, obesity was considered as an objective health risk-factor for higher morbidity and mortality levels, with the risk, apparently, proportionally increasing with BMI [5]. Thus, it is worth noting that individuals with obesity frequently have associated MS, or even T2DM, which further increases their health risk, favoring the development of various respiratory, cardiovascular, and multi-systemic complications during the course of COVID-19 [5,23,34]. Moreover, it should be highlighted that for this patient category, the inappropriate cardio-metabolic risk profile renders them susceptible toward a delayed/deficient restoration of the immune homeostasis, with the persistence of exaggerated inflammatory processes, responsible for the development of post-acute as well as long COVID-19 conditions [5,28]. The importance of quantifying the increased risk profile determined by these metabolic dysfunctions has become evident and, since IR is their common pathophysiological hallmark, its fast and precocious assessment represents an important issue. Because the hyperinsulinaemia-euglycemia clamp technique, considered the golden standard for the quantitative measurement of IR, is time-consuming and costly, the TyG index has been accepted as an alternative for determining IR [35,36]. Moreover, during the recent pandemic, in some notable reports, a bidirectional relationship between the SARS-CoV-2 infection effects and IR has been evidenced, since this disease appears to favor the IR and β-cell damage due to the release of IL-1β and TNF-α [37,38]. The study of Chang et al. demonstrated a significant association between the TyG index, as determined before COVID-19, and an elevated risk for severe complications during the acute infection [39]. In the same vein, some indexes such as the LAP and VAI—characterizing the abdominal obesity phenotype, which is associated with an impaired risk profile—were considered indicators for a worse COVID-19 outcome [33,40,41]. Starting from these observations, in our study, by analyzing the relationship between these indexes and the TTE parameters characterizing LVF, RVD, and DD, we evidenced statistically significant correlations ($p \leq 0.0001$) for all of them, but especially for the TyG index, the number of elements defining MS, the level of inflammation (as expressed by the initial CRP values), and the post-acute COVID-19 condition gravity (as quantified by the PCFS scale). As expected, individuals with T2DM and/or MS in our study showed higher levels of these indexes when compared with controls ($p \leq 0.0001$). According to recent literature studies, as well as concordant with our data, an inappropriate cardio-metabolic risk profile and the IR (as quantified by the TyG index) would predict a worse outcome, with a higher prevalence of cardiovascular complications and an extended recovery period due to the various post-COVID-19 syndrome implications [4,23,33,36]. Our research is based on the assumption that even in previously apparently healthy individuals with an inappropriate cardio-metabolic risk profile, undiagnosed(neither before, or during the acute phase of infection) with a significant cardiovascular pathology at a routine TTE, some subtle cardiac deviations could exist, identifiable only in a comprehensive TTE assessment, or, even more accurately, by more sophisticated imaging techniques, abnormalities that could favor the onset of the post-COVID-19 condition [42]. There is a general consensus attesting the wide range of cardiac alterations, identified by TTE, during the infection with the SARS-CoV-2 virus [9,12,43]; their persistence and evolution during the recovery phase are largely debated in current medical literature [28,44]. By means of TTE, in our study, we have managed to identify four main patterns of cardiac alterations. Even in subjects who were classified as having a TTE exam “within normal limits”, we frequently evidenced “borderline” values of the parameters characterizing LVF, RVD, PH, and DD, specifically, in almost half of those with T2DM, MS, and/or obesity. The novelty of our manuscript is that these individuals who are overweight or have grade 1 obesity, and are considered by their GP and by themselves as “apparently healthy”, frequently also have insulin-resistance and can easily fulfil three of the criteria defining MS. This high-risk metabolic profile is often overlooked, and in case of a COVID-19 infection, these patients are prone to develop cardiovascular alterations and post-COVID-19 syndrome. Fortunately, these cardiovascular abnormalities appeared to have been alleviated over time, so that at the 6-month follow-up, the majority of our subjects had predominantly normal values, except most of those with an identifiable DD, where some abnormalities tended to persist longer, raising the suspicion that, due to enduring inflammation, some interstitial fibrotic changes could have occurred in the myocardium, inducing progressive remodeling and stiffening, with an altered relaxation of the cardiac muscle [27,44,45]. Our study’s chief limitation emerges from an unavailability of a detailed TTE exam performed before and during the SARS-CoV-2 virus infection. Although we only selected individuals with a previous TTE examination, we also accepted succinct formulations, such as “within normal limits”, “incipient LVH”, but in most cases, precise measurements of LVMI, LV-GLS, RV-GLS, E/e’ ratio, LAVI, TRV, and sPAP were missing. Consequently, we cannot affirm with the utmost certainty that the subtle TTE abnormalities that we have identified in our study population, such as borderline LVF, mild RVD and/or PH, increased LAVI, or even DD, did not precede the infection, worsening during the acute phase of illness or even during the recovery phase, which appears all the more likely, seeing as a high percent of the individuals included in our study suffered from T2DM, MS, and/or were obese or overweight. ## 5. Conclusions For people suffering from diabetes mellitus and/or metabolic syndrome—even for those considered apparently healthy before the infection with the SARS-CoV-2 virus—there is a higher probability to develop a post-COVID-19 syndrome, requiring a longer recovery period, at least partially explained by the existence of subtle cardiac abnormalities that can be evidenced by a comprehensive TTE exam. 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--- title: Synthesis and Evaluation of Rutin–Hydroxypropyl β-Cyclodextrin Inclusion Complexes Embedded in Xanthan Gum-Based (HPMC-g-AMPS) Hydrogels for Oral Controlled Drug Delivery authors: - Abid Naeem - Chengqun Yu - Zhenzhong Zang - Weifeng Zhu - Xuezhen Deng - Yongmei Guan journal: Antioxidants year: 2023 pmcid: PMC10044933 doi: 10.3390/antiox12030552 license: CC BY 4.0 --- # Synthesis and Evaluation of Rutin–Hydroxypropyl β-Cyclodextrin Inclusion Complexes Embedded in Xanthan Gum-Based (HPMC-g-AMPS) Hydrogels for Oral Controlled Drug Delivery ## Abstract Oxidants play a significant role in causing oxidative stress in the body, which contributes to the development of diseases. Rutin—a powerful antioxidant—may be useful in the prevention and treatment of various diseases by scavenging oxidants and reducing oxidative stress. However, low solubility and oral bioavailability have restricted its use. Due to the hydrophobic nature of rutin, it cannot be easily loaded inside hydrogels. Therefore, first rutin inclusion complexes (RIC) with hydroxypropyl-β-cyclodextrin (HP-βCD) were prepared to improve its solubility, followed by incorporation into xanthan gum-based (hydroxypropyl methylcellulose-grafted-2-acrylamido -2-methyl-1-propane sulfonic acid) hydrogels for controlled drug release in order to improve the bioavailability. Rutin inclusion complexes and hydrogels were validated by FTIR, XRD, SEM, TGA, and DSC. The highest swelling ratio and drug release occurred at pH 1.2 ($28\%$ swelling ratio and $70\%$ drug release) versus pH 7.4 ($22\%$ swelling ratio, $65\%$ drug release) after 48 h. Hydrogels showed high porosity ($94\%$) and biodegradation ($9\%$ in 1 week in phosphate buffer saline). Moreover, in vitro antioxidative and antibacterial studies (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli) confirmed the antioxidative and antibacterial potential of the developed hydrogels. ## 1. Introduction Flavonoids consist of a wide variety of naturally occurring compounds. The variable phenolic structures found in plants include flavonols, anthocyanidins, flavan-3-ols, flavones, flavanones, and isoflavones. These substances are found in vegetables, stems, fruits, grains, roots, barks, and flowers and have received considerable scientific and therapeutic attention [1]. Since ancient times, flavonoids have played a fundamental role in several treatments. Several flavonoids can scavenge free radicals, enhance antioxidant functions, possess anti-inflammatory properties, exhibit anti-tumor properties, and display anti-thrombogenic properties and antibacterial properties [2]. Rutin (RT), a flavonoid, also called vitamin P, can be found in a wide variety of foods, including apples (350 to 4780 μg/g), citrus fruits (2.7 to 8106.7 µg/g), and tea (303 to 479 μg/g) [3]. Rutin is an important metabolite of Ruta graveolens [4]. It exhibits strong antioxidant properties (which are comparable to those of ascorbic acid and butylated hydroxytoluene), anti-inflammatory, antiviral, anticancer, antidiabetic, asthma-reducing, antibacterial, cardiovascular, cholesterol-lowering, and neuroprotective properties [5]. As a result of its polyhydroxy structure, rutin is susceptible to various environmental factors, including temperature (above 75 °C) and pH (in high acidic and alkaline environments). Moreover, rutin is poorly soluble due to the presence of the benzene ring and the hydroxyl group in the same molecule, thereby limiting its use and applications [6]. The use of rutin can be improved by resolving the problem of its solubility. The bioavailability and absorption of drugs are significantly affected by solubility and gastrointestinal permeability [7]. The antioxidant rutin is a quercetin glycoside that cannot be absorbed in its natural form due to its structure [8]. Rutin is primarily hydrolyzed by the caecal microflora in the large intestine [9]. Pure rutin has a bioavailability of approximately $20\%$ when taken orally [10]. Consequently, rutin is not very bioavailable due to its poor solubility in aqueous media (0.8 mg/mL) [11]. The development of novel drug delivery methods will ensure the bioavailability of this promising natural molecule, thereby enabling it to be used to treat chronic human diseases. Even though rutin is widely used in the pharmaceutical and nutraceutical industries, there have only been a limited number of studies that have explored how to increase its dissolution rate and, ultimately, its bioavailability. This can be accomplished by preparing coprecipitated microparticles or inclusion complexes that contain a hydrophilic polymer [12]. Inclusion complexes can be formed by utilizing cyclodextrins (CDs), cyclic oligosaccharides commonly used for stabilizing and protecting several active compounds, masking their unpleasant odor and taste, and increasing dissolution rates in pharmaceutical and nutraceutical products. The structure of a CD is truncated and resembles a cone [13]. Their hydrophilic external surfaces make them fully water soluble, whereas their hydrophobic interior cavities allow them to accommodate molecules of different sizes, shapes, and degrees of hydrophobicity [14]. Guest/host inclusion complexes are formed primarily through non-covalent interactions, such as hydrophobic and hydrogen bonds and van der Waals forces [15]. Compared to other CDs, α-cyclodextrin does not have a sufficient cavity to encapsulate most active molecules, while γ-cyclodextrin is not economically feasible due to the high cost. Thus, β-cyclodextrin (β-CD) has become the most widely used one [16,17]. 2-Hydroxypropyl-β-cyclodextrin (HP-βCD) represents a chemically modified version of β-cyclodextrin exhibiting enhanced safety properties compared with its naturally occurring parent compound [18]. HP-βCD, a water-soluble oligosaccharide that has been listed as generally recognized as safe (GRAS) by the FDA, has shown promising results in enhancing flavonoids’ solubility [19,20]. In a study conducted by Miyake et al., it was demonstrated that rutin was more soluble and bioavailable when complexed with HP-βCD [21]. Many limitations are associated with traditional drug dosage forms, including dose and delivery limitations arising from fluctuations in drug plasma levels beyond and below the therapeutic range. As a result, this effect will differ according to the biological half-life of the drug, frequency of administration, as well as the rate at which it is released. In addition, chronic diseases require a multi-dose regimen in which patients are unable to adhere to dosing frequencies. As a result of fluctuating plasma drug concentrations and poor patient compliance, conventional dosage forms are being replaced by controlled release dosage forms [22]. Controlled release drug delivery systems (CDDS) offer many advantages, including the capability of maintaining a steady concentration of the drug at the target site, improving patient compliance by reducing dosages and dosage frequency, reducing side effects, and delivering drugs effectively, etc. [ 23]. Controlled drug release may be achieved by using polymers with known release profiles. In this manner, drug release rate fluctuations are minimized and effectively delivered [24]. Generally, controlled drug release systems are designed to deliver drugs to a targeted area at a predetermined rate for a specified period of time in order to maintain the desired concentration and improve therapeutic efficacy [25]. Hydrogels consist of a crosslinked network of hydrophilic polymers capable of absorbing water solutions without being solubilized [26]. Hydrogels have been used extensively in a variety of fields, including agriculture, food, biotechnology, and pharmacy. Hydrogels are capable of absorbing and releasing substantial amounts of drugs as a result of their swelling properties. Hydrogel swelling behavior directly affects the release rate of the drug [27]. Additionally, hydrogels have several limitations in addition to these advantages. For example, many hydrogels have low mechanical strength, which prevents them from loading drugs, leading to early dissolution or egress from the targeted area. Gels can only be loaded with hydrophilic drugs, whereas hydrophobic drugs may pose problems, such as inadequate amounts of drug loading and decreased homogeneity [28]. Another factor to consider is the rapid and early release of drugs by some hydrogels. Hydrogels are characterized by large pores and high-water contents, which may lead to burst releases of their contents [29]. The advent of natural polymers, particularly polysaccharides, has influenced researchers in recent years to design dosage forms that are biodegradable, biocompatible, renewable, and non-toxic [30]. Xanthan gum is secreted by Xanthomonas campestris, and it is an anionic polymer because it contains both glucuronic and pyruvic acid groups. It has attracted considerable attention in the past few years due to its versatility, particularly in critical environments with acidic conditions, high salt levels, and high shear stresses [31]. Additionally, it can conjugate itself to other polymers, peptides, proteins, and nonpeptides, resulting in conjugates that can withstand enzyme degradation and are biocompatible and easily soluble. Since it has a high affinity for water, xanthan gum enhances the solubility of hydrophobic drugs and carriers. In vitro studies have shown that small quantities of xanthan gum can also decrease the rate at which drugs are released [32]. Hydroxypropyl methylcellulose or HPMC, belongs to the hydrophilic class of polymers that can be used to make oral drug delivery systems. It is characterized by a significant degree of swelling and surface activity. Drug release from a hydrogel is affected by its swelling properties. When HPMC polymer chains come into contact with water, they relax and expand, causing drugs to diffuse from the hydrogel [33]. In addition, polymer adsorption to drug surfaces strongly depends on surface activity. It has been shown that the surface activity of hydrophobic drugs is significantly influenced by the adsorption of cellulose ethers containing hydroxypropyl groups. Polymers such as HPMC are often used in solid drug dispersions as rate-controlling agents [34,35]. 2-acrylamido-2-methylpropanesulfonic acid (AMPS), a hydrophilic ionic monomer, plays an essential role in preparing hydrogels for drug delivery. Due to the presence of ionizable sulfonic groups in AMPS-based hydrogels, they exhibit a pH-independent swelling behavior. All of these sulfonic groups are completely dissociable irrespective of pH. Moreover, increasing the concentration of AMPS increases the swelling index of hydrogels because of the increase in the number of ionic groups in the hydrogels [36]. Ethylene glycol dimethacrylate (EGDMA) was utilized to crosslink the hydrogels. Based on the above considerations, the objective of this study was to prepare a hydrogel based on polysaccharides, namely xanthan gum (XG) and hydroxypropyl methylcellulose (HPMC), loaded with hydrophobic drug (rutin) inclusion complexes with 2-hydroxypropyl-β-cyclodextrin (RIC) for oral drug delivery and to control the release of rutin for a prolonged period of time in order to improve its bioavailability. A variety of techniques were used to characterize hydrogel’s structure, including Fourier transform infrared spectroscopy (FTIR), X-ray diffraction (XRD), differential scanning calorimetry (DSC), thermogravimetric analysis (TGA), porosity of the gel network, sol–gel fraction analysis, volume fraction of polymer (V2,s), crosslinking molecular weight (Mc), and biodegradation. Hydrogel swelling and release behavior were also examined using different polymer and crosslinker concentrations in different pH media (pH 1.2 and pH 7.4). Additionally, rutin inclusion complex-loaded hydrogels were evaluated for their antioxidant activities (DPPH and ABTS assays) as well as their antibacterial (Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli) properties. ## 2.1. Materials Hydroxypropyl methylcellulose (HPMC; MW 470.5 g/moL) was obtained from Rhawn chemical technology, Shanghai, China. Xanthan gum (Viscosity = mPa·s ≥ 1200, MW: 1016.8 g/moL) was obtained from cool chemical science technology, Shanghai, China. Rutin (≥$98\%$; MW: MW: 610.5 g/moL) and sodium bisulfite (SHS) were procured from Shanghai Aladdin biochemical technology, Shanghai, China. Ammonium persulfate (APS), 2-acrylamido -2-methyl-1-propanesulfonic acid (AMPS; MW: 207.25 g/moL), and ethylene glycol dimethacrylate (EGDMA; MW: 198.22 g/moL) was obtained from Sigma-Aldrich St. Louis, MO, USA. Hydroxypropyl beta cyclodextrin (HP-βCD; MW:1541.5 g/moL), DPPH (2,2- diphenyl-1-picryhydrazyl), ABTS (2,2’-azino-bis (3-ethylbenzothiazoline-6-sulfonic acid) and Cefepime HCL were obtained from Meilune biological company (Dalian,China). Bacterial strains such as Staphylococcus aureus (S. aureus: ATCC25923HBJZ005), Pseudomonas aeruginosa (P. aeruginosa: ATCC27853HBJZ017), and Escherichia coli (E. coli: ATCC25922HBJZ087) were obtained from Qingdao Hope Biotechnology, Co, Ltd., Qingdao, China. ## 2.2. Preparation of Rutin Inclusion Complexes with HP-βCD (RIC) The inclusion complexes were prepared by following the method of Vijaya Sri et al. with slight modification [37]. HP-βCD was dissolved in deionized water, while rutin was dissolved in methanol and protected from light to prevent its degradation. The molar ratio used to prepare inclusion complexes was 1:1 for HP-βCD and rutin. The samples were mixed and placed on a magnetic stirrer for 72 h at a speed of 350 rpm/min. After thoroughly mixing, the supernatant was removed by centrifugation and placed in a refrigerator at −20 °C overnight. Finally, rutin inclusion complexes (RIC) were obtained after freeze-drying the samples for 2 days. The entrapment efficiency (EE%) of rutin in RIC was $72.30\%$, drug loading (DL%) was $21.99\%$, and the yield was $89.07\%$. ## 2.3. Synthesis of Xanthan Gum-Based (HPMC-g-AMPS) Hydrogels Different batches of hydrogels were synthesized using the free radical polymerization method with slight modification by grafting monomers onto polymer networks [38]. HPMC, xanthan gum, APS/SHS, AMPS, and EGDMA were carefully weighed and placed in labelled glass bottles. The appropriate amount of water was added to all the labelled vials. Xanthan gum was dissolved without lumps or precipitations by stirring continuously at 40 °C, while HPMC was dissolved without any precipitation at room temperature. We used APS and SHS as initiators and co-initiators for the reaction, respectively. The SHS was dissolved in distilled water, and the APS was added slowly to prepare the initiator mixture. The clear aqueous solution of AMPS was also prepared by stirring continuously at room temperature. While stirring continuously, a clear solution was obtained by incorporating the initiator/co-initiator solution drop-by-drop into the monomer solution. HPMC solution was stirred continuously while the AMPS and initiator/co-initiator mixture was poured drop-by-drop into it. This solution was slowly added to the xanthan gum solution and well blended. The EGDMA was poured slowly into the mixture and stirred well to ensure even mixing. Finally, a sufficient amount of water was added to the reaction mixture and thoroughly mixed. After this, the mixture was put in an ultrasonic bath while nitrogen bubbles were used to eliminate any remaining air. The clarified solution was then covered by aluminium foil. Samples were then placed in a preheated water bath for 1 h at 50 °C, followed by an overnight increase to 65 °C. After 24 h, clear, transparent hydrogels were formed. Once the hydrogel had been formed in the water bath, it was cooled in the glass molds to room temperature. It was then removed from the molds and cut into 8 mm diameter discs. The discs were transferred to individually labeled Petri dishes after washing with ethanol and water (50:50). One week after being stored at 40 °C, the weight of the hydrogels became constant. Table 1 shows a series of xanthan gum-based (HPMC-g-AMPS) hydrogel compositions with varying amounts of polymer, monomer, and crosslinker. Figure 1 shows a schematic representation of xanthan gum-based (HPMC-g-AMPS) hydrogels [33,39,40,41]. ## RIC Loading in Xanthan Gum-Based (HPMC-g-AMPS) Hydrogels Rutin was used as a model drug, and its inclusion complexes were formed with HP-βCD and then loaded into hydrogels by the swelling-diffusion technique. Briefly, RIC was dissolved in 0.2 M phosphate buffer of pH 7.4. Then, dried and preweighed hydrogel discs were added to RIC solution for 48–72 h. The hydrogel discs were removed and dried to a constant weight. Drug loading in hydrogels was determined using the following formula. Moreover, drug loading was also verified by extracting all the drugs from the hydrogels. [ 1]Drug loading=Drug loaded hydrogel−Unloaded hydrogel ## 2.4.1. 1H NMR and Fourier Transform Infrared Spectroscopy (FTIR) 1H NMR spectra were measured utilizing TMS as an internal standard and D2O as a solvent on a Bruker AV 500 MHz (Bruker BioSpin, Zurich, Switzerland). TOPSPIN software was used to process the spectra. Attenuated total reflectance (ATR) spectroscopy was employed to analyze drug-formulation interactions using a Spectrum Two FTIR spectrometer (Perkin Elmer, Buckinghamshire, UK). Spectra of RIC-loaded and unloaded hydrogel samples and purified components were acquired between the scanning ranges of 400 and 4000 cm−1. ## 2.4.2. Thermal Analysis (TGA and DSC) The thermal stability of the sample was examined by the Exstar TG/DTA6300TG thermogravimetric analyzer (SII Nano, Tokyo, Japan) and differential scanning calorimetry (Perkin Elmer, Buckinghamshire, UK) [42]. A thermogravimetric analyzer was used to measure temperature-dependent weight change. Initially, reference standards were used to calibrate the weight profile. AMPS, XG, HPMC, RIC, rutin, and HP-βCD were placed in aluminium pans (0.5 to 5 mg each). The inert nitrogen flow rate of 10 mL/min was employed to measure weight loss with a 10 °C/min scanner. The melting points of AMPS, XG, HPMC, RIC, rutin, HP-βCD, and the prepared hydrogels were analyzed by differential scanning calorimetry (DSC). A sapphire standard was used to calibrate calorimeters for their heat capacity. A standard of indium was used to determine the cell constant and temperature. ## 2.4.3. X-ray Diffraction (XRD) X-ray diffraction (TD-3500 X-ray diffractometer, China) was used to measure crystallinity by irradiating the target (CuKα) at 30 kV and 20 mA [43]. The scans were performed at a speed of 2 degrees per minute at a slope of 2θ between 10° and 60°. Software such as Jade/MDI was utilized to analyze the data. Crystallinity can be determined by examining the peaks of the substances examined using XRD. The crystallinity of materials can be determined by sharp peaks, while amorphous properties can be determined by diffuse peaks. Measurements were performed on AMPS, HPMC, XG, RIC, rutin, HP-CD, and RIC-loaded and unloaded preparations. ## 2.4.4. Morphological Analysis The surface morphology of fabricated hydrogels was investigated by scanning electron microscopy (Quanta 250, FEI company, Eindhoven, The Netherlands). Dry hydrogels were cut to the appropriate size and adhered to aluminium tubing with double-sided tape [44]. Gold was sputtered on the stubs in a high-vacuum evaporator under an argon atmosphere. Photomicrographs of the coated samples under 15 kV accelerated current were obtained to determine the surface morphology. ## 2.4.5. Mechanical Properties Analysis Tensile strength (TS) and elongation at break (EAB) were measured using a TA.XT plus texture analyzer (Stable Micro Systems, Godalming, UK) equipped with a stainless steel spherical probe with a diameter of 5 mm and a testing speed of 1.0 mm/s. TS and EAB are determined by measuring the force and displacement exerted by the probe upon breaking the hydrogel (swollen form) [45]. [ 2]TS=FmTh [3]EAB=D2+R2R−1 In this equation, Th is the hydrogel thickness, and Fm is the maximal force exerted by the probe. Distance D between the probe first making contact with the hydrogel and the point at which it breaks the hydrogel is the radius of the orifice plate (R). ## 2.4.6. Sol–Gel Study The Soxhlet apparatus was used to examine the Sol–gel fraction of the formulated hydrogel by extracting it in deionized boiling water for 13 h [33]. Hydrogel slabs measuring 4 mm in thickness were tested in a Soxhlet apparatus. The slabs were removed from the apparatus after 13 h of extraction and dried at 40 °C until they reached a constant weight. The following equations were used to measure the sol and gel fractions. [ 4]Sol fraction %=E1 – E2E2 ×100 [5]Gel fraction=100−Sol fraction whereas E1 represents the weight of the hydrogel at the beginning (initial weight), and E2 represents the final weight (extracted). ## 2.4.7. Porosity Study The porosity of each hydrogel formulation was analyzed and evaluated using a solvent replacement approach. Hydrogel discs (Q1) of a known amount of weight were submerged in absolute ethanol (purity > $99.9\%$) for 5 days. After 5 days, the hydrogel discs were collected, wiped with filter paper to remove any remaining solvent, and then weighed again (Q2). The diameter and thickness of the discs were measured. Porosity was calculated using the provided equation [46]. [ 6]Porosity percentage %=Q2 – Q1 ρv ×100 whereas ρ is the ethanol density, and v represents the hydrogel volume in swelled form. ## 2.4.8. Biodegradation Study Xanthan gum-based (HPMC-g-AMPS) hydrogels were tested for biodegradation at a pH of 7.4 and a temperature of 37 ± 0.5 °C. Dry hydrogel discs were weighed and soaked in a pH 7.4 buffer solution for various durations (1, 2, 3, 4, 5, 6, and 7 days). Hydrogel discs were removed at the appropriate time, dried in a vacuum oven set to 40 °C, weighed again, and then placed in a buffer solution at a pH of 7.4. This procedure was repeated until further hydrogel disc degradation was detected [47]. The following formula can be used to calculate hydrogel degradation:[7]D=p1−p2p1 whereas D represents the degradation process; P1 represents the initial, dry sample weight; and P2 represents the final, immersed sample weight at time (t). ## 2.5. Characteristics of the Synthesized Hydrogel Polymer Network The most important indicators of the structure and properties of a hydrogel are the swelling-state polymer volume fraction (V2,s), crosslinking molecular weight (Mc), solvent interaction parameters (χ), and the number of linkages between pairs of crosslinks (N) [38,48,49]. ## 2.5.1. Diffusion Coefficient The degree of diffusion of a substance is determined by the nature of the polymer and its segmental mobility. The diffusion coefficient was estimated using the following formula:[8] D=πh.θ4.qeq2 whereas qeq represents hydrogel swelling, ɵ is the slope of swelling curves, and h represents the hydrogel disc thickness prior to swelling. ## 2.5.2. Polymer Volume Fraction (V2,s) V2,s represents the fraction of polymer in a fully swollen state. Volume swelling (Veq) data were collected for two pH values (1.2 and 7.4) to estimate polymer volume fraction. Polymer volume fraction was determined using the following formula:[9]V2,$s = 1$Veq ## 2.5.3. Average Molecular Weight between Crosslinks (Mc) Mc can be used to estimate the degree of crosslinking of polymer networks. The following equation can be used to determine it. [ 10]Mc =dpVsV 132,s−V2,s2ln1−V2,s+V2,s+χV22,s whereas dp refers to the polymer density. Vs reflects the solvent’s molar volume and χ provides information on the interaction of the polymer with the solvent according to Flory–Huggins theory. ## 2.5.4. Solvent Interaction Parameters (χ) It can be determined by the following equation. [ 11]χ=ln1−V2,s+V2,sV22,s whereas V2,s represents swollen gel volume fraction. ## 2.5.5. Crosslinking Units (N) The data of Mc was utilized to determine N. It was calculated using the following equation. [ 12]$$n = 2$$Mc Mr Mr refers to the repeating unit’s molar mass. The following equation can be used for its calculation:[13]Mr=mHPMCMHPMC + mXGMXG + mAMPSMAMPS+mEGDMAMEGDMAmHPMC+mXG+mAMPS+mEGDMA whereas m is the mass and M represents the molar mass of HPMC, XG, AMPS, and EGDMA, respectively. ## 2.6. Hydrogels Swelling Study The swelling behavior of fabricated hydrogels was studied at different pH values [50]. Hydrogel disc weights were initially recorded in dry form, and then the discs were placed in simulated gastric fluid (pH 1.2) and intestinal fluid (pH 7.4) at 37 °C. Disc weights were recorded periodically at defined time intervals until constant weights were obtained. The following formula was used for calculating the swelling ratio. [ 14] ESR=Ws−WdWd×100 whereas Ws represents the mass of a swollen hydrogel at a set time interval, and Wd represents the weight of a dried hydrogel. ## 2.7. In Vitro Release Study and Kinetics Data Modeling The USP dissolution apparatus II was used for in vitro rutin release testing to determine drug release patterns based on pH [51]. Dissolution mediums consisted of simulated gastric and intestinal fluid with pH 1.2 and 7.4, respectively. Drug-loaded hydrogel discs were weighed (approximately 1 g) before being inserted into both mediums (900 mL). In each dissolution basket, the medium volume was maintained at 900 mL. The experiment was carried out at a temperature of 37 °C. The paddle speed was adjusted to 50 revolutions per minute. Samples were obtained from the buckets at predefined intervals, diluted with fresh buffer solution, and examined with a UV–Vis spectrophotometer (T6 New Century; Beijing GM) at a wavelength of 359 nm (the calibration curve of rutin was prepared in the abovementioned buffer and used for the determination of the drug release from the hydrogels). [ 15]Drug release%=Amount of released drugAmount of loaded drug ×100 Drug release from hydrogels can be influenced by various factors, including the relaxation of polymer chains, the hydrogel’s swellability, the drug’s nature, and the release medium’s pH. The swelling of hydrogels is primarily the result of solvent diffusion required for this process during controlled release. Several models were used to determine the drug release pattern, including zero order, first order, Higuchi, and Korsmeyer–Peppas models. The given equations were used. [ 16] Zero−order kinetics Ft=K0t whereas the apparent rate constant is denoted by K0, and the amount of drug released at time t is denoted by Ft. [ 17] First order kinetics ln1−F=−K1t whereas F denotes the quantity of drug released during time t and k1 denotes the first-order release rate constant. [ 18] Higuchi model F=K2t12 whereas F is the quantity of drug released at time t, while K2 is the Higuchi constant. The model is defined by the following hypotheses: (I) the solubility of the drug is lower than its initial concentration in the framework, and (II) there is one-directional diffusion of the drug. [ 19] Korsmeyer−Peppas model MtM¥=K3tn whereas Mt represents the water mass obtained at time t, while M¥ represents the water mass that was obtained at equilibrium. K3 is a constant that considers the gels’ geometrical and structural features, and n is the release exponent. When the value of n is 0.45, it indicates a Fickian release mechanism; however, if the value of n is greater than 0.45 but lower than 1, it demonstrates a non-Fickian release mechanism. ## 2.8.1. DPPH Antioxidant Activity DPPH radical scavenging analysis was used to evaluate the antioxidant properties of the synthesized hydrogels. Predetermined quantities of samples were immersed in methanol for 24 h at room temperature in the dark. The sample solution was then combined with 1 mL of DPPH methanol solution (0.1 mM). The mixture was then thoroughly agitated and incubated for 30 min in a dark environment. The DPPH scavenging activity was then estimated by measuring the absorbance of the solution with a UV–Vis spectrophotometer at 517 nm. It was calculated using the following equation [52]. [ 20]DPPH%=A0−AA0×100 whereas A0 and A represent the absorbance of the reference and test samples, respectively. ## 2.8.2. ABTS Antioxidant Activity The ability of RIC-loaded hydrogels to scavenge free radicals was measured using the ABTS assay. The mixture of 7.4 mM ABTS and 2.4 mM potassium persulfate was combined in a 1:1 ratio and left to sit at room temperature for an entire night in order to induce radicalization of the ABTS molecules. Each hydrogel was incubated for 30 min at 37 °C with ABTS solution. The absorbance of the sample was measured at a wavelength of 730 nm. ABTS’s radical-scavenging efficiency was determined using the following formula [53]. [ 21]ABTS scavenging effect %=A0−A1A0×100 whereas A0 stands for ABTS absorbance and A1 for sample absorbance. ## 2.9. Antibacterial Study Hydrogels were tested for their antibacterial properties using a disk-diffusion method with Gram-negative and Gram-positive organisms in a nutrient agar medium. The agar medium was sterilized at 121 °C after it was prepared. A sterile broth was used to grow the bacterial strain. Staphylococcus aureus, Pseudomonas aeruginosa, and Escherichia coli were cultivated on agar medium under aseptic conditions and then transferred into Petri dishes for solidification. We divided the plates into four sections: a blank hydrogel, RIC-loaded hydrogels, a positive control (Cefepime at 1 mg/mL), and a negative control. These plates were incubated for 24 h in an incubator. The zone of inhibition was determined for each sample in order to compare the results [54]. [ 22]Percentage inhibition=Zone of inhibition of test sample mmZone of inhibition of standard drug mm×100 ## 2.10. Statistical Analysis All the data were expressed as mean ± SD. The statistical differentiation between pairs of data was determined using a two-way ANOVA and Tukey’s post hoc test. To determine the significance of the difference between swelling and drug release profile, p-values were determined and were denoted as * $p \leq 0.05$, $p \leq 0.01$, and * $p \leq 0.001.$ ## 3.1. 1H NMR and FTIR Analysis The 1H NMR technique provides direct confirmation of the inclusion of the guest into the HP-βCD cavity [55]. The inclusion of rutin in HP-βCD cavities is reflected by changes in protons’ electronic and chemical environments, resulting from the complexation process and reflected in changes in chemical shift (∆δ) measurements. This chemical shift provides evidence as to which part of the guest molecule is inserted into the CD cavity. The 1H NMR spectra of HP-βCD, rutin, and their inclusion complex are shown in Figure 2A. The 1H chemical shifts of free HP-βCD and rutin were consistent with previous studies and are shown in Table 2 [6]. Consequently, the inclusion complexation of rutin with HP-βCD has a negligible effect on the values of its H-2 protons (0.002). However, H-5 (narrow side) and H-6 protons exhibit significant chemical shift changes, 0.015 and −0.021 ppm respectively. Moreover, rutin addition shielded H-4 and H-6 on the inner surface of HP-βCD while leaving H-1, H-3, and H-5 unshielded when added to the HP-βCD. The positive sign of the Δδ ppm indicates a downfield displacement, whereas a negative sign indicates an upfield displacement (Δδ = δcomplex − δfree) [56]. These results indicate that rutin forms inclusion complexes with HP-βCD. FTIR spectra of HPMC, xanthan gum, AMPS, rutin, EGDMA, RIC, HP-βCD, unloaded hydrogel, and the drug-loaded hydrogel are shown in Figure 2B. HPMC exhibited a distinct broadband region at 3463 cm−1 corresponding to O-H stretching vibrations. Bands at 2890 and 1060 cm−1 were assigned to C-H and C-O bonds [57]. Additionally, HPMC showed an important stretching vibration at 1459,1119 cm−1 assigned to CH3 and C-O-C bonds [58]. Xanthan gum exhibits stretching vibrations at 3270 cm−1 due to axial deformation of the O-H. At 2879 cm−1, the peak results from the stretching vibrations of the C-H group, while at 1705 cm−1, the peak results from stretching vibrations of the C-O group, while the bands near 1601 cm−1 are due to the axial deformation of the C-O portion of the enol [59]. FTIR spectra of AMPS revealed a peak at 1461 cm−1 corresponding to the binding vibration of CH2. AMPS spectrum shows vibrational peaks at 1360 cm−1 (-C-O stretching), 2982 cm−1 (-CH stretching of -CH2), and 1230 cm−1 (symmetrical S=O stretching), which are all characteristic of AMPS. EGDMA spectrum revealed peaks at 1713 cm−1 associated with C=O stretching vibrations, whereas the peaks at 1633, 1291, and 1153 cm−1 are attributed to C=C and C-O stretching vibrations in the symmetric and asymmetric esters, respectively [60]. Absorption bands were seen for rutin at 3332 cm−1 (OH stretching), 2974 cm−1, and 997 cm−1 (C-H vibration), and 1300 to 1000 cm−1 (rutin C-O stretching vibration) [61]. The peaks at 1645 cm−1 belong to the C=O stretching vibration, and at 1596 cm−1 to the C=C stretching vibration of the aromatic structure of rutin [62]. HP-βCD showed peaks at 3410 cm−1 due to stretching vibrations of OH and at 2928 cm−1 due to C-H vibrations. The characteristic bands at 1160 cm−1 and 1030 cm−1 were attributed to C-O stretching vibrations. The results are consistent with those obtained by other researchers [63]. In the RIC FTIR spectrum, the broad band at 3379 cm−1 indicates the existence of a –OH group in the structure, whereas the peak at 1131 cm−1 corresponds to the C-O stretching vibration in HP-βCD. Rutin’s C=O structure is represented by the peak at 1645 cm−1, and the presence of multiple peak bands indicates that rutin and HP-βCD mixed thoroughly to form the inclusion complex. The FTIR spectrum of blank hydrogel showed an absorption band at 3289 cm−1 (OH stretching), and C-O bonding around 1600 cm−1. The peak data indicate that these substances are crosslinked successfully. FTIR spectra of RIC-loaded hydrogels displayed a different spectrum than those of their parent components. The bands appearing at 1645 cm−1 were considered as the peaks of the C=O group. The peak at 1036 cm−1 belongs to C-O stretching vibration. The emergence of new peaks, and the presence of functional groups, suggests that the polymers used were successfully crosslinked. ## 3.2. TGA Analysis TGA was conducted to assess the thermal stability of the polymers, inclusion complexes, and synthesized hydrogels (Figure 3). According to the TGA analysis of AMPS, there was a $6\%$ reduction in weight at 208 °C, followed by a $20\%$ reduction between 210 and 250 °C, indicating water and moisture loss. During the decomposition of the sulfonic acid group, a weight loss of $20\%$ was detected within the temperature range of 250 °C to 340 °C [64]. HPMC polymers lose approximately $4\%$ of their water at temperatures below 100 °C; however, it is thermally stable to a temperature of more than 300 °C [65]. Xanthan gum loses weight by $12\%$ from 25 °C to 180 °C, possibly due to moisture absorption from the sample’s surface and the bulk, but the weight loss rate decreases after 296 °C [66]. In the case of the HP-βCD samples, the weight loss below 80 °C was attributed to water evaporation, whereas the weight loss between 305 °C and 380 °C could be attributed to HP-βCD degradation, which is similar to that reported by other researchers [63]. The weight loss observed in the rutin sample between 30 °C and 134 °C is attributed to the evaporation of water, and the peak of weight loss between 248 °C and 364 °C may be attributed to rutin weight loss. RIC exhibited a change in temperature from 50 °C to 80 °C due to water loss and a change from 280 °C to 348 °C as a result of RIC degradation. Initially, hydrogel samples lose weight from 50 °C to 260 °C due to water dissipation and polymer bonding breakdown. When the temperature is raised to 260 °C and 280 °C, $20\%$ of the weight is lost. Thus, it shows that the formulation has higher thermal stability than pure reactants. ## 3.3. Differential Scanning Calorimetry (DSC) The HPMC exhibits an endothermic peak of free water at 50–100 °C, which suggests that some water molecules are bonded to the HPMC. Several small peaks indicate a relatively disordered crystalline state [67]. A strong absorption peak can be observed for xanthan gum near 100 °C, consistent with Umme Hani et al. ’s experimental results [68]. The glass transition’s temperature was estimated to be close to 290 °C. AMPS indicated a sharp endothermic peak around 202 °C, which is attributed to the decomposition of sulphonic acid groups [69]. DSC data of the rutin sample showed an endothermic phase between 56 °C and 150.78 °C resulting from water loss. As in previous studies, mass loss from 175.22 °C to 191.88 °C was attributed to rutin melting [61]. Based on the DSC data for HP-βCD, the melting peak of HP-βCD is located around 344.12 °C, while the endothermic peak corresponding to water molecules occurs at 77.70 °C. This change may be triggered by hydrogen bonds between water’s H group and the OH group of HP-βCD [63]. Hydrogels have an exothermic peak located around 198.17 °C, showing the presence of AMPS in the formulation. The newly developed structure has resulted in a thermally stable formulation (Figure 4). ## 3.4. XRD Analysis Figure 5 shows the XRD spectra of all the polymers and fabricated hydrogels. HPMC shows a diffraction peak at 2θ = 20.74° [70]. The amorphous nature of xanthan gum is evidenced by the absence of clear, sharp peaks and the presence of a broad, amorphous peak in the vicinity of 2 theta of 20°. Other researchers have reported similar results regarding xanthan gum’s amorphous nature [71]. HP-βCD displays two peaks at 2θ = 10.35° and 18.97°, which indicate that HP-βCD has amorphous characteristics. Other researchers have also obtained similar results. A sharp diffraction peak is observed for rutin at 2θ = 26.22°, which is typical for rutin with high crystalline properties. The diffractogram of the RIC sample reveals a broad peak at 2θ = 19.42°, while the unloaded hydrogel sample shows a broad peak at 2θ = 21.58°. However, the diffractogram of drug-loaded hydrogels demonstrates only one broad peak at 2θ = 22.92°, while no other intense drug peaks were displayed in their respective regions. The cause of this may be the physical interaction of the drug with the polymeric blend, which interfered with its purity and consequently diminished its crystal lattice properties. ## 3.5. SEM Analysis SEM images of HP-βCD show a porous, spherical cavity-filled ball-like structure [72]. However, upon complex synthesis with rutin, the porous and spherical morphology of HP-βCD was replaced by a regular folded structure [73]. These results indicate that after inclusion, a different solid phase with a different morphology was observed, which was consistent with the results obtained from the XRD analysis. Hydrogel microphotographs show highly dense, wavy, irregular surfaces (Figure 6). The polymeric gel network may collapse fractionally during the drying procedure, resulting in a rough surface. Furthermore, hydrogels were found to have a high density of crosslinked polymeric networks, which had pores and channels that were effective for entrapping drugs [74]. Porous structures cause swelling and the release of drugs due to the media being received at their surfaces. Micropores gradually absorb fluid as macropores fill up, followed by macropores filling up. It has been demonstrated that a smooth surface and a solid mass play a significant role in the stability of a polymeric network, as shown by the hydrogel system. ## 3.6. Mechanical Properties Analysis Hydrogels should be evaluated for their mechanical properties, including their tensile strength (TS) and elongation at break (EAB), before being used for drug delivery. Tensile strength was increased by the increase in EGDMA content (Table 3) [75]. In addition, the tensile strength of the composite increases gradually as the HPMC content increases. The mechanical strength of the gel will decrease when the AMPS content increases, likely due to increased electrostatic repulsion and osmotic pressure. Xanthan gum is also a biopolymer that increases the tensile stress and cohesion of bonds, resulting in stronger bonds and an increase in tensile strength [76]. The hydrogels showed better mechanical properties than the previously reported hydrogels [48,77]. ## 3.7. Sol–Gel Analysis The sol–gel fraction was assessed for all hydrogel compositions. Hydrogels are divided into two fractions: the sol fraction, which is composed of the uncrosslinked portion, and the gel fraction, which is composed of the crosslinked portion [32]. The sol fraction refers to the small amount of uncrosslinked hydrogel that remains after the polymerization reaction since there are no reactive sites. Each hydrogel formulation was subjected to sol–gel analysis to determine crosslinking and uncrosslinked percentages. This technique primarily quantifies the number of uncrosslinked polymers [78]. The hydrophilic nature of AMPS causes more chemical reactions to occur as the concentration of AMPS increases, resulting in a thicker gel. HPMC is a type of polymer that increases gel fraction with increased content. EGDMA acts as a crosslinking agent that induces gel formation due to the crosslinking process [79]. The gel fraction will increase correspondingly when EGDMA content increases [80]. ## 3.8. Porosity Study Porosity plays an instrumental role in hydrogel swelling, drug loading, and drug release. As pore sizes increase, swelling increases, and as a result, drug loading and release increase. A higher concentration of HPMC increases the porosity of fabricated hydrogels. As the reaction mixture becomes more viscous, bubbles are prevented from escaping, resulting in increased porosity. These factors lead to the development of interconnected channels and increasing porosity. Porosity decreases due to tight junctions and crosslinking of bulk densities as EGDMA concentration increases. The porosity of the gel can also be affected by xanthan gum. HXA-9 exhibits a smaller porosity than other groups due to an increased amount of xanthan gum [81]. Furthermore, HPMC also affects the porosity of the gel. Porosity increases with increasing amounts of HPMC, and similar results have been reported by other researchers. Due to the increased concentration of EGDMA, porosity decreased due to the formation of tight junctions and increased crosslinking. A higher concentration of AMPS will result in greater porosity since the sulfonate groups will generate more electrostatic forces. AMPS contains a hydrophobic alkyl group, which can reduce hydrogen bond interactions by forming hydrophobic microregions. This leads to an increase in pore size and network size in the hydrogel formulation (Figure 7), which is per other studies [33]. ## 3.9. Biodegradation Analysis Figure 8A–C illustrates the results of the biodegradation experiment performed to determine the degradation rate of the prepared hydrogel at different time periods. Degradation speed is affected by weight ratios, and with increasing EGDMA, hydrogel degradation speed has been found to be slow. This may be due to the generation of functional groups, which produce large quantities of free radicals. Consequently, these free radicals are capable of strengthening the polymerization reaction, resulting in a slower degradation rate as they contribute to the strength of the crosslinked compound’s hydrogel. Xanthan gum decreases gel degradation as the polymer content increases, which is consistent with the results of other researchers [84]. ## 3.10. Structural Parameters of Hydrogels In this study, various structural parameters of prepared hydrogels were examined, including the average molecular weight of crosslinks Mc (degree of polymer crosslinking), polymer volume fraction V2,s (amount of solvent taken up and held within the network), intersolvent interaction parameter χ, a repeating unit between crosslinks N, and diffusion coefficient D [80]. Table 4 lists the different structural parameters of fabricated hydrogels. The hydrogel’s maximum absorption and holding capacity depend on these characteristics; hence, calculating them is crucial. V2, S, and χ rose with the increased concentration of EGDMA, indicating the development of tighter and stiffer gel formations. The values of Mc decreased, and N increased as EGDMA concentration increased because higher amounts of EGDMA are responsible for forming stronger crosslinks. ## 3.11. Swelling Behavior Various concentrations of polymer (HPMC, xanthan gum, AMPS, and EGDMA) were used to prepare hydrogels to investigate the effects on swelling ratio in different media. Whenever hydrogel composites are soaked in different media, their bonds break off due to their hydrophilic nature and crosslinking of all polymers. Figure 9 shows the swelling rate of the hydrogel at various pH values over time. The results showed that the hydrogel swelling was higher at pH = 1.2 ($28\%$ swelling ratio) compared to pH = 7.4 ($22\%$ swelling ratio). This increased swelling is due to the ionization of hydroxyl (-OH) functional groups in the hydrogel matrix [54]. Hydrogel swelling behavior is determined by functional groups that may be ionized or protonated, interactions between hydrophilic and hydrophobic groups, and chain relaxation. The developed hydrogels exhibited high swelling kinetics at pH 1.2 and low swelling at pH 7.4. The behavior may result from the protonation of functional groups within AMPS [85]. The swelling degree of hydrogels increased with the increase in AMPS concentration, possibly as a consequence of AMPS containing a large number of—CONH2 and—SO3OH groups, which combine with water molecules after ionization, increasing the swelling of hydrogels, as was previously observed by other researchers [48]. The hydrophilicity of XG and the presence of o-acetyl and pyruvyl residues in XG may contribute to its ability to increase the swelling degree of the hydrogel. In addition, swelling will also increase with an increase in the ratio and concentration of XG. Other studies have also reported similar findings [85]. EGDMA acts as a crosslinking agent, so as the concentration of EGDMA increases, the crosslinking density of hydrogels will increase, which will reduce the porosity of the hydrogel and reduce the amount of water penetrating the mesh, leading to reduced swelling and vice versa [86]. ## 3.12. Drug Release Behaviour and Kinetics Modelling Drug release in the buffer of pH 1.2 ranged from $40.12\%$ to $75.17\%$. The drug release rate from HXA-6 was the highest at pH = 1.2 ($75.17\%$), whereas the drug release rate from HXA-3 was the lowest at pH = 1.2 ($40.12\%$). Drug release rates in the buffer with pH = 7.4 were $39.77\%$~$70.25\%$. The highest drug release was observed with HXA-7 ($70.25\%$), and the lowest was HXA-3 ($33.77\%$). The release curve for RIC indicated that its release differed with pH buffers, with the maximum release occurring in pH buffers of 1.2. The maximum drug release rate was $75.17\%$ after 48 h. Hydrogel discs are immersed in buffer/water, and as a result of the osmotic pressure gradient, water molecules diffuse into the polymer network. Diffusion of water causes the hydrogel discs to swell, which causes channels to open, causing the drug to be released (Figure 10). A regression coefficient value near 1 was used to determine which model best fits the release data. The regression coefficients (r) for samples (HXA-2, HXA-7, HXA-9) containing varying concentrations of xanthan gum, samples with varying concentrations of the crosslinker EGDMA (HXA-1, HXA-2, HXA-3) and samples containing varying concentrations of AMPS (HXA-2, HXA-4, HXA-6) followed the Korsmeyer–Peppas model and indicated that the drug release mechanism was diffusion-based. The release exponent (n) values of all drug-loaded samples (HXA-1, HXA-2, HXA-3, HXA-4, HXA-6, HXA-7, HXA-9) following the Korsmeyer–Peppas model were less than 0.5, which indicated Fickian diffusion (Table 5) [87]. ## 3.13. Antioxidation Analysis DPPH and ABTS scavenging abilities of hydrogels were evaluated by measuring the scavenging efficiency, as depicted in Figure 11A,B. Four formulations (HXA-1, HXA-6, HXA-7, and HXA-9) exhibited superior antioxidant activity than other formulations. These formulations all showed higher swelling and release levels and higher feeding concentrations of xanthan gum. Rutin has excellent antioxidant properties, as well as the ability to inhibit cancerous growth, mutations, and the growth of bacteria [88]. The antioxidant properties of rutin have also been demonstrated in vitro and in vivo. Rutin’s antioxidant properties were demonstrated in a previous study by its ability to scavenge ROS and reduce oxidative stress [89]. Rutin shows significant antioxidant properties when loaded into a hydrogel. Xanthan gum (XG) is produced by the anaerobic fermentation of a species of bacteria named Xanomonas brassica. There are several unique properties of XG, including its non-toxic nature, biodegradability, intrinsic ability to function as an immunity agent, antioxidant property, and stability. It has drawn much attention as a good scavenger of reactive oxygen species due to its mixture of hydroxyl, reducible sugar, pyruvate, and o-acetylation components. The XG formulation can effectively reduce intracellular ROS levels and alleviate the effects of oxidative stress [90]. ## 3.14. Antibacterial Study This study examined the antibacterial activity of the formulation against Gram-positive and Gram-negative bacteria, and their inhibition zones are illustrated in Figure 12. The results demonstrated that no zones were observed in the negative control and blank hydrogel groups, while clear zones were observed in the positive control, i.e., 27, 29, and 23 mm, and in the RIC-loaded hydrogels, i.e., 13, 15, and 8 mm against E. coli, S. aureus, and P. aeruginosa, respectively. According to the bacteriostatic formula, the inhibitory percentages of the RIC-loaded hydrogels for E. coli, S. aureus, and P. aeruginosa were $48.14\%$, $51.72\%$, and $34.78\%$, respectively. Broad-spectrum antibiotic cefepime kills both Gram-positive and Gram-negative bacteria [88]. Observations showed that cefepime had a smaller antibacterial activity against Gram-negative bacteria than Gram-positive bacteria. This is possible due to the structure of their cell walls. Gram-negative bacteria possess a thin cell wall composed of three layers: an inner membrane, a peptidoglycan cell wall, and an outer membrane. Gram-positive bacteria have a thick cell wall. However, they do not have an outer membrane as Gram-negative bacteria do. The outer membrane of Gram-negative bacteria serves as a protective barrier from the environment. Hence, Gram-positive bacteria demonstrate better antibacterial activity or a larger inhibition zone than Gram-negative bacteria. ## 4. Conclusions In this study, the rutin inclusion complexes with HP-βCD (RIC) were developed first in order to improve the aqueous solubility of the drug in aqueous media. The entrapment efficiency (EE%) of rutin in RIC was $72.30\%$, the drug loading (DL%) was $21.99\%$, and the yield was $89.07\%$, indicating successful inclusion complexation. Then, xanthan gum-based (HPMC-g-AMPS) controlled release hydrogel was fabricated using a free radical polymerization technique, incorporating natural (xanthan gum) and semi-synthetic polymers (HPMC) as well as grafting monomers (AMPS). FTIR, XRD, TGA, and DSC measurements confirmed the formation of HP-βCD-rutin inclusion complexes (RIC), the hydrogel network, and the successful loading of the drug (rutin), and SEM analysis showed that the hydrogels are porous. The hydrogels showed a slightly higher swelling at pH 1.2 ($28\%$ swelling) compared to $22\%$ swelling at pH 7.4 after 48 h. Moreover, a slightly higher drug release was observed in the synthesized hydrogels after 48 h at pH 1.2 ($70\%$) than at pH 7.4 ($65\%$), with Fickian diffusion becoming the dominant mechanism. Increased polymer ratios and monomer concentrations achieved longer drug release times and improved mechanical properties. Moreover, the hydrogels were found to be highly porous ($94\%$ porosity) and biodegradable ($9\%$ weight loss in 1 week). In addition, the developed hydrogels demonstrated significant antioxidant activity in the DPPH assay (inhibition of $65\%$) and the ABTS assay (inhibition of $62\%$). In addition, the hydrogels demonstrated excellent antibacterial properties against Gram-positive bacteria such as E. coli (zone of inhibition of 13 mm) and S. aureus (zone of inhibition of 15 mm), as well as Gram-negative bacteria P. aeruginosa (zone of inhibition of 8 mm). 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--- title: LncRNA MHRT Prevents Angiotensin II-Induced Myocardial Oxidative Stress and NLRP3 Inflammasome via Nrf2 Activation authors: - Pinyi Liu - Xiaoming Dong - Chao Dong - Guowen Hou - Wenyun Liu - Xin Jiang - Ying Xin journal: Antioxidants year: 2023 pmcid: PMC10044972 doi: 10.3390/antiox12030672 license: CC BY 4.0 --- # LncRNA MHRT Prevents Angiotensin II-Induced Myocardial Oxidative Stress and NLRP3 Inflammasome via Nrf2 Activation ## Abstract The development of angiotensin II (Ang II)-induced cardiomyopathies is reportedly mediated via oxidative stress and inflammation. Nuclear factor erythroid 2-related factor (Nrf2) is an important regulator of cellular antioxidant defense, and reactive oxygen species (ROS) can activate the NLRP3 inflammasome. MHRT is a newly discovered lncRNA exhibiting cardioprotective effects, demonstrated by inhibiting myocardial hypertrophy via Brg1 and myocardial apoptosis via Nrf2 upregulation. However, the underlying mechanism of MHRT remains unclear. We explored the potential protective effects of MHRT against Ang II-induced myocardial oxidative stress and NLRP3-mediated inflammation by targeting Nrf2. Chronic Ang II administration induced NLRP3 inflammasome activation (increased NLRP3, caspase-1 and interleukin-1β expression), oxidative stress (increased 3-nitrotyrosine and 4-hydroxy-2-nonenal), cardiac dysfunction and decreased MHRT and Nrf2 expression. Lentivirus-mediated MHRT overexpression inhibited Ang II (100 nM)-induced oxidative stress and NLRP3 inflammasome activation in AC16 human cardiomyocyte cells. Mechanistically, MHRT overexpression upregulated the expression and function of Nrf2, as determined by the increased transcription of downstream genes HO-1 and CAT, subsequently decreasing intracellular ROS accumulation and inhibiting the expression of thioredoxin-interacting protein (NLRP3 activator) and its direct binding to NLRP3. Accordingly, MHRT could protect against Ang II-induced myocardial injury by decreasing oxidative stress and NLRP3 inflammasome activation via Nrf2 activation. ## 1. Introduction Pathological activation of the renin-angiotensin system (RAS) is a key causative factor underlying several cardiovascular diseases. Angiotensin II (Ang II) is the main effector peptide of the RAS, produced in the circulatory system, as well as in the local cardiac tissue. Cardiac Ang II plays a key role in promoting the occurrence and development of various cardiac diseases, including diabetic cardiomyopathy, alcoholic cardiomyopathy and myocardial infarction [1,2,3]. Ang II interacts with its receptor, primarily AT1, induces the activation of nicotinamide adenine dinucleotide phosphate (NADPH) oxidase, and mediates the generation of reactive oxygen species (ROS) [4,5]. ROS generation that surpasses the scavenging ability of the body can lead to oxidative stress. Ang II-induced oxidative stress can rapidly activate the apoptosis signaling pathway, resulting in cardiomyocyte apoptosis or necrosis, ultimately leading to ventricular remodeling and heart failure [6,7,8]. Excessive ROS can also promote myocardial inflammation, apoptosis, hypertrophy and ventricular remodeling through epidermal growth factor receptor, mitogen-activated protein kinase and nuclear factor-kappa B (NF-κB) [9,10]. The NOD-like receptor protein 3 (NLRP3) inflammasome comprises the receptor protein NLRP3, apoptosis-related speck-like protein (ASC), and pro-caspase-1 [11]. Multiple molecular and cellular signaling events are reportedly involved in the activation of the NLRP3 inflammasome, including ionic flux, mitochondrial dysfunction, ROS production and lysosomal damage, among which the molecular mechanism underlying ROS is the most widely investigated [12,13,14]. Ang II-induced excessive ROS production can activate the NLRP3 inflammasome in human umbilical vein endothelial cells [15] and renal tubular epithelial cells [16]. Blocking the activation of the NLRP3 inflammasome can inhibit Ang II-induced myocardial inflammation, fibrosis and cardiac remodeling [17]. In addition, elevated ROS levels can induce the dissociation of thioredoxin-interacting protein (TXNIP) from the TXNIP/ thioredoxin (TRX) complex, as well as promote the binding of TXNIP with NLRP3 to participate in its activation during diabetes. A lack of TXNIP was found to inhibit the activation of the NLRP3 inflammasome and downstream secretion of interleukin (IL)-1β [18]. Therefore, ROS plays a central role in the activation of oxidative stress and NLRP3-mediated inflammation, which might be associated with the occurrence of Ang II-induced cardiomyopathy. Nuclear factor erythroid 2-related factor (Nrf2) is a redox-sensitive transcription factor that protects cells against oxidative stress and inflammation by upregulating the expression of approximately 200 cytoprotective genes [19]. Studies have shown that upregulation of Nrf2 can significantly reduce cardiomyocyte apoptosis and lipid peroxidation induced by hydrogen peroxide [20]. Nrf2 activation can prevent myocardial ischemia-reperfusion injury and diabetic cardiomyopathy [21]. Nrf2 gene-deficient mice were shown to be highly susceptible to Ang II-induced myocardial hypertrophy, and Nrf2 activation can prevent Ang II-induced oxidative stress in cardiomyocytes [22,23]. In addition, a recent report has shown that resveratrol can afford protection against myocardial injury induced by chronic intermittent hypoxia by targeting Nrf2 and blocking NLRP3 activation [24]. Signaling pathways, such as adenosine monophosphate-activated protein kinase, phosphatidylinositol 3 kinase (PI3K), activated protein kinase (JNK/SAPK) and extracellular signal-regulated kinase, are reportedly involved in regulating Nrf2 functions [25,26,27]. Nrf2 functions are also regulated by various long non-coding RNAs (lncRNAs). LncRNAs are non-coding RNA with a length greater than 200 nt [28], mainly transcribed from the antisense strand and spacer of protein-coding genes [29]. LncRNAs can achieve gene expression at multiple levels, such as chromatin remodeling, transcriptional regulation and post-transcriptional processing, and play a significant role in the occurrence and development of cardiovascular diseases [30,31]. Myosin heavy chain-associated RNA transcripts (MHRT) from the variable cleavage of the gene encoding myosin heavy chain 7 is a newly discovered lncRNA exhibiting a cardioprotective effect [32]. MHRT can reportedly prevent myocardial hypertrophy by antagonizing the function of Brg1 or by affecting myocardin acetylation [33,34]. Hong et al., have found that MHRT enhances Nrf2 gene transcription and blocks doxorubicin-induced cardiomyocyte apoptosis [35]. Based on the above studies, we aimed to explore whether MHRT can prevent Ang II-induced myocardial oxidative damage and NLRP3-mediated inflammation, as well as examined mechanisms targeting Nrf2. This study could provide a novel strategy for preventing Ang II-induced cardiac damage. ## 2.1. Animals Seven-week-old male C57/BL mice were purchased from Beijing Experimental Animal Technical Co., Ltd. (Beijing, China). Mice were housed at the Animal Center of Jilin University (Changchun, China). All animal procedures were approved by the Animal Care and Use Committee of the Chinese Academy of Medical Sciences (Beijing, China). After one week of acclimatization, all mice were randomly divided into control and Ang II groups ($$n = 27$$ per group). Mice in the Ang II group were subcutaneously administered Ang II (Sigma-Aldrich, St. Louis, MO, USA) at a dose of 0.5 mg/kg every other day for 2 M and observed until 6 M [1,36]. The control group was administered the same dose of saline. After 2 M, 4 M and 6 M of Ang II treatment, one-third of the mice in each group ($$n = 9$$) were sacrificed for heart tissue collection. ## 2.2. Measurements of Non-Invasive Blood Pressure (BP) and Cardiac Function BP was measured by tail-cuff manometry using a CODATM non-invasive BP monitoring system (Kent Scientific, Torrington, CT, USA) as previously described [3]. Briefly, mice were restrained in a plastic tube restrainer and warmed using heating pads during acclimation cycles. Occlusion and volume-pressure recording cuffs were placed over the mouse tail to measure BP in 15 measurement cycles. After 3 days of training, formal measurements were performed to collect BP data. Following sedation with 2,2-tribromoethyl alcohol (Avertin; Sigma-Aldrich, St. Louis, MO, USA), mice were placed in a supine position on a heating pad to maintain body temperature at 36–37 °C. Under these conditions, the animals’ heart rate ranged between 400 and 550 beats per minute, and cardiac function was measured using a high-resolution imaging system (Vevo 770, Visual Sonics, Toronto, ON, Canada) equipped with a high-frequency ultrasound probe (RMV-707B), as previously described [37]. Echo analysis included indices of LVID in diastole (d), LVPW thickness in diastole, systolic function by EF (%) and FS (%). ## 2.3. Cell Culture and Transfection of MHRT Lentivirus The human wild-type MHRT lentiviral vector (LV-MHRT, NR 126491) was designed by GeneChem (Shanghai, China) and used to infect AC16 cardiomyocytes in order to generate MHRT overexpression cell lines. The name of the vector is GV367. The sequence of components on the vector is Ubi-MCS-SV40-EGFP-IRES-puromycin and the cloning site is AgeI/NheI. The negative control cell lines were generated via infection with control lentivirus (LV-Vector, CON238) containing a random sequence as blank controls. Both of them were GFP gene recombinant vectors. AC16 cardiomyocytes were purchased from the Beina Chuanglian Institute of Biotechnology (Beijing, China). AC16 cells were maintained in Dulbecco’s modified Eagle medium (DMEM) supplemented with $10\%$ fetal bovine serum at 37 °C in $5\%$ CO2. Recombinant expression vectors of lncRNA MHRT (LV-MHRT, NR 126491) and an NC (LV-Vector) were provided by GeneChem (Shanghai, China). AC16 cells overexpressing MHRT were generated by stable transfection with lentiviral. Twenty-four hours prior to transfection, AC16 cells were inoculated in 6-well plates at a density of 1 × 105 cells/well and then transfected with MHRT overexpression virus (LV-MHRT) and overexpression control virus (LV-Vector) at a multiplicity of infection of 40. The culture medium was replaced 12 h later. Forty-eight hours post-transfection, puromycin at a final concentration of 2 µg/mL was added to the medium to select purely transfected cells. The cells were subsequently cultured for 2–3 generations for stable construction, and infection efficiency was assessed by fluorescence observation and quantitative reverse transcription PCR (qRT-PCR) analysis. AC16 cells overexpressing MHRT (LV-MHRT) and the NC (LV-Vector) were exposed to either Ang II (100 nM) or the control solution for 24 h in DMEM. ## 2.4. Western Blot Analysis Heart tissues and AC16 cardiomyocytes were homogenized in ice-cold 1× RIPA lysis buffer, supplemented with a protein inhibitor cocktail (Sigma-Aldrich, St. Louis, MO, USA) to obtain total protein. Total protein was separated using $10\%$ sodium dodecyl sulfate (SDS)-polyacrylamide gel electrophoresis (PAGE) and transferred to polyvinylidene difluoride membranes (Millipore, Billerica, MA, USA). Membranes were blocked with $5\%$ non-fat milk for 1 h and incubated overnight at 4 °C with the following antibodies: 3-NT (Millipore, Billerica, MA, USA), 4-HNE (Alpha Diagnostic International, SAN Antonio, TX, USA), NLRP3, caspase1, IL-1β, TXNIP (Affinity, Jiangsu, China) and β-actin (Santa Cruz, Dallas, TX, USA). After washing unbound antibodies, the membranes were incubated with horseradish peroxidase (HRP)-conjugated secondary antibody (Santa Cruz, Dallas, TX, USA) for 1 h at room temperature. Specific bands were visualized using an enhanced chemiluminescence detection kit (ECL) and Gel Documentation 2000 system (Bio-Rad, Hercules, CA, USA). Densitometric analysis of protein bands was analyzed using ImageJ software (National Institutes of Health, Bethesda, MD, USA). ## 2.5. RNA Isolation and Real-Time PCR Total RNA was extracted from heart tissues and AC16 cells using TRIzol reagent (Invitrogen, Grand Island, NY, USA). cDNA was synthesized from 1 µg of total RNA according to the manufacturer’s protocol for the SuperScript III First-Strand Synthesis System (Invitrogen, Grand Island, NY, USA). mRNA expression levels were determined using first-strand cDNA as a template by quantitative real-time PCR (qPCR) with Power SYBR Green PCR Master Mix (Applied Biosystems, Foster City, CA, USA). GAPDH was used as an endogenous control. All primers for human AC16 cells and mouse tissues are shown in Tables S1 and S2 in the Supplementary Materials. ## 2.6. ROS Measurement ROS was measured using dichloro-dihydro-fluorescein diacetate (DCFH-DA) using the Reactive Oxygen Species Assay Kit (YEASEN, Shanghai, China) according to the manufacturer’s instructions. AC16 cardiomyocytes (1 × 105) were incubated with 10 μM DCFH-DA for 30 min at 37 °C. Cells were collected and washed with phosphate-buffered saline (PBS) for flow cytometric analysis (BD Biosciences, Franklin Lakes, NJ, USA). Data were analyzed using the FlowJo V10 software. ## 2.7. FISH The FISH kit was purchased from Ribo Bio (Guangzhou, China), and the experiment was performed according to the manufacturer’s instructions. The specific probe for the lncRNA MHRT was synthesized by Ribo Bio (Guangzhou, China). U6 was used as the positive control for the nucleus, and 18S was used as the positive control for the cytoplasm. After fixation and permeabilization, cells were treated with a prehybridization buffer at 37 °C for 30 min, followed by overnight hybridization in the hybridization buffer containing 20 μM lncRNA MHRT probe at 37 °C. Subsequently, the coverslips were washed with wash buffer and PBS at 42 °C. DAPI was used for nuclear staining, and coverslips were washed and then visualized using a confocal microscope (Zeiss, Oberkochen, Germany). ## 2.8. Co-Immunoprecipitation AC16 cells were homogenized in immunoprecipitation (IP) lysis/wash buffer supplemented with phenylmethane sulfonyl fluoride (PMSF) and protease inhibitor cocktail for 30 min and then centrifuged at 13,000× g for 20 min at 4 °C. Lysates were immunoprecipitated with the TXNIP antibodies and protein A/G magnetic beads (Millipore, Billerica, MA, USA) at 4 °C for 6 h. Beads were washed five times with IP lysis/wash buffer. Then, immunocomplexes were eluted for 5 min and analyzed for NLRP3 expression using western blotting. ## 2.9. Statistical Analysis Data are presented as mean ± standard deviation (SD) ($$n = 9$$ per group). Comparisons were performed by two-way analysis of variance (ANOVA) for different groups, followed by Tukey’s test in pair-wise repetitive comparisons using Origin 7.5 software (Origin Lab Corporation, Northampton, MA, USA). Statistical significance was set at $p \leq 0.05.$ ## 3.1. Ang II Induces Cardiac Remodeling and Dysfunction in Mice Ang II administration every other day for 2 months (2 M) did not affect blood pressure until 6 months (6 M) (Figure 1A). The subpressor dose of Ang II induced cardiac remodeling and dysfunction at 4 months (4 M) and 6 M, reflected by the enhanced cardiac dilation index (left ventricular internal diameter (LVID)) and cardiac hypertrophy index (left ventricular posterior wall (LVPW)), reduced ejection fraction (EF) and fractional shortening (FS), as examined by echocardiography (Figure 1B). ## 3.2. Ang II Stimulation Induces Cardiac Oxidative Damage and Activation of NLRP3 Inflammasome in Mice A 2 M treatment with Ang II could induce persistent accumulation of 3-nitrotyrosine (3-NT; an index of nitrosative damage) and 4-hydroxy-2-nonenal (4-HNE; an index of lipid peroxidation) in the heart tissue of mice at 2 M, 4 M and 6 M (Figure 2A,B). Compared with the control group, the Ang II group presented significantly increased levels of protein expression and transcription of NLRP3, caspase-1 and IL-1β at 2 M, 4 M and 6 M (Figure 2C,D). These results indicated that the long-term effect of Ang II caused cardiac oxidative damage and activation of NLRP3 inflammasome. ## 3.3. Ang II Treatment Affects the Transcription of Cardiac Nrf2 and MHRT in Mice To determine whether Ang II-induced cardiac oxidative damage is associated with impaired Nrf2 and MHRT expression, we measured the transcription of Nrf2 and MHRT in the heart. Compared with the control group, cardiac transcription and protein expression of Nrf2 was significantly increased in the Ang II group at 2 M and decreased at 4 M and 6 M (Figure 3A). As the downstream antioxidant genes of Nrf2, the transcription of HO-1 and CAT was significantly increased in the Ang II group at 2 M, and decreased at 4 M and 6 M, consistent with the changes of Nrf2 expression (Figure 3B). The mRNA expression of MHRT was significantly decreased in the Ang II group at 2 M, 4 M and 6 M (Figure 3C). These results suggested that the transcriptional activity of Nrf2 and MHRT was impaired, accompanied by cardiac remodeling and dysfunction induced by Ang II stimulation at 4 M and 6 M. ## 3.4. Overexpression of MHRT Activates Nrf2 in AC16 Cells To determine the preventive role of MHRT in Ang II-induced cardiac damage, we first detected the location of MHRT in AC16 cells using fluorescent in situ hybridization (FISH) and showed that MHRT was distributed both in the nucleus and cytoplasm (Figure 4A). Ang II treatment could also reduce the mRNA expression of MHRT in AC16 cells (Figure 4B). Lentiviral transfection (LV-MHRT) was used to overexpress MHRT in AC16 cardiomyocytes, and MHRT was successfully transduced into AC16 cells, as reflected by the positive GFP and elevated MHRT mRNA expression in the LV-MHRT group (Figure 4C). In addition, compared with the LV-MHRT group, the expression of MHRT was slightly decreased in the Ang II/LV-MHRT group (Supplementary Figure S1). Overexpression of MHRT activated Nrf2 in AC16 cells, as demonstrated by the higher transcription level of Nrf2 and its downstream genes, CAT and HO-1, in the LV-MHRT group than those in the negative control (NC, LV-Vector) group (Figure 4D). Ang II treatment did not affect the expression of these genes in the NC group cells. However, compared with the Ang II group, cardiac expression of these genes was significantly increased in the Ang II/LV-MHRT group (Figure 4D). These findings implied that overexpression of MHRT significantly increased the activity of Nrf2 in AC16 cells with or without Ang II treatment. ## 3.5. Overexpression of MHRT Inhibits Ang II-Induced ROS Accumulation and Oxidative Damage in AC16 Cells Ang II exposure significantly induced ROS deposition in NC group cells; however, overexpression of MHRT decreased the accumulation in AC16 cells (Figure 5A). Compared with the NC group, the expression of oxidative damage indicators, namely 3-NT and 4-HNE, was significantly increased in the Ang II group, which was partially inhibited by MHRT overexpression (Figure 5B,C). To further explore whether antioxidant effect of MHRT depends on the inhibition of ROS production induced by Ang II, the AT1R expression and NOX enzymes activation was evaluated by detecting the protein expression of AT1R and p47phox. AT1R expression was significantly upregulated in the Ang II group at the protein levels and was not affected by MHRT overexpression (Figure 5D). Cytoplasmic subunit cp47phox is phosphorylated and translocated to the cell membrane (mp47phox) to form an active NOX complex. Ang II treatment significantly upregulated the expression ratio of mp47phox to cp47phox in AC16. However, there was no difference of the ratio between the Ang II and Ang II/LV-MHRT groups (Figure 5E). These findings suggested that MHRT plays its antioxidative role independent of Ang II-induced ROS production. ## 3.6. Overexpression of MHRT Inhibits Ang II-Induced Activation of NLRP3 Inflammasome and Its Binding to TXNIP The protein and mRNA expression levels of NLRP3, caspase-1 and IL-1β were significantly increased in the Ang II group, and overexpression of MHRT markedly inhibited the expression of these inflammatory factors (Figure 6A,B). Studies have reported that high levels of ROS can induce NLRP3 inflammasome via TXNIP activation, indicating that TXNIP acts as a link between ROS and NLRP3 inflammasome. Compared with the NC group, the transcription and protein expression of TXNIP were significantly increased in the Ang II group but not in the Ang II/LV-MHRT group (Figure 6C,D). In addition, Ang II treatment facilitated the combination of TXNIP and NLRP3 in AC16 cells, and overexpression of MHRT significantly inhibited their combination (Figure 6E), indicating that MHRT could inhibit the NLRP3 inflammasome by reducing the expression of TXNIP and its binding to NLRP3, possibly reducing by lowering ROS levels. ## 4. Discussion Previously, we have demonstrated that Ang II can induce cardiac oxidative damage, inflammation and subsequently cardiomyopathy associated with reduced Nrf2 function. Notably, a newly discovered lncRNA, MHRT, has shown a protective effect against myocardial hypertrophy and apoptosis. However, there is no evidence to demonstrate the direct role of MHRT in affording protection against Ang II-induced cardiac oxidative damage and inflammation and the underlying mechanism. The present study showed that: [1] Ang II-induced cardiomyopathy could be related to the downregulation of Nrf2 and MHRT, [2] overexpression of MHRT activated Nrf2 to inhibit Ang II-induced ROS accumulation and oxidative damage in AC16 cells, and [3] MHRT inhibited Ang II-induced activation of the NLRP3 inflammasome, probably by decreasing cellular ROS deposition to suppress the binding of TXNIP with NLRP3 in AC16 cells. Therefore, the present study provides direct evidence that MHRT has the potential to prevent Ang II-associated cardiomyopathy by preserving cardiac Nrf2. The key contributor to Ang II-induced cardiomyopathy is excessive ROS production, which not only causes oxidative stress but also triggers the NLRP3 inflammasome to induce myocardial injury [16]. The pathophysiological activity of Ang II involves the stimulation of NADPH oxidase to generate superoxide and hydrogen peroxide, along with the overproduction of mitochondrial ROS, leading to the feed-forward redox stimulation of NADPH oxidases [38,39]. In adult mammalian hearts, cardiomyocytes occupy approximately $75\%$ of the myocardial volume and around $30\%$ of the cells within the heart. The other $70\%$ of cells comprise endothelial cells, fibroblasts, smooth muscle cells and immune cells [40,41]. It has been reported that cardiomyocytes are the major sources of cardiac ROS production due to abundant mitochondria in their cytoplasm, where endogenous ROS are produced [40]. Vascular endothelial cells lining the coronary microvasculature can also produce ROS and interact with cardiomyocytes in several ways. Direct crosstalk may be mediated by diffusible ROS and NO. ROS produced by both cardiomyocytes and endothelial cells may influence extracellular matrix composition, which in turn, effects themselves. ROS-dependent alteration of paracrine factors such as Nox4 is also involved in the crosstalk [42]. Studies have shown that the Ang II-induced accumulation of ROS could activate NLRP3 inflammasome-mediated inflammation in human umbilical vein endothelial cells and renal tubular epithelial cells [15,16]. Additionally, inhibition of the NLRP3 inflammasome can attenuate pressure overload-induced myocardial remodeling in mice [43]. Ang II-induced cardiac inflammation, fibrosis and hypertrophy were shown to be prevented by blocking NLRP3 inflammasome activation in macrophages and cardiomyocytes [44,45]. It has also been reported that NLRP3 gene deletion in mice can significantly lower the risk of atherosclerosis and alleviate Ang II-induced cardiomyopathy by inhibiting mitochondrial dysfunction [46,47]. Our results revealed that a 2 M administration of a subpressor dose of Ang II without pressure overload could induce late cardiomyopathy at 4 M and 6 M, as demonstrated by a progressive increase in cardiac remodeling (elevated LVID and LVPW) and dysfunction (decreased EF and FS values), following a significant increase in cardiac NLRP3 inflammasome activation (indicators of NLRP3, caspase-1 and IL-1β) and oxidative damage (indicators of 3-NT and 4-HNE) (Figure 1 and Figure 2). Therefore, ROS plays a central role in the activation of oxidative stress and NLRP3-mediated inflammation, resulting in the occurrence of Ang II-induced cardiomyopathy. Cardiac tissue contains large numbers of resident macrophages, further increasing infiltration of macrophages and contributing to the inflammation in cardiomyopathy [48]. These resident macrophages are activated by the recognition of pathogen/damage-associated molecular patterns (PAMPs/DAMPs) via a number of pattern recognition receptors (PRRs) [49]. The activation of intracellular PRRs in cardiomyocytes leads to inflammasome activation, which converts pro-caspase-1 into the catalytically active protease that is responsible for the production of IL-1β and IL-18, subsequently triggering cardiac inflammation [50]. Therefore, macrophage infiltration also plays an important role in the induction of inflammation and inflammasome activation in Ang II-induced cardiac damage, even though it has not been further illustrated in this study. Nrf2 is a transcription factor that enhances the capacity of endogenous antioxidant defense and is located at the center of oxidative stress and the inflammatory response. The activation of Nrf2 has been shown to suppress oxidative stress-related cardiac hypertrophy and cardiomyopathy, including Ang II-induced cardiomyopathy [51,52]. Previous reports have revealed that cardiac overexpression of Nrf2 ameliorates Ang II-induced oxidative stress and cardiomyopathy and is exacerbated by the knockdown of Nrf2 [51,53,54]. However, it remains unclear whether the underlying mechanism of cardiac Nrf2 is impaired following long-term Ang II stimulation. Accumulating evidence suggests that Nrf2 and its antioxidant function can be regulated by lncRNAs [55,56]. It has been reported that lncRNA NEAT1 can upregulate Nrf2 by targeting miR-23a-3p, thereby inhibiting cardiomyocyte apoptosis [57]. LncRNA H19 inhibits myocardial ischemia and reperfusion injury by upregulating Nrf2 [58]. In the current study, we found that the expression and function of Nrf2 were decreased after Ang II treatment at 4 M and 6 M, accompanied by low expression of MHRT (Figure 3). MHRT is a newly discovered lncRNA exhibiting cardioprotective effects [32]. Han et al. have reported that MHRT can sequester Brg1 from its genomic DNA targets to prevent chromatin remodeling and inhibit the occurrence of cardiac hypertrophy [33]. In addition, Hong et al. have shown that overexpression of MHRT can promote the combination of H3 histone and the Nrf2 promoter to improve its transcription and expression, suppressing adriamycin-induced cardiomyocyte apoptosis [35]. To directly confirm the regulation of MHRT on myocardial Nrf2 and oxidative stress, AC16 cardiomyocytes were used to overexpress MHRT in this study and showed the activation of Nrf2 and reduction of Ang II-induced intracellular ROS accumulation and oxidative damage (Figure 4 and Figure 5), but no effect on the Ang II-induced ROS production (Figure 5D,E). Therefore, it can be demonstrated that MHRT can prevent Ang II-induced myocardial oxidative stress and injury partly through activation of Nrf2. However, we also observed the inconsistent expression between cardiac Nrf2 and MHRT in the Ang II group at 2 M (Figure 3). The increase of Nrf2 expression may be explained as an early compensatory reaction to overcome Ang II stimulation and the regulation of Nrf2 by MHRT is not dominate at this stage. While long-term oxidative stress stimulation impairs the function of Nrf2, including the involvement of MHRT to aggravate the cardiac oxidative damage, actually, Nrf2 and MHRT are reported to affect other types of cells in heart besides cardiomyocytes. For example, in neonatal rat cardiac fibroblasts, a time-dependent downregulation of protein expression of Nrf2 is observed after exposure to Ang II [59]; activation of Nrf2 can combat endothelial senescence [60]; overexpression of MHRT can promote collagen production in cardiac fibroblasts [61]. Therefore, the cardiac protective function of MHRT and Nrf2 in vivo probably comes from multiple regulatory pathways and target cells. Evidence indicates that ROS can activate the NLRP3 inflammasome. Therefore, in the present study, we found that overexpression of MHRT reduced ROS levels (Figure 5A) and inhibited NLRP3 inflammasome activation in AC16 cells (Figure 6A,B). NLRP3 can be activated by diverse molecules or cellular events, including mitochondrial dysfunction, ROS and lysosomal damage. Excessive ROS causes TRX to dissociate from TXNIP, and activated TXNIP combines with NLRP3 to promote inflammasome activation [18]. MHRT may inhibit combination of TXNIP and NLRP3 to restrain the activation of NLRP3 inflammasome via Nrf2-mediated inhibition of ROS accumulation. This finding illustrated the potential mechanism that MHRT inhibits Ang II-induced inflammation. However, the most unexpected finding of the present study was that MHRT could directly reduce TXNIP mRNA, protein levels and inhibit the combination of TXNIP and NLRP3 (Figure 6C–E), which may be attributed to the upregulated expression of TXNIP inhibitors by acting as a ceRNA via miRNAs or through the function of Nrf2 as a transcription factor. Further experiments are needed to elucidate the specific molecular mechanisms involved in this process. ## 5. Conclusions The findings of the present study indicate that chronic Ang II stimulation could activate cardiac oxidative damage and NLRP3 inflammasome-mediated inflammation, leading to cardiac hypertrophy and dysfunction, accompanied by MHRT inhibition. 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--- title: Long-Bone-Regeneration Process in a Sheep Animal Model, Using Hydroxyapatite Ceramics Prepared by Tape-Casting Method authors: - Lenka Kresakova - Lubomir Medvecky - Katarina Vdoviakova - Maros Varga - Ján Danko - Roman Totkovic - Tatiana Spakovska - Marko Vrzgula - Maria Giretova - Jaroslav Briancin - Veronika Šimaiová - Marian Kadasi journal: Bioengineering year: 2023 pmcid: PMC10044976 doi: 10.3390/bioengineering10030291 license: CC BY 4.0 --- # Long-Bone-Regeneration Process in a Sheep Animal Model, Using Hydroxyapatite Ceramics Prepared by Tape-Casting Method ## Abstract This study was designed to investigate the effects of hydroxyapatite (HA) ceramic implants (HA cylinders, perforated HA plates, and nonperforated HA plates) on the healing of bone defects, addressing biocompatibility, biodegradability, osteoconductivity, osteoinductivity, and osteointegration with the surrounding bone tissue. The HA ceramic implants were prepared using the tape-casting method, which allows for shape variation in samples after packing HA paste into 3D-printed plastic forms. In vitro, the distribution and morphology of the MC3T3E1 cells grown on the test discs for 2 and 9 days were visualised with a fluorescent live/dead staining assay. The growth of the cell population was clearly visible on the entire ceramic surfaces and very good osteoblastic cell adhesion and proliferation was observed, with no dead cells detected. A sheep animal model was used to perform in vivo experiments with bone defects created on the metatarsal bones, where histological and immunohistochemical tissue analysis as well as X-ray and CT images were applied. After 6 months, all implants showed excellent biocompatibility with the surrounding bone tissue with no observed signs of inflammatory reaction. The histomorphological findings revealed bone growth immediately over and around the implants, indicating the excellent osteoconductivity of the HA ceramic implants. A number of islands of bone tissue were observed towards the centres of the HA cylinders. The highest degree of biodegradation, bioresorption, and new bone formation was observed in the group in which perforated HA plates were applied. The results of this study suggest that HA cylinders and HA plates may provide a promising material for the functional long-bone-defect reconstruction and further research. ## 1. Introduction During the last decades, many researchers have investigated the biological process of bone regeneration in order to develop better strategies for bone healing [1,2]. The standard therapy for patients suffering from severe long-term problems, incomplete healing, nonunion healing (5–$10\%$ of fractures), or major bone defects is bone grafting using an autograft or allograft. Autologous grafts are collected mainly from other areas in the body (taken from the iliac crest, proximal tibia, ribs, etc.) and then transplanted into large fractures. There are many problems and obstacles associated with these procedures, e.g., treatment is limited by bone volume, recurrent fractures, soreness, associated complications such as infection, local haematoma, poor integration, donor site shortage, prolonged surgery time, donor site morbidity, and vascular problems [3,4,5]. Generally, allografts have low osteogenicity, low mechanical resistance, can evoke immunologic reactions, and pose a risk of graft rejection [6,7]. Therefore, a more sustainable and long-term treatment plan is necessary. For this purpose, bone graft replacements have been created to help treat damaged bones. The main strategies for bone repair are synthetic prostheses, carriers combined with various active molecules, cell therapy, biomimetic prostheses, bioactive polymers, inorganic composites, and others [8]. Biomaterials used in bone research should meet the main conditions of biocompatibility, biological stability, and the absence of immune response [9]. Despite a long period of research efforts, an ideal grafting material does not exist [10]. Calcium-phosphate (CaP)-based biomaterials have a composition comparable to bone minerals; therefore, they are able to induce biological responses similar to those found in bone healing [11]. CaP-based bone substitutes, especially tricalcium phosphate (αTCP and βTCP), hydroxyapatite (HA), and biphasic CaP-based bioceramics, present an appropriate choice in the field of bone tissue engineering and appear to be a suitable alternative to bone grafts in surgical bone replacement [12]. The composition of CaP is closely related to bone constitution; for that reason, CaP bone substitutes are successfully used in different clinical applications, e.g., artificial bone grafts, bone augmentations, maxillofacial reconstruction, bone replacements after tumour surgery, and orthopaedic and maxillofacial surgery [13,14,15]. CaP-based bioceramics are considered bioactive, biodegradable, biocompatible, and osteoconductive [5,16]. Certainly, it is necessary to mention that not all types of CaP have the same attributes and abilities. Some of them are able to degrade in short time in vivo, while others are stable. Although it is generally accepted that CaPs have osteoconductive properties [17,18], a number of studies have reported the osteoinductive capabilities of some types of CaP-based bioceramics [19,20,21,22,23,24,25,26,27]. The main advantages of CaP-based bioceramics are the chemical resistance, nonreactivity, nontoxicity, and degradability through cellular, biological, and chemical processes [14]. CaPs offer appropriate surface properties (e.g., roughness, porosity, and solubility), allowing the adhesion and proliferation of osteoblasts at the implant site and the increase in osteogenesis [5]. HA is one of the naturally occurring types of CaP that represents the essential ingredient and the largest amount of inorganic constituent in bones [28] and, related to that, provides the rigidity of the bone [29,30]. HA was reported as nontoxic, only minimally inflammatory, and osteoconductive [31,32]. Many previous findings have shown that HA has a very good biocompatibility, biomimetic character [6,33,34], and the functional characteristics that facilitate cell growth and the formation of new bone. Among the various synthetic biomaterials, HA gives the best results [35]. This material can be used alone or in combination with other biomaterials for bone reconstruction in many clinical applications [36,37]. HA can be synthesised by various methods and can be easily prepared in a laboratory [38]. For these properties, HA is still an interesting biomaterial that is used in bone tissue regeneration. The design of bioceramics also represents a new direction for bioceramics science [39]. As a result, and due to the positive scientific evidence, the main objective of this study was to observe the effect of the originally prepared HA ceramics (HA-perforated plates, HA-nonperforated plates, HA cylinders) on bone tissue regeneration. The HA ceramics were prepared using a tape-casting method which allows variation in the shape of samples after packing HA paste to the 3D-printed plastic form. The use of HA in cranial applications is relatively common and little or no stress and pressure develops under these conditions. In our study, we used a sheep long-bone-defect model. Our preclinical investigations focused on the effect of HA ceramics on the healing of bone defects, biocompatibility, biodegradability, bioresorption, osteoconductivity, osteoinductivity, and bonding to the surrounding tissue. After the in vivo application of HA cylinders and HA plates, we expected the formation of new bone tissue with properties similar to the original bone. ## 2.1. HA Synthesis and Preparation of HA Ceramics Hydroxyapatite powder was synthesised by the precipitation of 0.5 M Ca(NO3)2·4H2O (Sigma-Aldrich, St. Louis, MO, USA, analytical grade) solution and 0.5 M (NH4)2HPO4 (Sigma-Aldrich, analytical grade) solution with a molar ratio of Ca/P equal to 1.66. The aqueous solution of Ca2+ ions was applied slowly dropwise into the phosphate aqueous solution for 1.5 h using a peristaltic pump at pH close to 10.5, achieved by adding NH3(aq) (1:1) at 25 °C. Rotation speed of the magnetic stirrer was 450 rpm. The precipitates were allowed to age for 72 h at room temperature, with 24 h exchange of the solution above the HA layer. Then, the HA precipitates were washed with distilled water, ethanol, and filtered. The obtained hydroxyapatite powders (HAPs) were dried at 110 °C for 4 h, crushed, and sieved (Mesh 250). HA ceramics were prepared by a tape-casting method. First, the synthesised HAP was mixed with hydrogel (2 wt% carboxymethyl cellulose + 1 wt% polyacrylic acid) + glycerol for 5 min in a planetary ball mill (Fritsch, agate balls and vessel). The ratios of hydrogel to glycerol and HAP/hydrogel were 6:1 and 1:1, respectively. The obtained paste was moulded to polylactic acid forms printed on 3D printer (3D printer da Vinci 3 in 1, XYZ printing Inc, Thailand), dried at 37 °C for 12 h, removed from the form, and dried at 100 °C for 1 h and 120 °C for another hour. The final green samples were sintered at 1250 °C for 2 h at a heating rate of 5 °C/min. ## 2.2. Characterization of HA Ceramics The green samples had plate (25 × 10 × 3 mm), cubic (10 × 10 × 10 mm), or cylindrical (8 mm in diameter and 15 mm in length) shapes. The apparent density of the sintered ceramic samples was determined by calculation from the measurement of dimensions and mass of cubic-shaped samples. The theoretical density of the HA was 3.15 g·cm−3. The phase composition of the samples was analysed by X-ray diffraction (Philips X_PertPro). The microstructure of the scaffolds was observed using a scanning electron microscopy (SEM, JEOL FE SEM JSM-7000F). The compressive strength of the ceramic samples was measured by a LR5K Plus (Lloyd Instruments, Ltd., Fareham, UK) at the loading rate of 1 mm.min−1. The ceramic samples were then sterilised in a thermostat at 160 °C/1 h. ## 2.3. In Vitro Testing The substrates in the form of pellets (~6 mm in diameter and 0.5 mm in height) were soaked in PBS solution for 5 min at 37 °C, placed to the wells of a 96-well suspension plate, and 200 μL of the complete culture α-modification minimum essential Eagle medium (αMEM) ($10\%$ foetal bovine serum, $1\%$ ATB-ATM and osteogenic supplements: β-glycerophosphate 10 mM; ascorbic acid 50 μmL−1 and dexamethasone 50 nM, obtained from Sigma) containing 1.0 × 104 preosteoblastic murine MC3T3E1 cells (ATCC CRL-2593, Manassas, VA, USA) were carefully seeded on each disc. The density, distribution, and morphology of the MC3T3E1 cells grown on the tested discs for 2 and 9 days were visualised with fluorescent live/dead staining (fluorescein diacetate/propidium iodide) under an inverted optical fluorescence microscope (Leica DM IL LED, blue filter). ## 2.4. Animals, Surgical Procedures, and Postsurgical Management Twelve adult healthy female sheep of the Valachian/Merino breed were used in this study. Ethical approval was obtained from the State Veterinary and Food Administration of the Slovak Republic no. $\frac{2220}{17}$-221.The average weight of animals was 65.7 kg (range: 59–73 kg), and at the time of surgery the sheep were from 2 to 2.5 years old. General anaesthesia was induced by an intramuscular injection of butorphanol (0.1 mg/kg, Butomidor 10 mg/mL, Richter Pharma, Wels, Austria), medetomidine 0.02 mg/kg (Cepetor 1 mg/mL, CP-Pharma Handelsgesellschaft, GmbH, Burgdorf, Germany), and ketamine 8 mg/kg (Ketamidor 100 mg/mL, Richter Pharma, Wels, Austria) administered intravenously. In all sheep, the area around an impending operation on the left hind limb was shaved and prepared with a Betadine and alcohol solution using a sterile technique. We made a 3–5 cm long skin and soft tissue incision in the area of the planned defect. All animals were divided into 2 groups. Group 1 ($$n = 6$$): bone defects with a diameter of 6 mm and a depth of 15 mm were created by drilling in the distal end of the metatarsal bone, one defect in each animal. After that, an HA cylinder (15 × 6 mm) was implanted in the bone defect from the median side. Group 2 ($$n = 6$$): bone defects with dimensions 20 mm (length), 7 mm (width), and 3 mm (depth) were created by surgical chisels and filled with an HA plate (20 × 7 × 3 mm). Three HA plates were perforated by 7 holes with a diameter of 1 mm, while the remaining 3 were nonperforated (Figure 1). The original bone was partially removed, leaving an incomplete gap between the bones. All biomaterials completely filled the defect. After implanting the HA cylinders and HA plates, the surrounding tissues and skin were sutured. The surgical wound was covered by an aluminium fluid spray. The lack of bone fractures and correct performance of the surgical procedure was validated by X-ray (Philips Digital Diagnost, Delft, The Netherlands). After the surgical operation, the sheep were returned to cages and allowed to move freely without external support and fixation. Every second day, oxytetracycline dihydricum 20 mg/kg (Alamycin LA a.u.v., Norbrook, Newry, UK) was administered for 7 days after the surgery intramuscularly. Flunixin meglumine 2.2 mg/kg (Flunixin a.u.v., Norbrook, Newry, UK) was administered for postsurgical pain management intramuscularly, once a day for 7 days, and then as needed. All animals were euthanised 6 months after the surgery, according to a standard protocol, with xylazine 0.2 mg/kg (Rometar 20 mg/mL inj., Bioveta, Nitra, Slovak Republic) administered intramuscularly and ketamin 2 mg/kg (Narkamon 100 mg/mL inj., Bioveta) administered intravenously. The bones were examined by X-ray and CT and consequently evaluated histomorphologically and immunohistochemically. The results were compared with a control group consisting of physiological bone tissue. ## 2.5. Histological and Immunohistochemical Processing Bone tissue samples for histological examination were fixed in neutral formalin for 1–2 weeks. After washing in running water, the samples were placed in a chelaton solution for 4 weeks in a thermostat at 56 °C. The chelaton solution was changed once a week. At the end of the descaling process, the samples were embedded in paraffin. Sections were cut at 7 μm using a microtome (Leica, Bensheim, Germany) and stained by haematoxylin–eosin and Masson’s trichrome. Haematoxilin–eosin staining was used to assess the cell morphology and structure of the new bone. To examine the maturity of the newly formed bone, Masson’s trichrome staining was performed. Histologic sections were evaluated by a light microscope Olympus CX 43 (Olympus Corporation, Tokyo, Japan) and 300 MIPromicam digital camera (Promicra, Prague, Czech Republic). Evaluation was performed by two observers on five samples per each bone defect. The expression of type I collagen (COL1) was investigated by immunohistochemical staining. Bone specimens were decalcified in chelaton during a 3-week period and the specimens were then washed in sterile distilled water for several hours. After washing, the bone tissue was dehydrated in alcohol and embedded in paraplast. Subsequently, two slices with 7 μm thickness were taken from the implantation site for histological and immunohistochemical analysis. An immunohistochemical reaction was performed to demonstrate the presence of COL1 using a primary Rabbit polyclonal anticollagen I antibody (Abcam, ab233080) and a secondary DB DET SYS kit. The DB detection kit was a rabbit/mouse dual system (Biotech). DAB (3,3′-diaminobenzidine) (DAKO) was used to visualize the reaction. Finally, the cell nuclei were stained with acidic Mayer’s haematoxylin. ## 2.6. X-ray and CT Analysis X-ray analysis was done employing X-ray equipment (Philips Digital Diagnost, The Netherlands) at the exposure settings of 55–60 kV and 1.8–4.9 mAs and the pixel size of 0.133 mm. CT scans were obtained using a CT (Philips Brilliance 40-slice CT, Delft, The Netherlands) with the following scanning parameters: 120 kV, 250 mA, 300 mAs, pixel size 0.283 mm, thickness of slices 2 mm. We evaluated the presence of nonresorbed and nondegraded biomaterial, pathological and inflammatory changes, and the density of the newly formed bone tissue compared to the surrounding physiological bone. The bone density was evaluated using Hounsfield units (HU). ## 2.7. Ca/P Ratio The Ca/P ratio in newly formed bones and native bone was determined after dehydration in ethanol and coating with carbon by a field emission scanning electron microscopy (SEM) (JEOL FE SEM JSM-7000F, Jeol Ltd., Tokyo, Japan) equipped with the energy-dispersive X-ray analyser (EDX) (INCA, Oxford Instruments, Abingdon, UK). ## 2.8. Statistical Analysis Statistical analysis for differences between groups was performed using an unpaired t-test (GraphPad Prism 7.0 for Windows, GraphPad Software, San Diego, CA, USA). All data were expressed as means and standard deviations of means (SD). Differences were considered statistically significant at the levels of * = $p \leq 0.05$; = $p \leq 0.01$; * = $p \leq 0.001.$ ## 3.1. Microstructure, Properties, and Live/Dead Staining of Ceramic Samples The dense microstructure of the ceramic sample is shown in Figure 2. In the microstructure, a low fraction of 0.5–2.5 µm spherical pores is visible in the image, located mainly at the boundaries between three adjacent grains and formed by coalescence during sintering. The grain boundaries are relatively difficult to distinguish on fracture surfaces in a transgranular fracture mode, but from close observation, we believe that the HAP grain size did not exceed 5 µm. However, the sintering process was stopped before the final phase of densification of ceramics, when individual micropores were gradually eliminated from the microstructure. The relative density of the HA ceramic samples reached 84 ± $3\%$ of the theoretical HAP density. The XRD phase analysis (Figure 3) identified biphasic CaP ceramics with HA as the main phase (JCPDS 72-1243) and α-tricalcium phosphate (αTCP) as the secondary phase (JCPDS 29-0359). This composition would correspond to a small chemical nonstoichiometry of HAP powder during synthesis (e.g., as a result of substitution of carbonates for hydroxyl or phosphate group) with formation of calcium-deficient HAP. The average compressive strength of the samples was 71 ± 5 MPa, which is much higher than that of a cancellous bone (up to 15 MPa) but lower compared to a compact bone (up to 200 MPa) [40]. Nevertheless, it is a value sufficient for utilization of the ceramics as the bone defect filling material. Figure 4 shows a good adherence and spreading of osteoblastic cells on the ceramic surfaces and the absence of dead cells after culturing for 2 and 9 days. The growth of the cell population is clearly visible from the comparison of the 2- and 9-day cultured samples as a dense layer of osteoblasts covering the entire ceramic surface. These facts demonstrated the noncytotoxic character of the ceramic surfaces and the appropriate texture as well as the physicochemical properties of the samples. Obviously, αTCP is more soluble than HA and can significantly stimulate the osteogenic activity of osteoblasts in contact with the biphasic ceramic system [41]. ## 3.2. General Behaviour of Animals All sheep recovered well after anaesthesia and surgery with no serious complications. The general behaviour and condition were not negatively affected by the surgical intervention, and no abnormalities were observed regarding water and food intake behaviour. Visual examination showed a slight degree of lameness during the first 3 days after surgery with gradual improvement up to 10 days. Vital signs (body temperature, pulse rate, respiration rate) corresponded to the normal values. All animals survived without clinical signs of visually detectable pathological changes (damage) of wound. There were no macroscopic signs of infection, and no inflammatory processes were detected in any of the monitored sheep. All implants were well positioned at the surgery sites without any dislocation or loosening. No apparent breakage or other damage to the implants was observed. All HA cylinders and HA plates were well tolerated in vivo throughout the 6-month duration of the experiment. ## 3.3.1. HA Cylinders All implanted HA cylinders showed excellent biocompatibility with the surrounding bone tissue. We did not detect a fibrous interface, fibrous capsule, or pathological changes at the interface of the biomaterial and the bone. No cellular inflammatory reaction was detected. Biodegradation and bioresorption of all implanted HA cylinders was minimal. All implanted cylinders were very firmly attached to the adjacent bony tissue and well incorporated and exhibited excellent osteointegration and osteoconductivity. At the surface of HA cylinders, a new bone in close connection with the adjacent bone was seen. The newly formed bone was observed particularly at the periphery of the HA cylinders, and toward their centres, a number of bony tissue islets with osteocytes were seen, giving potential osteoinductive properties to the biomaterial (Figure 5). In all cases, complete bony bridging of the defect with a sporadically uneven surface of the newly formed bone was observed. The newly formed bone tissue exhibited typical cortical and trabecular organization. In some places, we observed the presence of an immature bone. Numerous osteons indicated high remodelling processes in the new bone. A positive immunohistochemical staining for COL1 showed that COL1 was strongly expressed in the cortical bone matrix as well as in the trabecular bone (Figure 6a,b). ## 3.3.2. HA Plates Based on our macroscopic and histological evaluation, no signs of inflammatory response were observed; therefore, the HA plates may be considered biocompatible. Complete resorption of the perforated HA plate was visible in two cases (Figure 7). No remnants of the biomaterial used were observed and the complete formation of new bone was visible. At the defect margins, excellent bonding of the new bone to the adjacent bone was observed. However, the surface of the newly formed bone tissue was slightly uneven, and the margin of the newly formed tissue slightly protruded from the surface. In one case, a small amount of the perforated nonresorbed plate was visible. No visible biodegradation of the nonperforated plates was observed. A thin bridge of the cortical bone covered the nondegraded and nonresorbed HA plates (Figure 7). In all cases, the implants were totally integrated into the surrounding cortical bone without interposition of fibrous tissue. The plate–host bone interface indicated excellent integration of the plates. Macro- and microscopic observations did not reveal presence of fibrous capsules around the plates. The process of bone remodelling was still going on at a high rate, as was demonstrated by the presence of large secondary osteons in various stages of formation. The immunohistochemical analysis showed the presence of COL1 in newly formed bony tissues of all samples. The COL1 was expressed in the bone matrix of the cortical bone. More noticeable distribution of COL1 was observed at the edges of the newly formed tissue (Figure 6c,d). ## 3.4. Ca/P Ratio The Ca/P ratio obtained using SEM-EDX analysis in newly formed bone was 1.56 ± 0.05 (mean ± SD) after application of the HA plates, 1.58 ± 0.04 after application of the HA cylinders, and 1.55 ± 0.05 (mean ± SD) was in native bone. The resulting comparison suggests that they are not statistically different ($p \leq 0.82$). ## 3.5. Radiographic Analysis Radiographic analysis was performed to support the broad characterization of the healing process. The correct position of the implants was verified in all animal groups with X-ray images immediately after surgery. The X-ray analysis performed 6 months later revealed no shift of the implants and demonstrated a stable bone bond between the HA ceramics and the host bones. The interface of the HA cylinders and HA plates and the surrounding cortical and trabecular bone tissue were free of inflammatory changes and no irritation of the surrounding tissue was noted. In accordance with the histological results, CT analysis indicated new bone formation in the defect area and around the nonresorbed biomaterials. In all samples, complete cortical bone bridging of the defect site was observed. The bone tissue reached the surface of the implants, with no apparent gap between implants and the new bone tissue. Neither fibrous encapsulation nor focal osteolytic and osteosclerotic changes were detected in the surrounding tissue (Figure 8). The average density of the newly formed cortical bone in comparison with the adjacent physiological bone is shown in Figure 9. In all cases, mineralization of the newly formed cortical bone did not reach the stage of complete mineralization of the native adjacent bone (Table 1). Compared to the native healthy bone, the density of the newly formed bone was significantly lower ($p \leq 0.01$). The fact that the newly formed bone still has a lower density compared to the surrounding healthy bone is physiological. Bone mineralization is gradual, the amount of Ca2+ in the bone increases and the bone density measurable on CT also gradually increases. ## 4. Discussion HA is one of the widely and regularly used bioceramics for bone regeneration, which has been investigated due to its excellent biological properties, potential resorbability, moulding capabilities, and easy manipulation. The use of various types of HA was described in diverse scientific sources [37,42]. HA is characterised by excellent biocompatibility with bony tissue, high bioactivity, and osteoconductivity. HA biomaterials have shown an increased effect on the osteogenic differentiation and proliferation rate of mesenchymal stem cells and a positive influence on osteogenesis [42,43]. Ansari et al. [ 8] reported that HA, besides other calcium phosphates, is frequently used for its biocompatibility and the potential to simulate the natural mineral portion of bones. Many other studies indicated an excellent biocompatibility of HA with bony tissue [10,35,38,42,43,44]. The biocompatibility of HA obtained from fish waste was also confirmed in vivo and in vitro in a study by Prado et al. [ 37]. Giorno et al. [ 38] stated that HA is a biomaterial whose rejection is only minimal or nonexistent. However, the results of their study showed a mild granulomatous and inflammatory response with the development of a fibrotic and collagenous capsule after implantation of HA-based cylinders containing $1\%$ zinc and lead ions. HA ceramics used in our study can be considered highly biocompatible and without ability to cause an inflammatory reaction. We did not observe signs of pathomorphological changes. Histomorphological analysis demonstrated no adverse tissue or inflammatory response. No fibrous capsule and no cellular inflammatory responses were detected at the bone–implant interface. Many various factors are involved in the gradual biodegradation and bioresorption of bioceramics, including physicochemical solution-mediated processes and the effects of multiple cells [10,45]. Some of these cells degrade bioceramics by phagocytotic mechanisms (monocytes/macrophages, osteoblasts) or by an acidic mechanism that lowers the pH of the environment and enables resorption of the present substrate (osteoclasts). Various mesenchymal cells, such as fibroblasts, are also involved in the process of bioceramic degradation and can induce the solubilisation of ceramics [46]. The cellular mechanism of the ceramic biodegradation process is modulated by several parameters, e.g., the intrinsic properties of bioceramics (particle size, chemical composition and preparation conditions, crystallinity, porosity), the area of implantation, as well as the environment at the implantation site (the presence of multiple proteins, hormones, cytokines, vitamins, ions) [33], and other circumstances (age, sex, and general metabolic health) [10]. Numerous cells involved in the complex mechanism of biodegradation can act directly or indirectly through their growth factors and cytokine secretions and their sensitivity to these molecules [46]. While the perforated HA plates used in our study were completely degraded in two cases and incompletely in one case, the nonperforated ones did not undergo biodegradation and bioresorption. Biodegradation and bioresorption of HA cylinders was only minimal. This indicated that different surface structure and shape significantly influenced the biodegradation processes of HA cylinders and HA plates. Brum et al. [ 35] contemplated that HA may have a lower degradation rate than other kinds of biomaterials. Klein at al. [ 47] reported that sintered hydroxyapatite materials showed no detectable resorption over a period of 9 months of implantation. In the study by Yang et al. [ 43], no significant degradation of the Zn–HA composite during the implantation period was reported. A homogeneous and slow degradation progress was observed. The low solubility of HA was also reported in the study by Cao et al. [ 48]. The authors of this study stated that the HA-based biomaterials are more suitable for long-term surgical and clinical applications. Biodegradation of HA cylinders and TCP cylinders implanted in the proximal tibia and distal femur was investigated by Eggli et al. [ 49]. The diameter of the implants was 3 mm, and 15 New Zealand White rabbits were used in this 6-month study. While TCP cylinders were degraded by up to $85.4\%$ after six months, HA cylinders showed only $5.4\%$ volume reduction. Further, the authors compared the HA ceramics with a pore size range of 50–100 microns and 200–400 microns. HA cylinders with smaller pores were completely infiltrated by bone after only four months, whereas in the HA cylinders with larger pores, bone tissue did not penetrate all pores and the amount of bony tissue in the implant after six months was small [49]. Porosity is one of the key features of biomaterials design for bone regeneration. Some critical aspects concerning the clinical success of bioceramics, (e.g., rate of resorption, angiogenesis, tissue ingrowth) depend on the intrinsic properties of the biomaterial but also on the shape, amount, and size of the pores [50]. HA has a porous structure which is comparable to the cancellous bone. The remodelling of HA can lead to mature bone formation [51]. The differences in porosity and roughness could impact dissolution and degradation process of the materials used for implantation [52,53]. According to Hing [54], the degradation of a porous surface could lead to a rapid release of Ca2+, which is one of the key factors that facilitate angiogenesis. A porous and rougher surface may be more appropriate for adsorption of biologically active molecules (bone morphogenetic proteins, growth factors). These factors support the attachment, differentiation, and proliferation of cells. Biomaterial degradation and subsequent dissolution depend also on the Ca/P ratio. A decrease in this ratio causes an increase in the solubility of HA in water [55]. Higher dissolution of HA results in the release of more Ca2+ and P ions, which can positively affect bone regeneration [56]. In the study conducted by Tanaka et al. [ 57], the unidirectional porous HA scaffold was inserted into mouse calvarial defects to evaluate the bone-forming ability. The unidirectional porous HA is the scaffold with pores continuously connected in the axial direction. This HA-based biomaterial with $84\%$ porosity showed a high cell number and excellent formation of new bone. Willie et al. [ 58] reported bone tissue ingrowth of porous implants inserted into the distal femur of sheep and also pointed to the fact that sheep and humans have a similar pattern of bone ingrowth into porous implants over time. Results of the mentioned study confirmed that the ovine model contributed to understanding of the skeletal attachment of porous-coated implants to the cancellous bone in humans. Triply periodic minimal surface (TPMS) hydroxyapatite implants were used in the repair process of rat femoral bone defects in a study by Charbonnier et al. [ 59]. Implants with gyroid porosity (GP) and implants with gyroid porosity reinforced by a cortical-like outer shell (GPRC) were specifically designed and created. The integrity of the implants as well as their ability to support bone ingrowth were evaluated 4, 6, and 8 weeks after implantation. The results of this study showed improved mechanical resistance of the GPRC implants and altered osteogenic mechanism compared to GP implants. Bone tissue was found around and inside of both implants, but it was preferentially formed in the area adjacent to the defect boundaries of the GP implants and in the implant core for the GPRC implants. No foreign body reaction or inflammation was detected in and around the implants. HA ceramics used in our study had appropriate surface properties for the bone tissue growth and also showed excellent osteoconductive properties. The surface of the nonperforated and nonresorbed HA plates, as well as the surface of the HA cylinders, was completely covered with new bone tissue in all cases. Islets of bone tissue were observed also inside the HA cylinders, although the biodegradation of the HA cylinders was minimal. We indicated a potential osteoinductive character of the HA cylinders. The study by Chu et al. [ 44], conducted in rabbits, investigated three types of biomaterials for bone tissue replacement: HA–20vol%Ti, Ti–metal, and a dense HA ceramic. After a 3-month observation, they concluded that the HA–Ti composite exhibited better osseointegration and osteoconduction properties than the Ti–metal and dense HA ceramic, and it was considered a promising biomaterial for hard tissue replacement. The study by Machado et al. [ 60] evaluated the biocompatibility and osteoconduction in surgical defects filled with nanohydroxyapatite microspheres containing $1\%$ strontium (nano-SrHA) and stoichiometric nano-HA microspheres (nano-HA) compared to the clot (control) in an animal model of sheep. Three perforations with a diameter of 2 mm were made on the medial surface of the tibia. After 30 days, all groups showed new bone formation from the periphery to the centre of the defect, while it was less intense in nano-SrHA group. A discrete mononuclear inflammatory infiltrate was observed in all groups but both materials were considered biocompatible and osteoconductive. In the study by Zhao et al. [ 61] composite scaffolds with HA were used for the repair of rabbit tibia bone defects. Results of this study indicated that the scaffolds had excellent osteoinductivity and osteoconductivity and remarkably promoted new bone formation without any adverse effects. The development of bioceramics with osteoinductive capabilities has recently been a very important achievement in the field of CaP. Various CaPs have shown osteoinductive properties in that they have the potential to initiate differentiation of undifferentiated cells towards the osteogenic lineage. This leads to formation of new bone, even without exogenous bone morphogenetic protein [50]. The mechanism of osteoinduction has not yet been fully elucidated. Several factors are involved in this process, e.g., the chemical composition, macroporosity, size and geometry of pores, microporosity, microstructure, and surface area [50,62,63]. One of the acceptable hypotheses combines the natural ability of CaP to bind bone morphogenetic proteins with the presence of scaffold concavities that promote retention of bone morphogenetic proteins and ions in the scaffold environment, creating a convenient area for the mesenchymal stem cells differentiation [50]. Li et al. [ 64] reported that HA has the ability to promote the osteogenic differentiation of stem cells and thereby accelerate the regeneration process of bone. This study further points to a rat calvarial repair model to observe the degradation–diffusion–reconstruction behaviour of HA in the bone-repair process. The authors firstly demonstrated the degradation of HA, followed by the diffusion of the degraded product, and finally reconstruction so that new HA was formed to repair the bone defect. In the study by Lin et al. [ 65], pluripotent mouse stem cells exposed to HA have expressed some osteo-specific genes. This is consistent with the proposition that the interaction of HA–cell occurs through an osteoinductive potential capable of promoting differentiation into osteoblasts. Ressler et al. [ 66] also stated that HA can enhance osteogenesis and improve bone regeneration processes. The osteogenic ability of four different ceramic constructs was observed in a study by Viateau et al. [ 67]. This study compared the osteogenic potential of ceramic scaffolds (Porites coral, Acropora coral, β-TCP and banked bone) with different resorbability. Tissue-engineered constructs were created with or without autologous bone marrow stromal cells and implanted in the ectopic subcutaneous pouch in sheep. New bone tissue formation was higher in the Porites coral and Acropora coral than in other constructs, and a direct correlation between implant resorption and new bone formation was seen. Among the implants, coral scaffolds containing MSCs provided the best results. In a study of Bensaid et al. [ 68], a metatarsal bone defect with a clinically relevant volume was reconstructed in an animal model of sheep. A porous coralline-based hydroxyapatite scaffold in combination with mesenchymal stem cells (MSCs) was used for implantation. Results were compared with coral/MSCs scaffolds and autografts. At 4 months, both constructs had the same osteogenic potential as autologous bone grafts in terms of the amount of new bone ($$p \leq 0.89$$). The bone defect was completely replaced by newly formed bone within 14 months. The rate of bone healing was improved when coralline-based HA/MSCs scaffolds were used (the defect healed in five out of seven animals) compared with the coral–MSCs construct (one out of seven healed), but it was still less satisfactory than using autografts (five out of five animals healed). A study by Viateau et al. [ 69] performed in a sheep model demonstrates the successful treatment of segmental long-bone defects using standardised particulate bone constructs engineered from coral granules and in vitro expanded autologous MSCs. The results were compared with the implantation of an autologous bone graft and a coral scaffold alone. The authors found nonsignificant differences between the amount of new bone in defects filled with coral granules/MSCs bone construct and bone defects filled with autograft, with radiological scores significantly different between the two groups ($21\%$ and $100\%$). The osteogenic ability of the coral/MSCs bone construct is similar to bone autografts. The authors also pointed out that the bone construct preparation procedure is much simpler compared to the preparation of customised massive constructs using computer-assisted techniques. Two different types of scaffolds were tested in a study by Kon et. al. [ 70], where one of them was made of ion-doped HA/β-TCP and the other was made of undoped HA only. These HA-based scaffolds with a hierarchically organised structure developed through a biomorphic transformation process and showed good results after application in a segmental bone defect in a metatarsal shaft of sheep in terms of safety, osteoinductivity, osteoconductivity, osteointegration, vascularization, and mechanical performance. Histological evaluation did not show any fibrous encapsulation or inflammatory processes at the bone–scaffold interface in both groups. Both scaffolds were well-integrated with the adjacent host bone. The ion-doped HA/β-TCP group showed earlier new bone formation, visible after 3 months. Implant resorption at CT and radiography appeared as gradual fragmentation with no significant differences between the two groups. The capacity of HA macroporous ceramic cylinders to support large defect repair in the tibia of sheep was investigated by Marcacci et al. [ 71]. Adjacent bone tissues showed adequate integration of ceramic with newly formed bone as early as 20 days after surgery. After 4 months, extensive integration of the HA ceramic with bone tissue, as well as the formation of compact appearing bone, was detected radiographically and confirmed by morphological study. Sufficient bone growth occurred to allow recovery and more than $80\%$ of the surface of the HA cylinder was covered with new bone tissue. Transverse sections taken through the ceramic cylinders revealed bone formation inside the central canal of the cylinder. A regular lamellar organization of Haversian systems was found in the new compact bone and in most of the bony trabeculae. Areas of woven bone, especially limited to the bone–ceramic interface, represented a smaller part of bone, which is also consistent with our study. The data from this study showed that large defects in a weight-bearing long bone can be repaired with full functional restoration. ## 5. Conclusions In conclusion, we can state that the HA cylinders and HA plates used in our study, prepared using a tape-casting method in 3D-printed forms, had a positive impact on new bone formation. Both biomaterials were highly biocompatible, osteoconductive, and were well osteointegrated, with potential osteoinductive properties. The ability of biodegradation and bioresorption of these biomaterials is substantial. All HA plates and HA cylinders were not broken or damaged, so their mechanical properties are also of great importance. We consider HA cylinders and HA plates to be suitable biomaterials for further research and potential use in human medicine. ## References 1. Campana V., Milano G., Pagano E., Barba M., Cicione C., Salonna G., Lattanzi W., Logroscino G.. Bone substitutes in orthopaedic surgery: From basic science to clinical practice. J Mater. Sci. Mater. Med. (2014) 25 2445-2461. DOI: 10.1007/s10856-014-5240-2 2. 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--- title: Inflammation Related to Association of Low Uric Acid and Progression to Severe Disease in Patients Hospitalized for Non-Severe Coronavirus Disease 2019 authors: - Masafumi Kurajoh - Yoshikazu Hiura - Ryutaro Numaguchi - Yasutaka Ihara - Takumi Imai - Tomoaki Morioka - Masanori Emoto - Yukio Nishiguchi journal: Biomedicines year: 2023 pmcid: PMC10044977 doi: 10.3390/biomedicines11030854 license: CC BY 4.0 --- # Inflammation Related to Association of Low Uric Acid and Progression to Severe Disease in Patients Hospitalized for Non-Severe Coronavirus Disease 2019 ## Abstract Uric acid has antioxidant properties. To examine whether a low uric acid level is associated with severe coronavirus disease 2019 (COVID-19) progression via inflammation, alveolar damage, and/or coagulation abnormality, a retrospective observational study of 488 patients with non-severe COVID-19 and serum uric acid level ≤7 mg/dL at admission was conducted. Serum C-reactive protein (CRP), serum Krebs von den Lungen 6 (KL-6), and plasma D-dimer levels were also measured as markers of inflammation, alveolar damage, and coagulation abnormality, respectively. Median values for uric acid, CRP, KL-6, and D-dimer at admission were 4.4 mg/dL, 3.33 mg/dL, 252.0 U/mL, and 0.8 µg/mL, respectively. Among the total cohort, 95 ($19.5\%$) progressed to severe COVID-19 with a median (interquartile range) time of 7 (4–14) days. Multivariable Cox proportional hazards regression analysis showed that low uric acid level was associated with a higher rate of severe COVID-19 progression. However, uric acid level was inversely associated with CRP level, and the association between the level of uric acid and severe COVID-19 progression was significantly different with and without CRP level inclusion. In contrast, no such association was found for KL-6 or D-dimer level. Low uric acid may contribute to severe COVID-19 progression via increased inflammation in subjects without hyperuricemia. ## 1. Introduction Worldwide, the numbers of coronavirus disease 2019 (COVID-19) cases caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and showing progression to increased severity have been reported to be increasing [1,2,3]. Accumulating evidence also indicates that pathophysiological factors considered to be related to oxidative stress [4,5], including inflammation, alveolar damage, and coagulation abnormality, underlie the development of severe COVID-19 cases [6,7]. Uric acid has been shown to have an ability to gain antioxidant properties by scavenging reactive oxygen species (ROS) as well as pro-oxidant properties by generating ROS [8,9,10,11]. Of interest, several studies have found that not only high but also low uric acid levels were associated with an increased incidence of severe COVID-19 progression, such as in cases with mechanical ventilation, intensive care unit (ICU) admission, and/or composite outcome, as well as COVID-19-related mortality such as in-hospital death [12,13,14], while a reduction in the antioxidant property is considered to be involved in those associations in patients with a low level of uric acid. The administration of uric acid precursors has been indicated to suppress inflammatory mediators thorough ROS elimination in cases of indomethacin-induced enteropathy [15]; thus, it is speculated that low uric acid may contribute to COVID-19 severity via inflammation. However, to the best of our knowledge, no study has been conducted to comprehensively examine whether the association of a low uric acid level with severe COVID-19 progression is related to inflammation, alveolar damage, and/or coagulation abnormality. This study aimed to clarify the role of reduced uric acid in progression to severe COVID-19. To achieve this objective, we examined the records of patients initially hospitalized for non-severe COVID-19 and with a serum uric acid level ≤7 mg/dL at the time of admission. From the data obtained, associations of serum uric acid level with [1] severe COVID-19 incidence and [2] markers of inflammation, alveolar damage, and coagulation abnormality, as well as [3] the effects of markers of inflammation, alveolar damage, and coagulation abnormality on the association of serum uric acid level with severe COVID-19 progression were analyzed. Although there are various markers related to inflammation, alveolar damage, and coagulation abnormality, it has been reported that serum C-reactive protein (CRP), serum Krebs von den Lungen 6 (KL-6), and plasma D-dimer levels, respectively, are specific useful markers of those, being used similarly for COVID-19 cases [16,17,18]. Therefore, in the present study, serum CRP was used as a marker of inflammation, serum KL-6 as a marker of alveolar damage, and plasma D-dimer as a marker of coagulation abnormality. ## 2.1. Study Design This observational retrospective study analyzed patients treated at Osaka City Juso Hospital, designated as a priority medical institution for COVID-19 by the Osaka Prefectural Government, and the first to focus on patients with non-severe COVID-19 in Japan. This investigation was conducted in full accordance with the principles of the Declaration of Helsinki and Ethical Guidelines for Clinical Studies by the Ministry of Health, Labor and Welfare, Japan. The protocol, which included anonymization of patient information, was approved by the Ethics Committee of Osaka City Juso Hospital on 23 June 2021 (No. 3-A1) and the Graduate School of Medicine of Osaka City University, which were merged in 2022 to form Osaka Metropolitan University on 5 October 2021 (approval No. 2021-159). ## 2.2. Inclusion and Exclusion Criteria Inclusion and exclusion criteria for the present study are discussed in the following section. Patients admitted to Osaka City Juso Hospital for non-severe COVID-19 between October 2020 and May 2021 were considered eligible for inclusion in this study. Exclusion criteria included those [1] with hyperuricemia, i.e., serum uric acid level at admission >7.0 mg/dL [19], [2] with severe COVID-19 at the time of admission, or [3] transferred to our hospital from a tertiary hospital following improvement from severe to non-severe COVID-19. Additionally, those who [4] received an immunosuppressive agent, [5] were pregnant, [6] self-discharged, or [7] were missing important data were not included. Since the purpose of this study was to analyze the significance of a low blood level of uric acid in cases of severe COVID-19 progression, treatment with a uric acid-lowering agent was not included in the exclusion criteria. ## 2.3. Determination of Uric Acid, CRP, KL-6, and D-Dimer Blood Levels Blood parameters including uric acid, CRP, KL-6, and D-dimer levels were routinely measured using a sample obtained at the time of admission, with or without fasting [20,21]. Serum uric acid was measured by use of a uricase-N-(3-sulfopropyl)-3-methoxy-5-methylaniline assay (L-Type UA M; FUJIFILM Wako Pure Chemical Corporation, Osaka, Japan), serum CRP by use of a latex-enhanced immunoturbidimetric assay (IATRO CRP-EX; LSI Medience Corporation, Tokyo, Japan), and serum KL-6 by use of a latex immunoturbidimetric assay (Nanopia KL-6; Sekisui Medical Co. Ltd., Tokyo, Japan), with analyses of those performed with an automated biochemical analyzer (LABOSPECT 008; Hitachi High-Technologies, Tokyo, Japan). Plasma D-dimer was measured using a latex immunoturbidimetric assay (Nanopia D-dimer; Sekisui Medical Co. Ltd., Tokyo, Japan), with analysis performed with an automated coagulation analyzer (SYSMEX CS-2500; Sysmex Corporation, Kobe, Japan). The reference values for CRP, KL-6, and D-dimer levels were ≤0.3 mg/dL, <500 U/mL, and ≤1.0 µg/mL, respectively. ## 2.4. Diagnosis of COVID-19 The diagnosis of each patient was obtained using methods based on clinical practice guidelines for COVID-19 published by the Japanese Ministry of Health, Labour and Welfare [22,23]. Briefly, COVID-19 diagnosis was obtained utilizing nucleic acid amplification testing procedures, including a real-time polymerase chain reaction (PCR) assay, loop-mediated isothermal amplification, and transcription-mediated amplification, and also confirmed with quantitative or qualitative antigen test results for SARS-CoV-2, as approved for use at hospitals or clinics by the Japanese Ministry of Health, Labour and Welfare. ## 2.5. Classification of COVID-19 Severity Level Severity classification was determined according to oxygenation and respiratory symptoms. Patients with a percutaneous oxygen (SpO2) saturation ≥$96\%$, and no respiratory symptoms, or coughing only without shortness of breath were classified as mild; those with an SpO2 saturation ranging from $93\%$ to $96\%$, shortness of breath, and pneumonia findings were classified as moderate I (no respiratory failure); those with SpO2 saturation ≤$93\%$ and requiring O2 administration were classified as moderate II (respiratory failure); and those admitted to the ICU or who required a mechanical ventilator were classified as severe. Using those classifications, mild, moderate I, and moderate II cases were defined as non-severe COVID-19 for the present study [22,23]. ## 2.6. COVID-19 Patient Management during Hospitalization The present patients were treated according to guidelines current at that time [22,23]. For discharge from a bed for COVID-19, the following criteria were used: (i) 10 days from symptom onset date and 72 h after resolution of symptoms, or (ii) for patients with negative results of PCR or quantitative antigen testing performed twice with at least 24 h between the tests, 24 h following symptom resolution [22,23]. Patients who showed progression from non-severe to severe COVID-19 were transferred to a hospital classified as specialized for treatment and had an ICU bed available, except those who did not require life-prolonging therapy. ## 2.7. Outcome The outcome for the present cases was based on period (days) between hospital admission and severe COVID-19 status progression. Any patient who did not progress to severe and met the discharge criteria was noted as not demonstrating progression to severe status following discharge, as previously described [21,24]. ## 2.8. Other Clinical Assessments Dates of onset of COVID-19 based on symptom appearance, use of medication, present and past illness, current smoking habit, height, and body weight for each subject were noted. Weight (kg) in kilograms divided by height in meters squared (kg/m2) was used to determine body mass index (BMI). An equation previously described for Japanese subjects was used to calculate estimated glomerular filtration rate (eGFR) [25]. Diagnosis of diabetes, hypertension, or dyslipidemia was performed according to history of treatment for the condition, or the American Diabetes Association, Japanese Society of Hypertension, or Japan Atherosclerosis Society guidelines [26,27,28]. ## 2.9. Statistical Analysis Due to the exploratory nature of the study, sample size calculation was not performed and all available data for the patients during the study period were used. Data representing clinical characteristics and baseline demographics are shown as median values (interquartile range [IQR]) for continuous variables, or number (percentage) for categorical variables. A multivariable Cox proportional hazards regression model was employed to examine the association of serum uric acid level with progression from non-severe to severe COVID-19, with adjustments made for potential confounding factors, noted as the following: age, sex, BMI, smoking habit, diabetes mellitus, hypertension, dyslipidemia, cerebrovascular and/or cardiovascular disease, chronic respiratory disease, eGFR, days from onset of disease to admission, COVID-19 severity at time of admission, and uric acid-lowering agent usage. Next, the consistency of the findings was analyzed after subgrouping the subjects based on sex (male, female) and uric acid-lowering agent usage (presence, absence). As for the associations of serum uric acid level with serum CRP, serum KL-6, and plasma D-dimer levels, analyses were performed using Pearson’s correlation coefficient and multivariable linear regression, with adjustments for the same factors as noted in the above-mentioned multivariable Cox proportional hazards regression model. Additionally, associations of serum CRP, serum KL-6, and plasma D-dimer level with progression to severe COVID-19 were examined using the same multivariable Cox proportional hazards regression model used for serum uric acid level. Finally, the relationship between uric acid level in serum and non-severe to severe COVID-19 progression was analyzed with an additional adjustment for serum CRP, serum KL-6, and plasma D-dimer level. Any change in serum uric acid hazard ratio (HR) indicating progression from non-severe to severe COVID-19, shown by these additional adjustments, was evaluated using a nonparametric bootstrap method with 1000 replications [29,30]. The values for serum CRP, KL-6, and plasma D-dimer levels were transformed logarithmically before the simple and multivariable regression analyses performed due to the skewed distribution. For all data analyses, the R software package, version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria), was used. Presented p values are two-tailed, with <0.05 considered to indicate statistical significance. ## 3.1. Study Population During the study period, 573 patients with COVID-19 were admitted to Osaka City Juso Hospital. Patients with a serum uric acid level at admission >7.0 mg/dL were excluded ($$n = 33$$). In addition, those with severe COVID-19 at the time of admission ($$n = 5$$) or transferred to our hospital from a tertiary hospital following improvement from severe to non-severe COVID-19 ($$n = 28$$) were not analyzed for this study. Finally, patients who were receiving an immunosuppressive agent ($$n = 9$$), pregnant ($$n = 1$$), self-discharged ($$n = 1$$), or missing important data ($$n = 8$$) were not included in the analyses. Thus, a total of 488 patients (254 males, 234 females) with non-severe COVID-19 and a serum uric acid level ≤7 mg/dL at the time of admission were enrolled in this retrospective observational study as subjects. ## 3.2. Clinical Characteristics of Patients The characteristics of the enrolled patients ($$n = 488$$) are presented in Table 1. The median value for uric acid for all was 4.4 mg/dL, and a uric acid-lowering agent was administered to 54 ($11.1\%$). CRP, KL-6, and D-dimer median values were 3.33 mg/dL, 252.0 U/mL, and 0.8 µg/mL, respectively. The numbers of patients classified as mild, moderate I, and moderate II COVID-19 were 211 ($43.2\%$), 167 ($34.2\%$), and 110 ($22.5\%$), respectively. ## 3.3. Medications Given for COVID-19 after Hospitalization The medications given to the present COVID-19 patients included favipiravir in 370 ($75.8\%$), steroids in 303 ($62.1\%$), ciclesonide in 183 ($38.4\%$), remdesivir in 24 ($4.9\%$), and baricitinib in 2 ($0.4\%$). ## 3.4. Progression from Non-Severe to Severe COVID-19 Ninety-five ($19.5\%$) of the present 488 patients with non-severe COVID-19 at the time of hospitalization progressed to severe COVID-19, while 393 ($80.5\%$) were discharged. From hospital admission to severe COVID-19 progression there was a median term of 7 days (IQR 4–14, range 1–40), while the median term to discharge was 12 days (IQR 9–16, range, 3–47). ## 3.5. Serum Uric Acid Level Associated with Progression from Non-Severe to Severe COVID-19 Multivariable Cox proportional hazards regression model results are presented in Table 2. A significant association of low serum uric acid level at admission with a higher rate of severe COVID-19 progression was noted, which was independent of the observed potential confounders [HR for decrease of 1 mg/dL 1.279, $95\%$ confidence interval (CI) 1.021–1.602; $$p \leq 0.032$$]. Furthermore, no remarkable inconsistency was observed after dividing the patients into subgroups based on sex and use of a uric acid-lowering agent (p values for interaction 0.948 and 0.473, respectively). ## 3.6. Serum Uric Acid Level Associated with Inflammation but Not Alveolar Damage or Coagulation Abnormality Markers The values for serum uric acid level were plotted against markers of inflammation, alveolar damage, and coagulation abnormality (Figure 1). Simple regression analysis findings showed that serum uric acid level was inversely correlated with log CRP (r = −0.165, $p \leq 0.001$), whereas there was no significant correlation with log KL-6 ($r = 0.086$, $$p \leq 0.058$$) or log D-dimer (r = −0.074, $$p \leq 0.104$$) levels observed. Additionally, multiple linear regression analysis results showed that serum uric acid level was significantly associated with log CRP but not log KL-6 or log D-dimer levels, even after the application of adjustments for other variables (Figure 1). ## 3.7. Inflammation Shows Stronger Relationship with Progression from Non-Severe to Severe COVID-19 as Compared to Alveolar Damage and Coagulation Abnormality Markers The associations of inflammation, alveolar damage, and coagulation abnormality markers with progression from non-severe to severe COVID-19 were examined using the same multivariable Cox proportional hazards regression model applied to serum uric acid level (Table 3). HR values were estimated based on a one standard deviation increase in each variable, then compared with each other. As compared with log KL-6 and log D-dimer, log CRP showed the strongest association with progression to severe COVID-19 (HR 2.079, $95\%$ CI 1.389–3.113; $p \leq 0.001$). ## 3.8. Inflammation Marker Found to Influence Association of Serum Uric Acid Level with Severe COVID-19 Progression The association of serum uric acid level at the time of admission with progression from non-severe to severe COVID-19 was re-evaluated with additional adjustments for the markers of inflammation, alveolar damage, and coagulation abnormality (Table 4). The results showed that the association of serum uric acid level with severe COVID-19 progression was significantly different from results obtained by analysis with and without inclusion of serum CRP level (HR = 1.337 vs. HR = 1.233; $$p \leq 0.041$$ by bootstrap method), whereas no statistically significant difference was noted for serum KL-6 or plasma D-dimer level, suggesting that inflammation influenced the association of uric acid with severe COVID-19 progression. ## 4. Discussion In this study of patients initially hospitalized for non-severe COVID-19 and with a serum uric acid level ≤7 mg/dL at the time of admission, a low serum uric acid level was found to be significantly associated with a higher rate of progression to severe COVID-19, with no remarkable difference noted after stratification by sex or use of a uric acid-lowering agent. Furthermore, there was an inverse association of serum uric acid with serum CRP noted, but not with serum KL-6 or plasma D-dimer level. In addition, the association of serum uric acid level with severe COVID-19 progression was significantly different with and without inclusion of serum CRP, but not of serum KL-6 or plasma D-dimer level. These results suggest that low uric acid contributes to severe COVID-19 progression via increased inflammation in subjects without hyperuricemia. Several previous studies have reported associations of low serum uric acid level with severity and mortality in COVID-19 patients [12,13,14], as well as the severity of other infectious diseases such as intra-abdominal sepsis and candidemia [31,32]. The Japan COVID-19 Task Force cohort found that a low uric acid level was associated with an increased risk of mechanical ventilation in 1523 patients with COVID-19 [12]. Additionally, in a study of 1854 hospitalized patients with COVID-19, Chen B and colleagues noted that a low uric acid level was associated with an increased risk of composite outcome, ICU admission, and mechanical ventilation [13], while Li and colleagues found an association of low uric acid with an increased risk of in-hospital death in a cohort of 540 severe or critical COVID-19 patients [14]. Consistent with those reports, the present results also indicated that a low level of serum uric acid was significantly associated with higher rate of progression from non-severe to severe COVID-19 in the examined patients. However, no known studies have comprehensively investigated the association between serum uric acid level and COVID-19 severity, including its association with inflammation, alveolar damage, and/or coagulation abnormality, which are known to underly the pathogenesis of severe COVID-19 cases [6,7]. Therefore, the role of low uric acid in severity of COVID-19 remains unclear. In the present study, serum uric acid level was found to be significantly associated with the level of serum CRP, but not that of serum KL-6 or plasma D-dimer. Furthermore, serum CRP, but not serum KL-6 or plasma D-dimer level, had an influence on the association between serum uric acid level and severe COVID-19 progression. These results provide a deeper understanding of the mechanism underlying the association of low uric acid and severe COVID-19 progression, and indicate the importance of inflammation in that association. The antioxidant property of uric acid may be a key factor related to the importance of inflammation in the relationship of low serum uric acid level with severe COVID-19 progression. Accumulating evidence suggests that oxidative stress caused by overproduction of ROS and disruption of the antioxidant system plays a crucial role not only in the pathogenesis of SARS-CoV-2 infection, but also in the severity of COVID-19 [33,34]. Oxidative stress has been implicated to be involved in inflammation via signaling mechanisms, including redox-sensitive activation of transcription factors such as nuclear factor-kappa B in respiratory viral infections [35,36], and is also thought to be related to inflammation in COVID-19 cases [37,38]. Inflammation has a critically important role in the severity of COVID-19 progression [6,7], with elevated levels of inflammatory cytokines as well as CRP secreted under the influence of inflammatory cytokines, such as interleukin-6 and tumor necrosis factor-alpha, observed in patients with severe COVID-19 [39,40], suggesting the involvement of a cytokine storm in severe COVID-19 progression [41]. Consistent with those reports, a strong association of CRP level with severe COVID-19 progression was observed in the present cohort. On the other hand, the administration of steroids, an anti-inflammatory medication, has been shown to reduce the level of severity [7,38,42]. In addition, other reports have noted that the administration of antioxidants resulted in reductions in inflammation and severity in respiratory viral infection cases [43,44,45], with similar effects expected in patients affected by COVID-19 [5,34]. Additionally, uric acid has been shown to gain an antioxidant property by scavenging ROS, such as singlet oxygen, and peroxyl and hydroxyl radicals, and is also known to provide approximately half of the free radical-scavenging capacity in the human body [8,46]. Furthermore, it has been reported that administration of uric acid precursors resulted in a suppression of inflammatory mediators through ROS elimination in indomethacin-induced enteropathy cases [15]. The present results along with those in previous reports suggest that a low level of uric acid is not able to exert anti-inflammatory effects by scavenging ROS via its antioxidant property, resulting in severe COVID-19 progression in patients without hyperuricemia. On the other hand, renal uric acid excretion is known to be increased in patients with COVID-19 [47], suggesting a link to inflammation, as also seen in severe acute respiratory syndrome patients [48]. Therefore, it cannot be ruled out that the relationship of serum uric acid level and inflammation with COVID-19 severity observed in the present study reflects increased renal uric acid excretion due to inflammation in affected patients. To provide better clarification of the role of uric acid in inflammation and severe COVID-19 progression, an interventional study that examines the association of uric acid administration with inflammation and severe progression in patients with non-severe COVID-19 and without hyperuricemia will be needed. While ROS have been reported to be related to alveolar damage and coagulation abnormality [4,5], the present study did not find a significant association of serum uric acid level with serum KL-6 or plasma D-dimer level, and there was also no evidence of an effect of KL-6 or D-dimer on the relationship of serum uric acid level with severe COVID-19 progression. Those results indicate that a low level of uric acid contributes to severe COVID-19 progression, without affecting alveolar damage or coagulation abnormality. However, median serum KL-6 and plasma D-dimer values were not higher than the reference values. In addition, only the levels of serum KL-6, as a marker of alveolar damage, and plasma D-dimer, a marker of coagulation abnormality, were used in the present study. Thus, the relationship of uric acid with alveolar damage and coagulopathy requires further investigation. There are important limitations to this study that should be noted. First, serum uric acid level was measured at the time of admission, and that level prior to the onset of COVID-19 was not considered in the analyses. Second, since vaccinations against SARS-CoV-2 for the general public began in late May 2021 in Osaka Prefecture, patients who had received such vaccination were not included. It is unfortunate that we were not able to conduct a survey of SARS-CoV-2 strains that affected the individual cases examined, as that made it impossible to examine the effects of vaccination against SARS-CoV-2 or related strains on the results obtained in the present study. Third, it is also important to point out that high-sensitivity CRP testing was not performed. Fourth, though the diagnosis of COVID-19 in each case was made using a test approved by the Japanese Ministry of Health, Labour and Welfare for use at hospitals or clinics, it was not possible to investigate the diagnostic methods employed for each patient. Therefore, the association of serum uric acid level, inflammation, alveolar damage, and coagulation abnormality markers with COVID-19 severity, as well as diagnostic methods, could not be analyzed. Fifth, it was not possible to fully investigate occurrences of death or extrapulmonary manifestations, including involvement of the renal, urogenital, gastrointestinal, hepatic, endocrine, immune, and/or neurological systems [49,50,51]. Finally, this study was conducted using patients treated at a single center. However, based on instructions provided by Osaka Prefecture, Osaka City Juso Hospital had agreed to accept patients with non-severe COVID-19 from throughout Osaka Prefecture; thus, there was no apparent selection bias related to the results. ## 5. Conclusions The present findings regarding patients initially placed in a hospital for the treatment of non-severe COVID-19 indicate a significant association of a low level of serum uric acid (≤7 mg/dL) at the time of admission with higher rate of progression to severe COVID-19. However, an inverse association of uric acid level with CRP level was noted, while the association between uric acid level and progression to severe COVID-19 was significantly different with and without its inclusion. Together, the present results indicate that a low level of uric acid contributes to severe COVID-19 progression via increased inflammation in individuals without hyperuricemia. ## References 1. 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--- title: 'Immediate Effects of Whole-Body versus Local Dynamic Electrostimulation of the Abdominal Muscles in Healthy People Assessed by Ultrasound: A Randomized Controlled Trial' authors: - Lorena Álvarez-Barrio - Vicente Rodríguez-Pérez - César Calvo-Lobo - Raquel Leirós-Rodríguez - Eduardo Alba-Pérez - Ana Felicitas López-Rodríguez journal: Biology year: 2023 pmcid: PMC10044981 doi: 10.3390/biology12030454 license: CC BY 4.0 --- # Immediate Effects of Whole-Body versus Local Dynamic Electrostimulation of the Abdominal Muscles in Healthy People Assessed by Ultrasound: A Randomized Controlled Trial ## Abstract ### Simple Summary The muscles of the abdominal wall play a fundamental role in the stabilization of the pelvis and the spinal column, and they must function properly. The simultaneous combination of physical exercise with electrical currents, called dynamic electrostimulation, can have beneficial effects on this musculature in terms of gaining muscle mass and strength. Our research aimed to determine the immediate effects of a single session of dynamic electrostimulation on the thickness of the abdominal musculature and the inter-rectus distance evaluated by ultrasound, as well as on the physiological parameters of heart rate, blood pressure, and body temperature. In addition to the possible differences according to the way of application—local with electrodes placed in the abdominal area or global with whole-body electrostimulation—a total of 120 healthy participants were randomly divided into three groups: WB-EMS, EMS, and control groups. No differences were found in the results of the variables analyzed between the groups, except for heart rate. The EMS group showed a smaller increase in post-intervention heart rate compared to the WB-EMS and control groups. The use of localized dynamic EMS on the abdominal musculature in populations with cardiorespiratory disorders could be of interest, and more research is needed. ### Abstract Dynamic electrostimulation consists of the application of local or global electrostimulation together with physical exercise. This study aimed to investigate the immediate effects of a dynamic electrostimulation session on the thickness of the abdominal musculature, inter-rectus distance, heart rate, blood pressure, and body temperature, and to identify possible differences in its form of application. A total of 120 healthy participants were divided into three groups: the whole-body electrostimulation group, the local electrostimulation group, and the control group without electrical stimulation. All groups performed a single session with the same dynamic exercise protocol. Muscle thickness and inter-rectus distance were evaluated ultrasonographically using the Rehabilitative Ultrasound Imaging technique both at rest and in muscle contraction (the active straight leg raise test) to find the post-intervention differences. The results showed significant differences in immediate post-intervention heart rate, with a smaller increase in the local electrostimulation group compared to the control and whole-body electrostimulation groups. No significant differences were identified between the groups after the interventions in the rest of the variables analyzed. Therefore, a local application, with the same effects as a global application on the abdominal musculature, has fewer contraindications, which makes its use more advisable, especially in populations with cardiorespiratory disorders, for which more research is needed. ## 1. Introduction Electrical muscle stimulation (EMS) consists of the application of electrical currents to produce a visible muscle contraction [1]. Muscle strength gain can be achieved through EMS or physical exercise, but, according to the evidence, better results are obtained when both are performed together [1]. The application of EMS in conjunction with physical exercise is called dynamic EMS. The application of dynamic EMS can be local or global [2]. Local EMS is applied to a specific body area, stimulating only the nerve or muscle cells in that area. The electrical current circulates locally between the positive and negative poles, located in the same muscle belly [3]. Less current is needed to cause a visible muscle contraction if the surface electrodes are placed at the motor point of the target muscle [4]. EMS can also be applied globally through whole-body electromyostimulation (WB-EMS), which allows a large muscular surface area (2800 cm2) [5] to be stimulated simultaneously, making it more functional training as it stimulates both agonists and antagonists [6]. The negative electrodes are placed on one half of the body and the positive electrodes on the other, with the electric current circulating between them and passing through the different tissues and organs of the body [3]. Both applications are well accepted even by untrained people, but they are not free of contraindications, especially when their application is global; in addition to the contraindications of EMS, there are systemic conditions (Diabetes mellitus, etc.) and health risks due to inappropriate use (rhabdomyolysis exertional) [7,8]. For this reason, the WB-EMS guide’s recommendations for its proper and safe use have been considered in this study [9]. Physical training with WB-EMS/EMS obtains similar results in muscle strength as conventional training but with a lower training intensity [10]. Therefore, this method can be considered an alternative [2] in populations that for various circumstances cannot perform a high-intensity physical exercise (advanced age, cardiovascular or respiratory pathologies, sarcopenia, sarcopenic obesity, menopausal women, etc.) [ 11] or for strengthening muscles that are more difficult to obtain maximum muscle contraction due to their biomechanical characteristics, such as the muscles of the anterolateral abdominal wall and especially of the deep musculature (Transversus Abdominis, TrA) [12]. The abdominal wall musculature (Internal Oblique (IO), External Oblique (EO), and rectus anterior (RA), TrA) plays a fundamental role in the stabilization of the pelvis and lumbar spine. Due to its anatomical peculiarities and functional complexity [13], more research is needed on this body region with the use of modern techniques, such as real-time ultrasound [14]. Rehabilitative Ultrasound Imaging (RUSI) is a non-invasive, objective, and validated technique to evaluate structural muscle changes and their behavior [15]. It has high reliability in the measurement of trunk muscles [16,17] and has been used in various investigations on the effects of EMS on the abdominal musculature [15,18,19,20,21]. The application of EMS to the abdominal wall, in addition to improving muscle strength, produces preferential stimulation of the central stabilizing musculature over the superficial musculature [19]. Indeed, patients who suffered from low back pain presented a loss of automatic control of deep musculature, as shown by TrA showing ultrasound thickness and electromyography muscle activity reductions during isometric leg tasks compared to healthy participants [22]. However, these benefits are not so clear in healthy populations [23,24]. There are several studies on the chronic application of dynamic WB-EMS, especially on the limb musculature and its effects on physiological parameters [2,6,25,26,27,28,29,30], while studies on local EMS are mostly not combined with physical exercise [31]. Few studies analyze the evolution of morphological and physiological changes during a period of training, and there may be differences depending on the type of muscles stimulated [32]. More research is needed on the effects of dynamic EMS [33]. The scientific literature is scarce on the immediate effects of a single WB-EMS/EMS session in healthy individuals, especially on the abdominal musculature, and an objectively assessing the immediate changes in its thickness using RUSI and its acute effects on physiological parameters such as heart rate. Likewise, we have detected a scarcity of studies evaluating the possible differences between local (local EMS) and global (WB-EMS) applications. According to Coghlan et al. [ 19], a single session of EMS may preferentially promote the stimulation of deep stabilizer abdominal muscle contraction, increasing muscle thickness by RUSI, and was claimed as a potential therapeutic intervention, although the immediate effects of WB-EMS over the abdominal musculature remain unknown. Thus, our alternative hypothesis was that the application of dynamic EMS would cause immediate changes in abdominal muscle morphology, or IRD, with respect to WB-EMS or control groups, as well as different changes in physiological parameters in both experimental groups compared to the control group. Nevertheless, the null hypothesis was that the application of WB-EMS or dynamic EMS would not cause immediate changes in abdominal muscle morphology, IRD, or physiological parameters compared to the control group. Lastly, the aim of this study was to identify and compare the acute effects of a dynamic exercise session with WB-EMS and local EMS on the thickness of the abdominal wall musculature (TrA, IO, EO, and RA) and the inter-rectus distance (IRD) in young, healthy individuals as evaluated by ultrasound. Additionally, as a secondary objective, observe the immediate effects on physiological parameters: heart rate (HR), blood pressure (BP), and body temperature. ## 2.1. Study Design A parallel-group, triple-blind, randomized controlled trial (participants, evaluator, and statistician) was performed between November 2021 and June 2022, following the CONSORT 2010 criteria [34] (the Consolidated Standards of Reporting Trials statement). The protocol for this study was prospectively registered on Clinical Trials.gov (ID: NCT05117203) and approved by the Ethics Committee of the Universidad de León (code: ETICA-ULE-009-2020). Ethical regulations, as well as the Helsinki Declaration of Helsinki [35], the Spanish Law for Protection of Data (Organic Law $\frac{3}{2018}$), and Biomedical Research in Human Participants ($\frac{14}{2007}$), were respected. The standard informed consent procedure was followed and signed by all subjects who agreed to participate in this study. ## 2.2. Participant Recruitment Students from the Faculty of Health Sciences of the Ponferrada Campus (Universidad de León, Ponferrada, León, Spain) were recruited by probability sampling through a publicity campaign by the investigators. The inclusion criteria for the study were: (a) healthy subjects of both sexes; (b) age range between 18 and 35 years old; (c) a good medical history with normal medical examinations and no previous history of cardiac disturbance; and (d) no surgeries in the previous year. The exclusion criteria were [8,9]: (a) body mass index (BMI) greater than 30 kg/m2; (b) elite or professional physical activity performance; (c) hyperventilation/hypercapnia and a score equal to or greater than 23 points on the Nijmegen questionnaire [36]; (d) women during their menstrual period; (e) habitual intake of medications; (f) abdominal surgeries (scars or keloids); (g) old or recent muscle injury at the abdominal level; (h) congenital diseases with musculoskeletal alterations at the level of the back and lower extremities, such as scoliosis, protrusion, or disc herniation; (i) presence of chronic low back, hip, or thigh pain; and (j) present any contraindication to WB-EMS/EMS [7]: pregnancy; viral or bacterial infections; arterial circulatory disorders, advanced arteriosclerosis; type I diabetes; hemophilia, bruising, hemorrhage; cognitive deficits; neuronal diseases, neuronal disorders, or epilepsy; recently performed operations in stimulation areas; abdominal wall and inguinal hernia; acute diseases, or inflammatory diseases, etc. ## 2.3. Sample Size Calculation Sample size calculation was carried out by the software GPower 3.1.9.2. using the F-test family for fixed effects, omnibus, and one-way analysis of variance (ANOVA). Based on a previous pilot study ($$n = 15$$) divided into 3 groups with 5 participants per group, using the mean of the difference (post-pre) of the thickness change during muscle activation (contraction-rest) in the TrA for EMS group (0.028 cm), the WB-EMS group (−0.006 cm), and the control group (0.018 cm) were calculated, as well as the standard deviation (SD) within each group (0.048). An effect size f of 0.294 was used for the sample size calculation in conjunction with an α error probability of 0.05, a power (1 – β error probability), and 3 for the number of groups, obtaining a necessary total sample size of 117 participants, divided into 39 subjects per group, in order to achieve an actual power of 0.809. ## 2.4. Randomization and Blinding Triple-blind study (participants, evaluator, and statistician). The intervention investigator performed randomization using sealed, opaque envelopes that determined the intervention group [37] and assigned a numerical code to each participant generated with computer software. The ultrasound evaluation investigator and the statistician received the blind-coded data from the study groups. Sham treatment participants were blinded to their membership in the control group. ## 2.5. Procedure The study began in November 2021 with an initial contact with each participant to record their demographic data, anthropometric measurements, and level of physical activity using the International Physical Activity Questionnaire (IPAQ-SF) [38] and the Nijmegen questionnaire [36], in addition to performing an anamnesis to verify the absence of any contraindication for the use of WB-EMS/EMS or any exclusion criteria for this study. Finally, the correct execution of the dynamic exercises and the Borg scale [39] were explained to them. A series of guidelines on the use of WB-EMS and anti-COVID-19 measures were recommended for an adequate and safe intervention. According to Figure 1, the pre-protocol data collection was performed after 10 min of resting in the sitting position before interventions assessing physiological parameters, the abdominal fold, and RUSI measurements [25,26]. Next, the interventions were performed by the same researcher who received previous training on WB-EMS/EMS [9,40], and they started in January 2022, in the afternoon hours, at the same location and under the same environmental conditions (22°–24° ambient temperature and 40–$60\%$ relative air humidity). Please see Section 2.6 below for detailed interventions. Finally, only one post-protocol collection time was applied after 1 min of resting in the sitting position after interventions for physiological parameters, abdominal fold, and RUSI measurements [25,26]. The RUSI protocol was carried out by the same researcher in charge of capturing the RUSI images and measurements, with experience in ultrasound evaluation of the abdominal musculature and prior training on the RUSI protocol, in a different room to the dynamic EMS intervention to ensure its blinding. Please see Figure 1 and Section 2.7 below for detailed ultrasound evaluations. ## 2.6. Interventions The intervention consisted of a single session of a dynamic exercise aimed at stimulating the contraction of the abdominal musculature combined with local or global EMS (Table 1), supervised by the responsible researcher, ensuring the safety of the participants [9,41]. The guide of recommendations for the appropriate and safe use of WB-EMS [9] was considered, applying an intervention protocol with safe electrical parameters for the participant based on scientific evidence [2]. The dynamic exercises were coordinated with the electric current by performing the gesture in the impulse phase and returning to the initial position in the rest phase [42]. Table 1* \| Session: 20’ \| WB-EMS/EMS \| Dynamic Exercise Protocol [41,43,45] \| \| --- \| --- \| --- \| \| WARM UP 5’ \| Bipolar rectangular current20 Hz—350 µsNo periods of stimulation/rest2/3 RPE \| Walking, running, and skipping \| \| MAIN PART 12’ \| Bipolar rectangular current85 Hz—350 µs4″ stimulation/4″ rest4/5/6 RPE \| 1 series × 8 repetitions: dynamic squats;2 series × 8 repetitions: dynamic side lunges (right–left);2 series × 8 repetitions: lateral trunk flexion;2 series × 8 repetitions: static forward lunges (right–left);1 series × 8 repetitions: fitball tilting;1 series × 8 repetitions: fitball front plank;2 series × 8 repetitions: dynamic crunches diagonally (right–left);2 series × 8 repetitions: side plank right and left. \| \| COOL DOWN 3’ \| Bipolar rectangular current5 Hz—150 µs1″ stimulation/1″ rest2/1 RPE \| Stretching \| WB-EMS group: Exercise intervention with WB-EMS (Justfit; https://justfitart.com accessed on 1 February 2023) with wireless sensors controlled by Bluetooth technology via a tablet. Nine muscle groups were activated simultaneously: arms, trapezius, dorsal, lumbar, gluteus, quadriceps, femoral, abdomen, and the electrodes of the pectoral area were placed on the sides of the abdominal wall. The intensity was adjusted individually for each muscle group. The participants wore cotton clothing previously moistened with water as well as electrodes [46]. EMS group: Exercise intervention with EMS in abdominal muscles (PhysiomedExpert, Physiomed Elektromedizin AG; a variable intensity maximum of 75 mA at 500 ohms of impedance and 230 volts at the maximum voltage peak). Rectangular adhesive electrodes (100 × 50 mm) were placed on the TrA and lateral wall (pencil electrode to locate skin areas of best response to electrical stimulation) [4]. Control Group: Exercise intervention with WB-EMS without electrical stimulation [45]. The same protocol as the WB-EMS Group was followed, with individual adjustment of the current intensity in each muscle group so that they would perceive the electric current (blinding belonging to the control group), lowering the intensity to zero at the beginning of the dynamic exercise session. To achieve the appropriate intensity of the application with WB-EMS/EMS dynamically [5], each participant’s rating of perceived exertion (RPE) using the modified Borg scale CR-10 [39] was used, with participants perceiving it as “somewhat strong” and “strong” in the main part of the session (4–5 on the Borg CR-10 Scale). Depending on the tolerance level of each participant, the intensity was increased every 3 min without exceeding level 6 to avoid possible negative consequences of high exertion intensity without prior adaptation to the WB-EMS [46]. In the warm-up phase, the intensity was $\frac{2}{3}$ RPE (from light to moderate), and in the cool-down phase, it was $\frac{2}{1}$ RPE (from light to very light). The intensity of the dynamic exercises in the control group, with the WB-EMS but without electrical stimulation, was also adjusted with RPE with the same parameters as dynamic EMS. ## 2.7. Ultrasound Protocol An ultrasound tool (Versana ActiveTM, General Electric; GE HealthCare, Madrid, Spain) with a linear probe using a trapezoidal preset to expand the scanning area and a 5–13 MHz range (12L-RS type) [40] was used to generate ultrasound images in B-mode; the imaging measurements were performed in the ultrasound tool’s software. The RUSI technique was used bilaterally to assess abdominal wall muscle thickness at rest and during muscle activation (contraction—rest thickness) [47,48]. The thickness of the abdominal wall musculature (TrA, IO, EO, and RA) was measured between the inner limits of each muscle at rest and during muscle activity (Figure 2 and Figure 3) (CCI between 0.62 and 0.99 for muscle thickness and between 0.48 and 0.78 for the comparison of the change in muscle thickness concerning the resting basal value) [49]. The IRD was measured between the inner limits of the medial borders of both RA at rest and during muscle activity (Figure 4); the CCI was between 0.74 and 0.90 [50]. The position of the participants was supine decubitus with a neutral position of the upper and lower extremities [51]. The probe was placed transversely to the abdominal wall [47] for RUSI assessments at rest and during muscle activity, without exerting pressure on the skin, and held in the same location and with the same pressure (only the weight of the probe itself) at each reference point (Figure 5). To assess abdominal wall muscle contraction, the active straight leg raising test (aSLRT) was used to activate the abdominal musculature (CCI of 0.65–0.69 for change in TrA thickness and 0.65–0.79 for OI) [51]. The participant was asked to actively raise the leg on the side to be assessed from the couch, with the knee straight, 30° of hip flexion (measured with a universal goniometer) [51], or 15 cm from the starting position. RUSI imaging was performed before and after each intervention. The evaluator explained the RUSI procedure to the participant beforehand and gave the following execution commands [18]: “prepare to lift”, “lift”, “hold for 10 s of lifting”, “prepare to lower”, and “lower” for the return to the starting position. At each set point, three ultrasound images were captured for reliability: during rest and aSLRT [18], at the end of unforced expiration [48], and with 30″ rests between each to minimize the influence of muscle fatigue [18]. The order of measurements was randomized before RUSI assessments to reduce potential measurement bias. ## 2.8. Outcome Measurements/Descriptive Data Outcome measures were recorded at baseline and at the end of the intervention. The outcome measurements were TrA muscle thickness (main outcome measurement), IO, and EO as the IRD, which were assessed at rest and during aSLRT to calculate their thickness changes (aSLRT-rest thickness difference), as well as HR, BP, and temperature (Visomat comfort $\frac{20}{40}$ sphygmomanometer; Uebe Medical GmbH, Zerbst, Germany; accuracy clinically validated by the European Society of Hypertension). The descriptive data were: age (years); sex (female/male); body weight (kg); body height (m); BMI (kg/m2) according to the Queletet method [52]; respiratory distress measured by the Nijmegen questionnaire (Spanish version) (specificity of 0.91 and sensitivity of 0.95 to detect the presence of hyperventilation or hypercapnia that can alter the function of the TrA) [36]; and level of physical activity measured by the International Physical Activity Questionnaire, short Spanish version (IPAQ-SF) (low, moderate, and high levels) (adequate reliability from 0.66 to 0.88) [38]. ## 2.9. Statistical Analyses Statistical analyses were carried out using the 22.0 version of the Social Sciences Statistical Package (SPSS) software. Normal distribution was assessed by the Kolmogorov–Smirnov test. Categorical data were described by frequency (n) and percentage (%), and their comparison was performed by the chi-square test. Quantitative data adjusted for normal distribution were described by mean ± standard deviation (SD) and completed with range (minimum–maximum), and their between-groups comparison was performed using one-way analysis of variance (ANOVA). Quantitative data adjusted for non-normal distribution were described by median ± interquartile range (IR) and completed with range (minimum–maximum), and their between-groups comparison was performed using the Kruskal–Wallis test. Effect size for F-tests was determined by the partial Eta squared coefficient (ηp2), interpreting ηp2 = 0.01 as a small effect size, ηp2 = 0.06 as a medium effect size, and ηp2 = 0.14 as a large effect size [53,54,55]. Post-hoc comparisons were performed using Bonferroni’s correction and adjusted p-values, as well as their effect sizes, which were calculated by Cohen’s d and categorized as very small effect sizes if d < 0.20, small effect sizes if $d = 0.20$–0.49, medium effect sizes if $d = 0.50$–0.79, and large effect sizes if d > 0.8 [56]. p-values < 0.05 were interpreted as statistically significant regarding a $95\%$ confidence interval (CI). In order to detail intra- and intergroup comparisons, a 2-way analysis of variance (ANOVA) was performed, including 3 groups and 2 measurement moments, considering repeated measurements across time (before and after interventions) as a within-subject factor as well as groups (EMS, WB-EMS, and control groups) as a between-group factor, and completed with linear graphs in order to detail comparisons for all outcome measurements, respectively [57]. Furthermore, the significance of these tests was considered by the Greenhouse–Geisser correction when the Mauchly tests rejected sphericity [58]. Indeed, Bonferroni’s corrections were applied to determine post-hoc comparisons. Again, effect sizes for F-tests were calculated by the partial Eta squared (ηp2) coefficients, as described previously [53,54,55]. Finally, multivariate regression analyses were performed to predict the outcome measurement differences (post-pre) after intervention based on baseline data to check if baseline differences or characteristics could influence our study results. Linear regression models were performed by the stepwise selection method, and the R2 coefficient was calculated to determine the adjustment quality [59]. Baseline data were selected as independent variables, including group (EMS = 1; WB-EMS = 2; and control = 3), sex (male = 1; female = 2), dominance (right = 1; left = 2), age (years), IPAQ (METs/min/week), sitting time (minutes), IPAQ level (sedentary = 1; moderate = 2; and vigorous = 3), Nijmegen score (points), weight (kg), height (m), BMI (kg/m2), and abdominal fold (mm). Outcome measurements differences (post-pre) after the intervention were selected as the dependent variables. Pre-established F-probabilities values from Pin = 0.05 to Pout = 0.10 were considered. ## 3.1. Flow Diagram All participants assessed for eligibility ($$n = 120$$) completed the study course and were randomized into the EMS ($$n = 40$$), WB-EMS ($$n = 40$$), and control ($$n = 40$$) groups. Seven participants were excluded for the following reasons: abdominal surgery in the last year ($$n = 1$$); recent muscle injury ($$n = 1$$); score >23 points on the Nijmegen questionnaire ($$n = 3$$); BMI > 30 kg/m2 ($$n = 1$$); and professional sports activity ($$n = 1$$) (Figure 6). ## 3.2. Baseline Measurements Descriptive data comparisons did not show statistically significant differences ($p \leq 0.05$) among the EMS, WB-EMS, and control groups for IPAQ score, Nijmegen score, height, BMI, sex, dominance, and IPAQ level (Table 2). Nevertheless, there were statistically significant differences for age distribution ($$p \leq 0.033$$), sitting time ($p \leq 0.001$), abdominal fold ($p \leq 0.001$), and dominance ($p \leq 0.001$). According to Bonferroni’s correction, post-hoc comparisons showed older age for the WB-EMS group with respect to the control group ($$p \leq 0.035$$), longer sitting time for the EMS group with respect to the WB-EMS ($p \leq 0.001$) and control ($$p \leq 0.001$$) groups, as well as greater abdominal folds for the EMS group with respect to the WB-EMS ($$p \leq 0.017$$) and control ($p \leq 0.001$) groups. In addition, the EMS groups showed a higher presence of left dominance with respect the WB-EMS and control groups. The rest of the post-hoc comparisons did not show statistically significant differences ($p \leq 0.05$). Comparisons for outcome measurements at baseline did not show any statistically significant difference (p ≥ 0.05) among the EMS, WB-EMS, and control groups for HR, SBP, DBP, and temperature, as well as bilaterally for TrA, RA, IO, and EO muscle thickness and IRD changes after interventions (Table 3). ## 3.3. Outcome Measurements Differences after Interventions Despite comparisons for outcome measurement differences after interventions showing no statistically significant differences ($p \leq 0.05$) among EMS, WB-EMS, and control groups for SBP, DBP, and temperature, as well as bilaterally for TrA, RA, IO, and EO muscles thickness and IRD changes after interventions (Table 4), there were between-groups statistically significant differences with a large overall effect size for HR differences after interventions ($p \leq 0.001$; F[2,117] = 30.874; ηp2 = 0.345). According to Bonferroni’s correction, post-hoc comparisons showed an HR increase with a large effect size for the WB-EMS (mean difference = 25.05 bpm; $p \leq 0.001$; $d = 1.53$) and control (mean difference = 19.92 bpm; $p \leq 0.001$; $d = 1.22$) groups with respect to the EMS group (Figure 7). ## 3.4. Two-Way ANOVA of Repeated Measurements for Intra- and Intergroup Comparisons The described findings were confirmed by the two-way ANOVA for repeated measurements to detail intra- and intergroup comparisons. Firstly, HR showed significant differences for time ($p \leq 0.001$; $F = 246.546$; ηp2 = 0.678) and timegroup interaction ($p \leq 0.001$; $F = 30.874$; ηp2 = 0.345). Post-hoc comparisons showed intragroup statistical differences ($p \leq 0.01$) for a HR increase in all groups after interventions and intergroup statistical differences with a large effect size ($p \leq 0.001$; $d = 1.22$–1.53) for a HR increase in both the WB-EMS and control groups with respect to the EMS group (Figure 8). Furthermore, the ANOVA for repeated measurements of SBP did not show significant differences for time ($$p \leq 0.246$$; $F = 1.362$; ηp2 = 0.012) or time × group interaction ($$p \leq 0.312$$; $F = 1.177$; ηp2 = 0.020). In addition, DBP did not present significant differences for time ($$p \leq 0.342$$; $F = 0.911$; ηp2 = 0.008) and timegroup interaction ($$p \leq 0.439$$; $F = 0.829$; ηp2 = 0.014). Next, an ANOVA for repeated measurements of temperature showed significant differences for time ($$p \leq 0.002$$; $F = 9.667$; ηp2 = 0.076), but not for time × group interaction ($$p \leq 0.668$$; $F = 0.406$; ηp2 = 0.007). Intragroup comparisons by Bonferroni’s corrections showed a significant reduction of the temperature after EMS ($$p \leq 0.028$$; $d = 0.21$) and control ($$p \leq 0.038$$; $d = 0.42$) interventions, but not after WB-EMS ($$p \leq 0.290$$; $d = 0.21$) interventions (Figure 9). In addition, IRD change presented significant differences for time ($p \leq 0.001$; $F = 32.877$; ηp2 = 0.219), although not for timegroup interaction ($$p \leq 0.651$$; $F = 0.430$; ηp2 = 0.007). Indeed, intragroup comparisons using Bonferroni’s correction displayed a significant reduction of the IRD change after all interventions, such as EMS ($$p \leq 0.012$$; $d = 0.37$; $d = 0.37$), WB-EMS ($p \leq 0.001$; $d = 0.63$), and control ($p \leq 0.001$; $d = 0.97$) groups (Figure 10). Regarding the rest of the RUSI measurements, there were not statistically significant differences for time considering the thickness changes of the right ($$p \leq 0.942$$; $F = 0.005$; ηp2 = 0.000) and left ($$p \leq 0.466$$; $F = 0.536$; ηp2 = 0.005). TrA, right ($$p \leq 0.618$$; $F = 0.250$; ηp2 = 0.002), and left ($$p \leq 0.566$$; $F = 0.331$; ηp2 = 0.003). IO, right ($$p \leq 0.363$$; $F = 0.835$; ηp2 = 0.007) and left ($$p \leq 0.081$$; $F = 3.093$; ηp2 = 0.026) EO, as well as the right ($$p \leq 0.975$$; $F = 0.001$; ηp2 = 0.000) and left ($$p \leq 0.971$$; $F = 0.001$; ηp2 = 0.000) RA. Likewise, there were not statistically significant differences for time × group interaction regarding thickness changes on the right ($$p \leq 0.891$$; $F = 0.115$; ηp2 = 0.002) and left ($$p \leq 0.415$$; $F = 0.885$; ηp2 = 0.015). TrA, right ($$p \leq 0.769$$; $F = 0.263$; ηp2 = 0.004) and left ($$p \leq 0.769$$; $F = 0.263$; ηp2 = 0.004) IO, right ($$p \leq 0.671$$; $F = 0.400$; ηp2 = 0.007) and left ($$p \leq 0.792$$; $F = 0.234$; ηp2 = 0.004) EO, and right ($$p \leq 0.190$$; $F = 1.683$; ηp2 = 0.028) and left ($$p \leq 0.322$$; $F = 1.145$; ηp2 = 0.019) RA. ## 3.5. Multivariate Linear Regression Models Multivariate regression analyses did not display any valid regression model to predict the outcome measurement differences after interventions for HR, SBP, and DBP, as well as IRD and thickness changes of the left TrA and bilaterally the IO and EO muscles. Nevertheless, a linear regression model ($$p \leq 0.014$$; F[1,118] = 6.275; R2 = 0.050; β = +0.003) showed that a greater right TrA thickness change difference after intervention was predicted by a higher Nijmegen test score. In addition, a linear regression model showed that a lower right RA thickness change difference after intervention ($$p \leq 0.008$$; F[1,118] = 7.238; R2 = 0.058; β = −0.150) was predicted by lower height of participants, as well as another linear regression model (R2 = 0.123), which determined that a lower left RA thickness change difference after intervention was predicted by lower height ($$p \leq 0.005$$; F[1,118] = 8.330; R2 = 0.066; β = −0.271) and Nijmegen scores ($$p \leq 0.007$$; F[1,118] = 9.606; R2 = 0.057; β = −0.003). Finally, a linear regression model ($$p \leq 0.002$$; F[1,118] = 6.275; R2 = 0.075; β = +0.262) displayed that a higher temperature difference was predicted by female sex. Thus, statistically significant between-group differences were shown at baseline (Table 2), such as age, sitting time, abdominal fold, and dominance, which did not influence nor predict the outcome measurement differences. ## 4. Discussion The main objective of this study was to identify the effects of a single session of dynamic exercises with WB-EMS or EMS on the thickness of the abdominal wall musculature. The results did not show significant changes in the morphology of the deep (TrA and IO) and superficial (RA and EO) abdominal musculature compared to the control group; therefore, the hypothesis raised in this research is confirmed. To our knowledge, this is the first study to investigate the immediate effects of a session of local EMS and WB-EMS on the abdominal muscles in healthy young people, assessed using RUSI. No significant differences were obtained in the abdominal muscle thickness of the WB-EMS/EMS interventions versus the control group with WB-EMS deactivated, despite several studies suggesting that the application of EMS can lead to greater muscle metabolic stress (oxygen consumption (VO2), lactate and hormone levels, and delayed onset muscle soreness (DOMS)) [23,28] than voluntary muscle contraction by causing greater muscle fiber recruitment (synchronous) [11], leading to higher acute energy expenditure during exercise compared to exercise alone [60] and a higher level of muscle fatigue, especially with global application with WB-EMS, as it stimulates a larger body surface area than local EMS [61]. The acute effect of increased muscle thickness observed in other studies [62] may be due to an increase in muscle protein synthesis resulting in “muscle swelling” or to an acute inflammatory response induced by exercise, immediately after the first training session [62] and especially with the use of EMS currents. This acute inflammatory response depends on the volume, intensity, type of exercise, and level of fatigue [63], which is an indirect marker of muscle damage. High-intensity or high-volume endurance or strength training leads to greater acute responses in muscle thickness and greater changes in muscle morphology, especially due to high volume [64], which is not the case with our protocol. The acute muscle response visible with ultrasound has been similar in the three interventions, suggesting that the dynamic exercise protocol [62,64] and the electrical current parameters of this study are safe, as in previous studies that used those parameters with different types of populations [3] and recorded no adverse reactions [65]. The sample in this study was healthy; no differences were obtained in the acute effects of the addition of WB-EMS/EMS to the physical exercise protocol, as in other studies with healthy populations that did not find a greater benefit with EMS training [9,29,30]. In contrast, in populations with certain pathologies (low back pain and abdominal rectus diastasis), better results were obtained on the abdominal musculature in terms of muscle mass gain, muscle strength, and improved abdominal muscle recruitment with the combination of EMS and physical exercise [20,23,66]. Based on our findings, a single session of dynamic WB-EMS/EMS did not generate a sufficient stimulus to produce a visible muscle response in the abdominal musculature. Despite the parameters used (bipolar rectangular, 85 Hz, 350 µsg, and 4″ of impulse and 4″ of rest) being sufficient to develop a high voluntary muscle contraction [41,67], they did activate the necessary muscle responses leading to strength adaptations [67]. Previous studies [32,68] that examined the temporal evolution of muscular changes at a morphological level during a training period found that in the first sessions, muscular responses are produced at a molecular level, stimulating processes of myofibrillar protein synthesis [68], which are not detectable with the ultrasound measuring instrument used in this study. These muscular responses added over time give rise to morphological changes at least, with 4 weeks of acute sessions of strength exercise with EMS [68] becoming evident in the increase in muscle thickness within the first 6 weeks [20]. Long-term EMS produces muscle hypertrophy, with 8 weeks of strength or resistance training being necessary [30,66,69], with adaptations in muscle mass and architecture occurring between the 4th and 8th weeks [32]. Furthermore, no changes were seen in the thickness of the abdominal musculature according to the dominant or non-dominant side or in the muscle response to EMS as a function of abdominal crease thickness. Other studies in healthy individuals also did not observe differences in thickness in abdominal musculature on both sides, both at rest and during muscle contraction [14,21]. In terms of physiological parameters, no significant immediate effects were obtained for BP or body temperature with any of the three interventions compared to their baseline values before the session. On the other hand, a different behavior was observed in the HR after the application of local EMS compared to the other groups. The HR measured immediately at the end of the intervention increased in all three groups compared to the baseline HR, but its increase with local EMS was significantly lower than with the application of WB-EMS (+25 beats/minute compared to EMS) or with exercise alone (+19 beats/minute compared to EMS). Electrical stimulation can modulate sympathetic and parasympathetic activity [70]. There are few studies evaluating acute HR modifications due to the application of dynamic WB-EMS/EMS in a single session; as in our results, they observed that the increase in HR immediately following dynamic exercise with WB-EMS was greater in both groups (obese and healthy) than with exercise alone, but the differences were not statistically significant. They concluded that WB-EMS did not alter cardiac autonomic modulation in the obese young population [25,26] or in the healthy [25,27]. The results obtained from the cardiac parameters analyzed suggest that it is a safe procedure, coinciding with the study of Jee [2018] [45], in terms of cardiopulmonary factors in healthy people with similar characteristics to our sample. Post-intervention HR was higher with WB-EMS; this is because this device allows electrical stimulation of a larger body surface area than local EMS, stimulating several muscle groups simultaneously greater than the abdominal area, resulting in greater metabolic responses (lactate concentration), a higher level of muscle fatigue, and greater perceived exertion by the participant than local application or exercise alone [61]. The reasons why EMS reduces HR in terms of control may have different explanations. It could be because local EMS applied in the abdominal area enhances parasympathetic modulation of HR by an increase in its activity or by a decrease in sympathetic activity. Electrical stimulation may have produced a mild effect on vascular reflexes involving the autonomic nervous system, causing a decrease in efferent sympathetic impulses and reducing sympathetic activity; it may have stimulated arterial baroreceptors, producing inhibition of sympathetic activity [71]; or it may have produced a lower perception of work effort due to modifications in breathing patterns, by direct stimulation of this musculature that supports respiratory function, resulting in different cardiovascular effects than the other interventions at the same work intensity (the placement of electrodes in local EMS is more precise, allowing motor point stimulation of the deep abdominal musculature to be more comfortable to tolerate and maximizing spatial recruitment of motor units [4]; whereas WB-EMS stimulation is non-specific and can produce simultaneous direct stimulation of somatosensory afferent nerve fascicles that influence the participant’s perception of pain and exertion) [4,72] or by a different functioning of the muscle metaboreflex that increases parasympathetic tone causing a lower increase in HR [73]. A greater number of studies with a more exhaustive assessment of cardiorespiratory and biochemical parameters (O2 uptake, lactate, and phosphocreatine accumulation) would be necessary to provide more information and could offer more plausible explanations. It is necessary to recognize the limitations of this study, among them the use of a single session of dynamic EMS, sufficient to determine its immediate effects, although it would be interesting to know its effects with an intervention of several sessions; the cross-group study design when investigating the acute effect of the intervention; the use of electromyography to measure muscle activation in addition to the evaluations of the changes in muscle thickness carried out with RUSI and the calculation of the percentage difference in muscle thickness; the inclusion of an intervention group with WB-EMS/EMS (without exercise), although the three intervention groups performed an identical exercise protocol, to determine their effects on muscle structure and physiological parameters versus physical exercise; and a WB-EMS/EMS without abdominal stimulation to observe post-exercise HR behavior. As strong points, the obtaining of a representative sample with a high number of ultrasound measurements and the comparison of the possible differences between the application of EMS or WB-EMS in the abdominal musculature. ## 5. Conclusions The application of a single session of electrostimulation (local or global) does not produce immediate acute changes in the thickness of bilateral abdominal muscles or in the inter-rectus distance (analyzed with the RUSI technique), which seems to indicate that the dynamic EMS protocol of this study does not produce acute inflammatory effects in these structures. In addition, a single EMS session (local or global) does not produce statistically significant pre- and post-intervention changes in the physiological variables of body temperature and systemic blood pressure. In contrast, there were significant differences between the groups analyzed in terms of HR after the interventions. 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--- title: Racial Differences in Androgen Receptor (AR) and AR Splice Variants (AR-SVs) Expression in Treatment-Naïve Androgen-Dependent Prostate Cancer authors: - Farhan Khan - Obianuju Mercy Anelo - Qandeel Sadiq - Wendy Effah - Gary Price - Daniel L. Johnson - Suriyan Ponnusamy - Brandy Grimes - Michelle L. Morrison - Jay H. Fowke - D. Neil Hayes - Ramesh Narayanan journal: Biomedicines year: 2023 pmcid: PMC10044992 doi: 10.3390/biomedicines11030648 license: CC BY 4.0 --- # Racial Differences in Androgen Receptor (AR) and AR Splice Variants (AR-SVs) Expression in Treatment-Naïve Androgen-Dependent Prostate Cancer ## Abstract Androgen receptor splice variants (AR-SVs) contribute to the aggressive growth of castration-resistant prostate cancer (CRPC). AR-SVs, including AR-V7, are expressed in ~$30\%$ of CRPC, but minimally in treatment-naïve primary prostate cancer (PCa). Compared to Caucasian American (CA) men, African American (AA) men are more likely to be diagnosed with aggressive/potentially lethal PCa and have shorter disease-free survival. Expression of a truncated AR in an aggressively growing patient-derived xenograft developed with a primary PCa specimen from an AA patient led us to hypothesize that the expression of AR-SVs could be an indicator of aggressive growth both in PCa progression and at the CRPC stage in AA men. Tissue microarrays (TMAs) were created from formalin-fixed paraffin-embedded (FFPE) prostatectomy tumor blocks from 118 AA and 115 CA treatment-naïve PCa patients. TMAs were stained with AR-V7-speicifc antibody and with antibodies binding to the N-terminus domain (NTD) and ligand-binding domain (LBD) of the AR. Since over 20 AR-SVs have been identified, and most AR-SVs do not as yet have a specific antibody, we considered a 2.0-fold or greater difference in the NTD vs. LBD staining as indication of potential AR-SV expression. Two AA, but no CA, patient tumors stained positively for AR-V7. AR staining with NTD and LBD antibodies was robust in most patients, with $21\%$ of patients staining at least 2-fold more for NTD than LBD, indicating that AR-SVs other than AR-V7 are expressed in primary treatment-naïve PCa. About $24\%$ of the patients were AR-negative, and race differences in AR expression were not statistically significant. These results indicate that AR-SVs are not restricted to CRPC, but also are expressed in primary PCa at higher rate than previously reported. Future investigation of the relative expression of NTD vs. LBD AR-SVs could guide the use of newly developed treatments targeting the NTD earlier in the treatment paradigm. ## 1. Introduction Approximately 270,000 men in the United States were diagnosed with prostate cancer (PCa) and 34,000 died of PCa in 2022 [1]. Globally, the number of PCa survivors is projected to increase to over 4.5 million by 2026 [2]. Current therapeutic strategies for castration-resistant prostate cancer (CRPC) include androgen receptor (AR) antagonists and a CYP17A1 inhibitor [3,4,5]. Although these drugs extend progression-free survival (PFS), approximately $30\%$ of tumors do not respond to these therapies, and patients who initially respond to these therapies develop resistance shortly after treatment initiation [6]. One of the primary reasons for treatment failure and CRPC relapse is the expression of AR splice variants (AR-SVs) that lack the ligand binding domain (LBD) and are constitutively active [7,8,9]. AR-SVs contribute to CRPC aggressive phenotype, shorter PFS, and failure to respond to enzalutamide or abiraterone [7,10,11,12,13,14]. With a subset of primary PCa not responding to treatment and the reminder developing treatment resistance over time, AR-SV expression is a potential escape mechanism to androgen independency, treatment resistance, and disease progression [15]. Studies have shown that AR-SVs are expressed in CRPC, but minimally in primary PCa [16,17,18]. Several studies with CRPC specimens and preclinical models identified multiple AR-SVs (Figure 1A) and found that the AR-SVs contribute to cancer relapse after radical prostatectomy [16]. Approximately 20 AR-SVs have been identified preclinically and clinically [10]. Drugs including enzalutamide and abiraterone target the androgen pathway to inhibit PCa progression. However, the treatments fail after an initial period of response, and AR-SVs contribute to the treatment failure [6,19]. For example, AR-V7, a commonly identified AR-SV, in clinical specimens correlated with a unique 59 gene signature in CRPC that corresponded to shorter survival times and resistance to treatments [19]. However, there are many known AR-SVs, and expression levels vary from 30 to $70\%$ in CRPC patients [6,13,19]. Advanced CRPC patients treated with enzalutamide and abiraterone expressed AR-V7 at $39\%$ and $19\%$, respectively, and AR-V7-positive patients had statistically significant lower PSA response rates [6]. Several mechanisms including gene rearrangement and alternate splicing through splicing factors have been attributed to the development of AR-SVs [9,20,21]. Since the AR NTD is the main coactivator interacting surface, the expression of this region in the AR-SVs makes AR-SVs constitutively active and allows them to retain the majority of their activity. Clinically, AR-V7 is detected in both the prostate tissue and in the circulating tumor cells using both at the transcript and protein levels [22]. AR-V7 expression varies widely in hormone-sensitive PCa between studies, ranging from low single-digit percents to over $90\%$ [19,23]. To our knowledge, it is currently unknown whether AR-SV expression levels or patterns differ with race, and no large patient cohorts have been analyzed for a comprehensive expression of AR-SVs in treatment-naïve primary PCa. Compared to Caucasian American men (CA), African American (AA) men have a $63\%$ higher overall PCa incidence. These patients are more likely to be diagnosed with aggressive PCa [24], are 2.44-fold more likely to die from PCa [25], and have shorter disease-free survival [26]. PSA levels remain higher in healthy AA men compared to CA men even after adjusting for age, BMI, and insurance [27,28]. High-grade prostatic intraepithelial neoplasia is more prevalent in younger AA men than age-matched CA men, suggesting an accelerated progression prior to diagnosis [29,30,31]. A previous analysis of men undergoing prostate biopsy that adjusted for clinical and demographic differences found that AA men were $50\%$ more likely to be diagnosed with PCa and $84\%$ more likely to have high-grade PCa than CA men [30]. Among men undergoing radical prostatectomy, AA men were $28\%$ more likely to have a recurrence than CAs [32]. While differential healthcare access may contribute to racial disparities in PCa detection and treatment patterns, when compared to CAs, AA men have higher PSA levels, are diagnosed at a higher grade, show higher tumor expression of adverse molecular markers, and have a higher risk of PCa progression after surgery. Further characterizing AR-SVs in diverse clinical populations could provide new treatment approaches. In the last decade, several groups have identified molecules that bind to the N-terminus domain (NTD) and inhibit and/or degrade AR and AR-SVs [33,34,35,36]. Considering that the NTD is conserved, and all AR-SVs express the NTD, targeting the NTD would be more effective than a receptor–ligand approach through targeting the LBD. Evaluating the expression of AR-SVs in primary PCa in AA and CA men will determine whether the expression of AR-SVs contributes to development of aggressive PCa or CRPC in AA patients. In this case, targeted clinical assays would be developed, such that existing or new drugs may be administered earlier in the treatment paradigm to improve patient outcomes and prognosis. In this study, we evaluated the expression of AR and AR-SVs in treatment-naïve primary PCa from AA and CA men in the mid-south USA. Only two primary prostatectomy PCa tumors stained for AR-V7, both in AA men. However, $20\%$ of the patient specimens stained for NTD at a rate 2.0-fold higher than LBD, indicating AR-SVs other than AR-V7 may serve as potential targets in primary PCa. ## 2. Materials and Methods AR NTD antibody was procured from Millipore, Burlington, MA, USA (06-680-MI), AR-LBD antibody C19 was from Sigma, St. Louis, MO, USA (SAB5500007), and AR-V7 antibody was obtained from Abcam, Cambridge, UK (ab198394). ## 2.1. TMA Creation and Staining Patient specimens were collected under an UTHSC Institutional Review Board (IRB)-approved protocol. Patient details were redacted before the clinical information was released to the researchers. The patient specimens that were collected and stored since 1990 were obtained from the UTHSC center for cancer research biorepository core. Each patient specimen had multiple blocks, and sections were made from each block and sent to the pathologist (FK) for marking the tumor areas. Cores from identified sections were obtained from each block and TMAs were created with a total of 233 specimens (each TMA containing 30 specimens). Twelve specimens showed insufficient tumors and were excluded from the study. Staining was optimized using LNCaP and 22RV1 cells that expressed AR and AR and AR-SV, respectively, with selected PCa slides. Immunohistochemistry protocol was optimized using an automated processor (Leica, Bond III). Cases ($$n = 221$$) were evaluated for staining with AR NTD and LBD antibodies and 192 cases for AR-V7-binding antibody. We used three AR NTD antibodies (Cell Signaling D6F11) and AR-441 (in addition to PG-21) to optimize staining and chose to use PG-21 antibody for the staining of TMAs. Similarly, the AR-V7 Abcam antibody was compared with RevmAb AR-V7 antibody and chose to continue with the Abcam antibody. Results were scored by two independent pathologists for intensity of staining (0–3) and the number of cells (0–100), and H score was calculated ranging between 0 and 300. ## 2.2. Statistical Analysis Fisher’s exact test was used for an analysis of contingency between the African American and Caucasian population with possible splice variants. A similar test was performed on the samples with no staining present. Finally, a Welsh’s t test was performed on all samples comparing the fold change in NTD vs. LBD staining. ## 2.3. Patient-Derived Xenograft Animal experiments were performed under an UTHSC Institutional Animal Care and Use Committee (IACUC)-approved protocol. Animals were maintained in a 12 h light:dark cycle and were provided with water and food ad libitum. Patient specimens collected from surgical suites in RPMI medium supplemented with penicillin:streptomycin and fungizone were fragmented using collagenase and implanted subcutaneously in male NSG mice. ## 2.4. Western Blot Tumor tissue from PDX was minced and protein extracted by three freeze thaw cycles in a lysis buffer that contained protease and phosphatase inhibitors. Western blot was performed according to a method previous published [36]. ## 3. Results Since AR-V7 is the only AR-SV with a clinically validated assay, there is a higher chance that the expression of other AR-SVs could be overlooked. Hence, the actual percent of AR-SVs expressed in primary PCa or in CRPC is underestimated. We expected that the approach shown in Figure 1B could provide clarity on the percent of PCa expressing AR-SVs. Two antibodies, one binding to the NTD and one to the LBD, were optimized using LNCaP and 22RV1 cells. LNCaP cell line expresses AR, while 22RV1 expresses AR and AR-SV. LNCaP produced a ratio of 1:1 with the antibodies, while 22RV1 provided a ratio of greater than 2, suggesting that this approach can distinguish between specimens expressing AR only or AR and AR-SVs. We used a cut-off of 2.0 to classify a specimen as AR-positive or AR- and AR-SV-positive. This is similar to the approach adopted earlier [37]. ## 3.1. Patient Characteristics and Demographics A prostatectomy PCa specimen (UT-1335) from an AA patient aggressively grew from implantation to 2000 mm [3] in under 40 days. Protein was extracted from the specimen and Western blot for AR was performed with an antibody directed to the NTD of AR. Interestingly, the antibody detected a truncated band at ~65 KDa (Figure 2). This led to the assumption that primary treatment-naïve PCa from AA patients could potentially express AR-SVs, and that could potentially contribute to aggressive phenotype. To address this hypothesis, we created nine tissue microarrays (TMA) from 233 treatment-naïve prostatectomy specimens from AA [118] and CA [115] patients. The patient characteristics and demographics are provided in Table 1. Serum PSA and Gleason scores were matched between the races to ensure that the tumor stage and grade do not contribute to differences in the expression of AR-SVs. The TMAs were stained with AR NTD and LBD-binding antibodies, and with an AR-V7 antibody. The staining intensity was quantified, and an H-score was provided for each specimen. ## 3.2. Treatment-Naïve Primary PCa Specimens Express AR-SVs Out of the 221 evaluable specimens, 53 specimens ($24\%$) did not stain for AR with either antibody. This suggests that these patients may not respond to any AR-targeted therapeutics and could potentially develop AR-negative neuroendocrine PCa. No racial differences were observed (27 vs. 26) (Figure 3). The pathological grade of AR-negative specimens was T2C or higher, with a Gleason score of 7 or higher. Only two patients, both AA men, stained positive for AR-V7. There were 47 patients with a rate of NTD staining 2-fold greater than that of LBD staining, corresponding to $21\%$ of PCa specimens potentially expressing AR-SVs. This included 17 from AA men and 30 from CA men. No statistical significance between the races was identified. Representative IHC images are shown in Figure 4. Specimens ($$n = 6$$; 3 AA + 3 CA) were randomly selected, RNA was extracted, and real-time PCR was performed with TaqMan probes binding to the NTD and LBD. The results at the mRNA level confirmed the observation made with IHC. ## 4. Discussion While most studies are performed to detect the AR-SVs focus on CRPCs, we focused on primary PCa with the assumption that expression of AR-SVs at an early stage might correlate with late-stage aggressive cancer. This study is potentially one of a few to take a distinct approach to identify AR-SVs in prostatectomy specimens with a ratio of N-terminus to C-terminus staining as a defining measure of AR-SV expression. An earlier study utilizing the same approach did not identify AR-SVs in primary PCa [37]. This approach in our patient cohort identified ~$20\%$ of primary PCa specimens expressing AR-SVs, including two AA patients with AR-V7 expression. AR-SVs could potentially be expressed in PCa specimens at higher rate than previously known. Considering that 20 AR-SVs [38] have been clinically identified, the proportion of AR-SVs in primary PCa and CRPC could be underestimated. AR-V7 was detected in less than $1\%$ of primary PCa specimens, while it was detected in over $75\%$ of PCa specimens where the patients underwent androgen deprivation therapy (ADT) [19]. This number further increased in patients treated with abiraterone acetate or enzalutamide. Not many studies have comprehensively evaluated the expression of AR-SVs in treatment-naïve primary PCa specimens. At this time, more than 20 AR-SVs have been detected clinically and preclinically [38]; however, AR-V7 is the only AR-SV that can be reliably measured clinically. Since the NTD is highly conserved in almost all of the AR-SVs, our approach might provide a method to determine if there are AR-SVs expressed in PCa and CRPC specimens. Detecting the overall expression of AR-SVs in PCa specimens will help with the choice of treatment and will also provide an explanation for the failure to respond to treatments. With the advent of new AR NTD-targeting treatment approaches [33,34,35,36] comes the ability to detect AR-SVs early in the PCa occurrence, and using this approach suggests that these new AR NTD-targeting drugs might be beneficial to a broader spectrum of PCa patients. The IHC results were confirmed using real-time PCR with the limited availability of the specimens. Unfortunately, the specimens used in this manuscript are very old and enough tissues are not available to perform Western blot analysis. Though we expected a racial disparity in the expression of AR-SVs, with AA patients expressing at a higher rate than the CA patients, we did not find any statistical difference between the two races in terms of AR-SV expression. Interestingly, the CA patients had a higher proportion of AR-SV-positive tumors. An earlier study with a small number of ($$n = 10$$) hormone-responsive bone metastasis specimens was performed to detect AR, AR-V7, AR-V1, and AR-v567es at mRNA levels. The study found that mRNA was detected in most of the primary tumors and metastasis, and this number increased in CRPC patient specimens [10]. Though that study evaluated the expression at the mRNA level, this study provides an independent validation for the expression of AR-SVs in early-stage PCa. Strengths of the analysis include large number of AA and CA specimens with comparable ranges of age, PSA, and Gleason scores. The assumption that higher NTD staining relative to LBD was indicative of AR-SV expression was based on in vitro analyses with LNCaP and 22RV1 cell staining. However, there are limitations, including the potential for the masking of the antibody epitope in specimens that stained weakly at the LBD, hence providing a false positive interpretation that AR-SVs are expressed in primary PCa. Our results need to be confirmed using one of the sequencing methods such as probe-capture sequencing. Irrespective of these limitations, the study provides evidence that the expression of AR-SVs is not limited to CRPC, as previously thought, but could be expressed in primary PCa at a much higher rate and that the expression of AR-SVs in primary PCa might be underestimated clinically. ## References 1. Siegel R.L., Miller K.D., Fuchs H.E., Jemal A.. Cancer statistics, 2022. 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--- title: Fatty Acid Binding Proteins 3 and 4 Predict Both All-Cause and Cardiovascular Mortality in Subjects with Chronic Heart Failure and Type 2 Diabetes Mellitus authors: - Ricardo Rodríguez-Calvo - Minerva Granado-Casas - Alejandra Pérez-Montes de Oca - María Teresa Julian - Mar Domingo - Pau Codina - Evelyn Santiago-Vacas - Germán Cediel - Josep Julve - Joana Rossell - Lluís Masana - Didac Mauricio - Josep Lupón - Antoni Bayes-Genis - Núria Alonso journal: Antioxidants year: 2023 pmcid: PMC10044995 doi: 10.3390/antiox12030645 license: CC BY 4.0 --- # Fatty Acid Binding Proteins 3 and 4 Predict Both All-Cause and Cardiovascular Mortality in Subjects with Chronic Heart Failure and Type 2 Diabetes Mellitus ## Abstract Subjects with type 2 diabetes mellitus (T2D) are at increased risk for heart failure (HF). The cardiac-specific (FABP3) and adipose-tissue-specific (FABP4) types of the fatty acid binding proteins have been associated with both all-cause and cardiovascular (CV) mortality. The aim of this study was to explore the prognosis value of FABP3 and FABP4 in ambulatory subjects with chronic HF (CHF), with and without T2D. A prospective study involving 240 ambulatory CHF subjects was performed. Patients were followed-up for a mean of 5.78 ± 3.30 years and cause of death (if any) was recorded. Primary endpoints were defined as all-cause and CV death, and a composite endpoint that included CV death or hospitalization for HF was included as a secondary endpoint. Baseline serum samples were obtained and the serum FABP3 and FABP4 concentrations were assessed by sandwich enzyme-linked immunosorbent assay. Survival analysis was performed with multivariable Cox regressions, using Fine and Gray competing risks models when needed, to explore the prognostic value of FABP3 and FABP4 concentrations, adjusting for potential confounders. Type 2 diabetes mellitus was highly prevalent, accounting for $47.5\%$ for total subjects with CHF. Subjects with T2D showed higher mortality rates (T2D: $69.30\%$; non-T2D: $50.79\%$, $$p \leq 0.004$$) and higher serum FABP3 (1829.3 (1104.9–3440.5) pg/mL vs. 1396.05 (820.3–2362.16) pg/mL, $$p \leq 0.007$$) and FABP4 (45.5 (27.6–79.8) ng/mL vs. 34.1 (24.09–55.3) ng/mL, $$p \leq 0.006$$) concentrations compared with non-T2D CHF subjects. In the whole study cohort, FABP3 was independently associated with all-cause death, and both FABP3 and FABP4 concentrations were associated with CV mortality. The predictive values of these two molecules for all-cause (FABP3: HR 1.25, $95\%$ CI 1.09–1.44; $$p \leq 0.002.$$ FABP4: HR 2.21, $95\%$ CI 1.12–4.36; $$p \leq 0.023$$) and CV mortality (FABP3: HR 1.28, $95\%$ CI 1.09–1.50; $$p \leq 0.002.$$ FABP4: HR 4.19, $95\%$ CI 2.21–7.95; $p \leq 0.001$) were only statistically significant in the subgroup of subjects with T2D. Notably, FABP4 (HR 2.07, $95\%$ CI 1.11–3.87; $$p \leq 0.022$$), but not FABP3, also predicted the occurrence of the composite endpoint (death or hospitalization for HF) only in subjects with T2D. All these associations were not found in CHF subjects without T2D. Our findings support the usefulness of serum FABP3 and FABP4 concentrations as independent predictors for the occurrence of all-cause and CV mortality in ambulatory subjects with CHF with T2D. ## 1. Introduction Increasing evidence has shown a greater risk for heart failure (HF) associated with the presence of type 2 diabetes mellitus (T2D) [1]. Indeed, HF is one of the main cardio-vascular (CV) manifestations reported in subjects with T2D [2]. Despite this, the prognosis of subjects with HF and T2D is elusive [3]. In this regard, the enhanced mortality risk in these subjects cannot be fully explained by established risk factors [4,5,6,7]. Therefore, stratification of the mortality risk related to HF remains a challenge for these subjects and additional HF biomarkers among subjects with T2D should be considered. Metabolic disturbances, including impaired glucose and fatty acid metabolism, have been related to increased risk for HF independently of coronary artery disease [8]. Enhanced oxidative stress, mitochondrial dysfunction and cardiomyocyte apoptosis are among the main molecular mechanisms underlying myocardial dysfunction [9]. Accumulating evidence suggests a role for serum circulating molecules that may behave as sensors of metabolic alterations and might directly contribute to increased risk of HF in subjects with T2D. In this context, several members of the Fatty Acid Binding Protein (FABP) family have been linked to metabolic diseases related to cardiac disorders [10]. Members of this family are intracellular lipid transporters that take part in the intracellular regulation of lipid trafficking and their responses. Specifically, the cardiac-specific fatty acid binding protein (FABP3) has been related to the control of cardiac insulin resistance [11] and fatty acid uptake [12]. Another form of FABP, adipose-tissue-specific (FABP4), exhibits cardio-depressant effects [13] and participates in the trans-endothelial transport of nutrients to the cardiomyocyte [14], directly impacting insulin signaling in cardiac cells [15]. Both FABP3 and FABP4 have been described as circulating biomarkers of several cardiac and metabolic disturbances. FABP3 is rapidly released into the bloodstream after acute myocardial injury [16,17,18]. FABP3 elevations have also been related to different cardiac pathologies, including several cardiomyopathies, acute coronary syndrome (ACS) and HF [19] and proposed as a silent biomarker for the progression of myocardial damage in subjects with insulin resistance [20]. On the other hand, FABP4 has also been related to HF and CV disease [10,15,21,22,23,24,25,26,27,28]. Specifically, serum FABP4 concentrations correlate positively with the HF biomarker N-terminal fragment of pro-B-type natriuretic peptide (NT-proBNP), this association being even stronger in subjects with diabetes and HF [23]. Recently, FABP4 has also been related to ectopic fat accumulation in the heart [15], one of the main precursors of myocardial dysfunction due to diabetes [29,30,31,32,33]. Both FABP3 and FABP4 have been linked to oxidative stress. For instance, circulating FABP3 has been positively related to oxidative stress biomarkers, including malondialdehyde (MDA) and asymmetric dimethylarginine (ADMA), and inversely associated with the total antioxidant capacity (TAC) in patients with carbon-monoxide-induced cardiotoxicity [34]. On the other hand, experimental studies performed in FABP4-deficient mice showed a decline in oxidative stress during myocardial ischemia/reperfusion (MI/R) injury and diabetes-induced cardiac dysfunction, as revealed by concomitant activation of the endothelial nitric oxide synthase/nitric oxide (eNOS/NO) pathway and reduced superoxide anion production [35]. Therefore, both FABP3 and FABP4 may directly impact the disease progression through oxidative stress regulation. An increasing body of evidence supports the notion that both FABP3 and FABP4 serum concentrations can predict both all-cause [36,37,38,39,40,41,42] and CV mortality [40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58]; however, the potential role of these FABPs as predictive biomarkers for the mortality risk among subjects with T2D and chronic HF (CHF) has not been explored yet. Thus, the aim of this study was to assess the prognostic value of these two FABPs (i.e., FABP3 and FABP4) for both all-cause and CV mortality in outpatient CHF subjects with T2D. ## 2.1. Study Population The current research was performed in a subset of a well-characterized ambulatory cohort of subjects with CHF, prospectively admitted in a structured ambulatory multidisciplinary HF unit [59,60]. Specifically, samples from 240 outpatients with CHF referred to the HF unit were included in the study. Heart failure was diagnosed according to the European Society of Cardiology guidelines regardless of etiology. Baseline serum samples were obtained via centrifugation from venous blood samples and stored at −80 °C for further analysis, avoiding freeze–thaw cycles. Clinical echocardiograms were performed at baseline, and left ventricular dimensions and function were determined according to guidelines [61,62]. Patients were followed-up until death or end of follow-up (if alive), and causes of death (if any) were recorded. All-cause and CV death were defined as the primary endpoints of the study. A death was considered as CV when it was due to HF (worsening HF or treatment-resistant HF in the absence of another cause), sudden cardiac death (any unexpected death, witnessed or not, of a previously stable patient with no evidence of worsening HF or any other known cause of death), myocardial infarction, stroke, secondary to a CV procedure (post-diagnostic or post-therapeutic) or other CV causes (e.g., rupture of an aneurysm, peripheral ischemia or aortic dissection). Cardiovascular death and HF rehospitalization were further included as composite endpoint. Nine patients were lost during follow-up and appropriately censored. All participants provided written informed consent. The study was approved by Local Ethics Committee of the Hospital Universitari Germans Trias i Pujol (code: EO 10-076) and was performed according to the ethical standards outlined in the Declaration of Helsinki [63]. ## 2.2. Clinical and Biochemical Data Anthropometric and clinical data were obtained at the point of study inclusion and were described elsewhere [60]. ## 2.3. Serum FABPs Determination Serum concentrations of FABP3 and FABP4 (Biovendor, Brno, Czech Republic) were determined in duplicate using commercial sandwich enzyme-linked immunosorbent assay kits (intra- and inter-assay coefficients of variation were estimated <$5\%$). ## 2.4. T2D Diagnosis A diagnosis of T2D was made when one of the following criteria were met: [1] a diagnosis of T2D was previously established and recorded in the patient’s electronic history, [2] fasting plasma glucose ≥ 126 mg/dL or HbA1c ≥ $6.5\%$ identified by laboratory testing [19] or [3] the patient had a current prescription for oral hypoglycemic medication or insulin. All the included patients in this study had type 2 diabetes. ## 2.5. Statistical Analysis The Kolmogorov–Smirnov test was used to determine the normality of the continuous variables. Continuous variables were expressed as median and interquartile range, unless otherwise indicated. Categorical variables are expressed as numbers with percentages. Differences between patients were analyzed by the Chi-squared test, Student’s t test and Mann–Whitney U test, as required. The association of FABP3 and FABP4 with all-cause and CV mortality, or the composite endpoint (i.e., CV death and HF hospitalization), was evaluated using a multivariable Cox regression analysis approach. The primary endpoints were considered as the dependent variables and the selected relevant clinical variables (i.e., age, sex, diabetes, ischemic etiology, New York Association (NYHA) functional class, time of evolution, FEECO (ejection fraction on echocardiography) NT-ProBNP and obesity) plus FABP3 or FABP4 as independent covariables. Competing risks models using the Fine and Gray method were realized with CV mortality and the composite endpoint (i.e., CV mortality or HF hospitalization). FABP3 and FABP4 analyses were performed for each 1 ng/mL or 1 ng/dL increase, respectively. Statistical analyses were performed using STATA V.16.0 (College Station, TX, USA). Differences were considered statistically significant with a two-sided $p \leq 0.05.$ ## 3. Results The baseline characteristics of the study population and a comparison of the clinical and biochemical parameters of patients with CHF with and without T2D are shown in Table 1. Out of 240 subjects with CHF included, 170 were men and 70 were women. The median age of the study population was 69 (59–77) years. Approximately $14.7\%$ of subjects were usual smokers and $43.3\%$ ex-smokers. Type 2 diabetes was present in 114 ($47.5\%$) of subjects. The percentages of subjects with hypertension and hypercholesterolemia were higher in patients with T2D compared with those without T2D ($77.2\%$ vs. $57.9\%$, p-value = 0.002; $80.7\%$ vs. $49.2\%$, p-value < 0.001, respectively). Upon inclusion, $73.7\%$ of subjects with CHF and T2D were receiving oral antidiabetic drugs and $62.3\%$ were under insulin treatment. The percentage of subjects with NYHA functional classes III-IV was higher in subjects with T2D compared with those without T2D ($29.0\%$ vs. $15.9\%$, p-value < 0.015). Subjects with T2D showed lower serum concentrations of total-, HDL- and LDL-cholesterol and increased serum concentrations of creatinine and NT-proBNP, compared with subjects without T2D. Additionally, subjects with T2D also showed increased serum concentrations of FABP3 (1.3-fold, p-value = 0.007) and FABP4 (1.3-fold, p-value = 0.006) compared with subjects without T2D. No significant correlations were found between lipid parameters (i.e., total-, LDL- and HDL-cholesterol and triglycerides) with FABP3 and FABP4 in T2D patients (Supplementary Figure S1). Urate was determined as a surrogate biomarker for oxidative status [64,65,66]. Whereas both FABP3 and FABP4 were found positively correlated with urate in non-T2D individuals (FABP3: ρ = 0.221, p-value < 0.013; FABP4: ρ = 0.195, p-value < 0.029), non-significant correlations were found in T2D patients (FABP3: ρ = 0.022, p-value < 0.820; FABP4: ρ = −0.038, p-value < 0.692) (Supplementary Figure S2). The mortality rate was higher in subjects with T2D compared with those without T2D ($18.5\%$, p-value = 0.004 vs. non-T2D). During a mean follow-up period of 5.78 ± 3.30 years, 143 patients died. The average years of follow-up until death (5.22 (2.02–8.17) vs. 7.13 (3.27–8.90), p-value = 0.009) or the composite endpoint (death or readmission) (2.57 (0.61–6.81) vs. 5.72 (0.90–8.31), p-value = 0.006) were lower in T2D than in non-T2D subjects. The rate of all-cause mortality increased along with FABP3 (Figure 1A) and FABP4 (Figure 1B) serum tertiles among subjects with T2D. Similarly, the rate of CV death was also increased with FABP3 (Figure 1C) and FABP4 (Figure 1D) tertiles. In line with these observations, in subjects with T2D, multivariable Cox models revealed both FABP3 and FABP4 as independent predictors for the occurrence of all-cause mortality (FABP3: HR 1.25, $95\%$ CI 1.09–1.44, p-value = 0.002; FABP4: HR 2.21, $95\%$ CI 1.12–4.36, p-value = 0.023, respectively) (Table 2) and CV death (FABP3: HR 1.28, $95\%$ CI 1.09–1.50, $$p \leq 0.002$$; FABP4: HR 4.19, $95\%$ CI 2.21–7.95, p-value < 0.001, respectively) (Table 3). Nevertheless, in subjects without T2D, serum FABP3 and FABP4 concentrations were unable to predict both all-cause (Supplementary Table S1) and CV (Supplementary Table S2) mortality. Additionally, FABP4 (HR 2.07, $95\%$ CI 1.11–3.87; p-value = 0.022), but not FABP3, predicted the occurrence of the composite endpoint (CV death or rehospitalization for HF) in subjects with CHF and T2D (Table 4), but not in subjects without T2D (Supplementary Table S3). Indeed, the composite endpoint rate also increased along with serum FABP3 (Figure 2A) and FABP4 (Figure 2B) tertiles in subjects with T2D. Finally, when both subjects with and without T2D were included in the analysis, the occurrences of all-cause (Supplementary Table S4) death were predicted by FABP3 and the occurrence of CV mortality (Supplementary Table S5) was predicted by both FABP3 and FABP4. Additionally, none of the studied FABPs were able to predict the occurrence of the composite endpoint in the whole cohort (Supplementary Table S6). Multivariable models were adjusted for clinically relevant variables. AUC for FABP3 = 0.8644. AUC for FABP4 = 0.8657. FABP3: fatty acid binding protein 3; FABP4: fatty acid binding protein 4; HF: heart failure; NYHA: New York heart association; LVEF: left ventricular ejection fraction; NTproBNP: pro-B-type natriuretic peptide; eGFR estimated glomerular filtration rate (CKD-EPI equation). Multivariable models were adjusted for clinically relevant variables. AUC for FABP3 model = 0.7532. AUC for FABP4 model = 0.7428. FABP3: fatty acid binding protein 3; FABP4: fatty acid binding protein 4; HF: heart failure; NYHA: New York heart association; LVEF: left ventricular ejection fraction; NTproBNP: pro-B-type natriuretic peptide; eGFR estimated glomerular filtration rate (CKD-EPI equation); SHR: Subdistribution Hazard Ratio. Multivariable models were adjusted for clinically relevant variables. AUC for FABP3 model = 0.7131. AUC for FABP4 model = 0.7188. FABP3: fatty acid binding protein 3; FABP4: fatty acid binding protein 4; HF: heart failure; NYHA: New York heart association; LVEF: left ventricular ejection fraction; NTproBNP: pro-B-type natriuretic peptide; eGFR estimated glomerular filtration rate (CKD-EPI equation); SHR: Subdistribution Hazard Ratio. ## 4. Discussion Subjects with CHF and T2D frequently display a poor prognosis [3]. In these subjects, the risk stratification of mortality is a challenging goal as it cannot be fully predicted by established risk factors [4,5,6,7]. Both FABP3 and FABP4 have been directly linked to a wide range of metabolic and cardiac disturbances, including HF [10,15,19,21,22,23,24,25,26,27,28]. Remarkably, increased serum concentrations of these molecules have been associated with myocardial alterations in subjects with impaired insulin signaling [15,20]. On the other hand, accumulating experimental evidence shows that both molecules can actively promote cardiac remodeling, leading to myocardial dysfunction [11,15]. The role of FABP3 and FABP4 as independent predictors of mortality has been reported in subjects with pulmonary embolism [46,47] and after acute coronary syndrome (ACS) [36,37,38], and all-cause death increased together with increasing FABP3 tertiles in subjects with stable angina [39]. Noteworthily, increased circulating FABP4 concentrations were found significantly associated with all-cause death in subjects with T2D [40,41], and all-cause mortality was associated with the highest tertile of FABP4 concentrations in subjects with peripheral arterial disease [42]. In the present study, a comprehensive Cox regression model was built in order to further analyze the potential role of both FABP3 and FABP4 as all-cause mortality predictors in a cohort of ambulatory patients with CHF. Serum concentrations of both molecules were higher in patients with CHF and T2D compared with patients with CHF without T2D. In our models, FABP3, but not FABP4, was identified as an independent predictor of the all-cause death in the whole study cohort. Remarkably, when only CHF subjects with T2D were considered, both FABP3 and FABP4 predicted the occurrence of all-cause mortality but were unable to predict the all-cause mortality in the subgroup of subjects without T2D. Focusing on CV mortality prediction, previous studies identified that low concentrations of FABP3 may predict CV death in combination with high BNP concentrations in patients with non-ischemic dilated cardiomyopathy [43]. Moreover, FABP3 has been defined as an independent predictor of CV events, including CV death, in subjects with suspected ACS [44], patients with HF and preserved ejection Fraction (HFpEF) [45] and in subjects with stable coronary artery disease and impaired glucose metabolism [48]. On the other hand, circulating FABP4 has been proposed as an independent predictor of CV mortality in the general population [57] and in patients with end-stage renal disease [52], peripheral arterial disease [42], coronary heart disease [52], stable angina undergoing percutaneous coronary intervention [54], ischemic stroke [49] and T2D [40,41,55]. In addition, circulating FABP4 concentrations have been reported to predict the risk of CV mortality among older adults with and without established CV disease [56] and associated with the risk of sudden cardiac death in older non-T2D individuals [53]. Moreover, FABP4 changes over time have been associated with adverse clinical outcomes, including CV death, in ambulatory patients with CHF [58]. In this context, we performed a competitive risk-regression model in order to explore the role of FABP3 and FABP4 predicting the occurrence of CV mortality. To our knowledge, this is the first report that FABP3 has strong predictive value for CV death in ambulatory CHF subjects with T2D. Noteworthily, it failed predicting CV death in subjects without T2D. Similarly, FABP4 predicted CV mortality in subjects with T2D, but not in non-T2D individuals. To further confirm this notion, additional studies were performed in order to explore the potential predictive value of FABP3 and FABP4 for a composite endpoint, including CV death and readmission, for HF. FABP4, but not FAPB3, was able to predict composite endpoint in the subset of CHF subjects with T2D, but not in subjects without T2D. In the metabolic context of T2D, the energy substrates of cardiomyocytes to produce metabolic energy switch from glucose to fatty acid. The increased use of fatty acids for energy production in mitochondria is frequently associated with increased reactive oxygen species (ROS) production, which leads to enhanced oxidative stress in diabetic cardiomyocytes. The accumulation of ROS profoundly affects normal cardiomyocyte physiology and function, leading to reduced cardiac contractibility and maladaptive cardiac response [67]. In this context, both FABP3 and FABP4 may directly impact the disease through oxidative stress regulation. Actually, both FABPs directly contribute to the fatty acids transport and, thus, may further fuel mitochondria. The serum levels of FABP3 have been found to be directly correlated to some oxidative stress biomarkers, such as MDA and ADMA, and inversely correlated to total TAC in patients with carbon-monoxide-induced cardiotoxicity [34]. On the other hand, FABP4 has been identified as a key molecule in oxidative stress during MI/R injury and diabetes-induced cardiac dysfunction in FABP4-knockout mice [35]. Additionally, FABP4 deficiency also led to activation of the eNOS/NO pathway and reduction in superoxide anion production [35]. Noteworthily, we used the serum concentrations of urate as a surrogate biomarker of oxidative status [64,65,66]; however, neither FABP3 nor FABP4 were associated with urate in T2D patients. Nevertheless, further molecular analyses are warranted in order to fully characterize the contribution of FABP3 and FABP4 to cardiac disturbances related to oxidative stress. Our study has some limitations. First, it was performed in a subset of the general population attending a single-center HF unit in a tertiary hospital, and it is not possible to rule out the possibility of bias due to selection. The relatively small sample size attenuated the impact of the results. Unfortunately, data on insulin, as well as oxidative parameters other than urate were unavailable in our cohort data sets. Additionally, the retrospective nature of our study precludes the extrapolation of causal relationships from our data. 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--- title: Swept-Source OCT Mid-Peripheral Retinal Irregularity in Retinal Detachment and Posterior Vitreous Detachment Eyes authors: - Stewart R. Lake - Murk J. Bottema - Tyra Lange - Keryn A. Williams - Karen J. Reynolds journal: Bioengineering year: 2023 pmcid: PMC10044997 doi: 10.3390/bioengineering10030377 license: CC BY 4.0 --- # Swept-Source OCT Mid-Peripheral Retinal Irregularity in Retinal Detachment and Posterior Vitreous Detachment Eyes ## Abstract Irregularities in retinal shape have been shown to correlate with axial length, a major risk factor for retinal detachment. To further investigate this association, a comparison was performed of the swept-source optical coherence tomography (SS OCT) peripheral retinal shape of eyes that had either a posterior vitreous detachment (PVD) or vitrectomy for retinal detachment. The objective was to identify a biomarker that can be tested as a predictor for retinal detachment. Eyes with a PVD ($$n = 88$$), treated retinal detachment ($$n = 67$$), or retinal tear ($$n = 53$$) were recruited between July 2020 and January 2022 from hospital retinal clinics in South Australia. The mid-peripheral retina was imaged in four quadrants with SS OCT. The features explored were patient age, eye axial length, and retinal shape irregularity quantified in the frequency domain. A discriminant analysis classifier to identify retinal detachment eyes was trained with two-thirds and tested with one-third of the sample. Retinal detachment eyes had greater irregularity than PVD eyes. A classifier trained using shape features from the superior and temporal retina had a specificity of $84\%$ and a sensitivity of $48\%$. Models incorporating axial length were less successful, suggesting peripheral retinal irregularity is a better biomarker for retinal detachment than axial length. Mid-peripheral retinal irregularity can identify eyes that have experienced a retinal detachment. ## 1. Introduction More than $50\%$ of retinal detachments present with the macula detached [1]. Combined with surgical complications [2], most people who experience a retinal detachment will suffer permanent vision loss even with successful intervention [3]. The prevention of retinal detachment with cryotherapy or laser retinopexy has been shown to be effective post-posterior vitreous detachment (PVD) in the presence of a retinal tear [4] and prior to PVD in eyes known to be at high risk, including type 1 Stickler syndrome and the fellow eyes of individuals with a giant retinal tear [5,6,7,8,9,10,11]. Unfortunately, for the majority, there is no clinical feature to identify those who would benefit from prophylactic treatment. Increased axial length or myopia is not specific enough to guide intervention. Lattice degeneration, the most significant peripheral retinal degeneration, has also been found insufficient to guide treatment [12,13]. There is then the need for a biomarker to increase the identification of eyes that would benefit from prophylactic treatment prior to vision loss. Optical coherence tomography (OCT) is widely available in ophthalmic practices [14] The retinal shape or contour, defined as the path of the retinal pigment epithelium across the B-scan, has already been shown to be a useful feature in retinal disease management. Macular curvature is useful in the assessment of myopic maculopathy, including dome-shaped maculopathy, myopic traction maculopathy, and macular schisis [15,16,17,18,19]. Smaller within-scan features have also been shown to be useful in the assessment of macular degeneration and focal choroidal excavation in pachychoroid retinopathy [20,21,22,23]. While the generation of the rectangular B-scan image from the fan-shaped A-scan capture leads to some alteration in general retinal curvature (which can be corrected) [24] the smaller within-scan features are well preserved in the image in a manner similar to the way in which the patterns on a leather belt remain recognisable, whether it is held straight or flexed. Retinal irregularity is the difference between the best-fit curve to the retinal contour and the retinal shape. This can be quantified in the frequency domain through Fourier transformation, which deconstructs the irregularity into shape features consisting of partitions (bins) in a spectrum of sine waves of varying frequencies. Retinal irregularity varies in a consistent manner across different regions of the posterior and mid-peripheral retina and increases with increasing axial length, the primary determinant of myopia [25]. Differences in irregularity measured with spectral domain OCT (SD OCT) have been found between eyes with a retinal detachment and those with PVD [26]. Over the last decade, swept-source OCT (SS OCT) has emerged as a new imaging modality with its own unique properties [27,28,29]. SD OCT scan rates of 65,000 A-scans per second can be exceeded by swept-source devices, which may achieve up to 200,000 A-scans captured per second [30,31]. Swept-source OCT uses a tuneable laser to replace the SD OCT super-luminescent diode as the sampling light source, with the returning light from the object captured by a dual-balanced photodetector [32]. Ophthalmic SS OCT use lasers with wavelengths in the infrared, typically centred around 1040–1060 nm, longer than the SD OCT light source, which has the disadvantage of reducing its minimum theoretical axial resolution, although the fast resampling combined with image processing can offset this to produce results similar to SD OCT [33]. The advantages of swept-source OCT are a faster image capture rate and lower sensitivity roll-off with increasing tissue penetration, which provides greater image quality across the depth of tissue [34,35]. This has enabled longer and wider retinal sampling in a single B-scan [36,37,38]. This paper explores the use of SS OCT mid-peripheral retinal images in differentiating retinal detachment from PVD eyes. The aim is to identify a biomarker that can be tested prospectively to identify eyes at risk of retinal detachment before PVD occurs. As an initial step, the objective here was to find such a biomarker in eyes that have experienced PVD or treatment for retinal detachment or a retinal tear. ## 2.1. Subjects Participants were recruited from outpatient eye clinics at two general teaching hospitals (Flinders Medical Centre and the Royal Adelaide Hospital) in South Australia. Eyes were imaged from individuals who had experienced either a PVD, retinal tear, or retinal detachment, with the latter two groups including only those that were the result of PVD-related events and after all treatment was completed. All eyes were examined by a retinal specialist (S.R.L.), and PVD was diagnosed in the presence of typical symptoms and signs and confirmed with OCT. All imaging was performed between July 2020 and January 2022 at Flinders Medical Centre using a Zeiss swept-source Plex Elite OCT (Carl Zeiss Meditec, Dublin, CA, USA). The study was approved by the Southern Adelaide Local Health Network Human Research Ethics Committee and was performed in accordance with the tenets of the Declaration of Helsinki. Prior written informed consent was obtained from all participants. ## 2.2. Image Capture SS OCT images were taken using a UHD 1 Spotlight 200 kHz scan, with a single 16 mm (2047 pixels) wide by 6 mm (3072 pixels) deep composite image created from 100 repetitions of the B-scan, eliminating the effects of subject movement. Four peripheral retinal images were taken from each eye. Images were taken in the coronal plane perpendicular to the direction of gaze, with the participant looking up, down, left, and right. The temporal and nasal scans were oriented vertically at 90 degrees to the horizontal, with those taken looking up and down parallel to the horizon. OCT image capture was performed at the most extreme eccentricity, where retina could still be visualised across the full width of the 16 mm B-scan window (Figure 1). The 6 mm deep scan ensured that all eyes were able to be imaged across the full width. ## 2.3. Image Processing Data extraction and image analysis were performed with programs written for this study in MATLAB. Raw.img data files were exported from the OCT device with the IMG export facility. These were converted to tagged image file format (tiff). Retinal shape represented by the retinal pigment epithelial line was extracted from the OCT images using a purpose-built graph theory algorithm [39]. The best-fit second-order polynomial curve was subtracted from the retinal contour, and a fast Fourier transformation was performed on the residual. The moduli for each frequency bin were corrected for the length of the signal (the adjusted length of the retina in the B-scan). This results in all irregularity values in the frequency domain being measured in mm2 per mm of retina imaged or simply mm. For each scan, the irregularity was determined relative to the average irregularity of the PVD eyes. PVD eyes alone were used for this average so that the reference standard came from the most common single diagnostic group. The eyes were distributed into five folds, and to ensure that there was an even distribution of axial lengths and diagnoses between each fold, they were sorted separately for each diagnostic group by axial length. Eyes were randomly allocated from each group into the 5 folds by generating consecutive sets of random numbers from 1–5 equal to the number of eyes. Numbers in each fold were non-equal when the sample was not divisible equally by five, and the remaining n samples were randomly placed into n groups. For eyes in each fold, an average B-scan irregularity was calculated from all the images from PVD eyes in the other 4 folds, equivalent to $80\%$ of the PVD sample, and the difference between this and each image’s irregularity was determined. A candidate feature vector for each eye was created consisting of the first 30 bins of the irregularity spectrum from each of the four directions of gaze (in order: up, down, temporal, and nasal), the best-fit curvature to the retinal contour for each image in the same order, the axial length of the eye, and the participant’s age, resulting in 126 potential features [25]. ## 2.4. Statistical Methods Spearman’s rank correlation was used to assess the correlation between axial length and average irregularity, the irregularity for each region, and age, for all eyes combined and by diagnostic group. Between-group comparisons were performed using two-sample t-tests. Comparison of regional differences in irregularity was performed with one-way ANOVA. All analyses considered statistical significance to be reached when $p \leq 0.05.$ Classifier sensitivity and specificity were calculated from the PVD and retinal detachment eye test set. Sensitivity was calculated as retinal detachment eyes labelled 2 (true positive) divided by total number of test set retinal detachment eyes. Specificity was the number of PVD eyes labelled 1 (true negative) divided by the total number of test set PVD eyes. ## 2.5. Feature Selection Feature identification and training were performed with retinal detachment and PVD eyes. Retinal tear eyes were considered as eyes that would have developed a retinal detachment but for their timely presentation and were used as a second validation set. The retinal detachment and PVD eyes were randomly split $\frac{2}{3}$:$\frac{1}{3}$ into training and testing sets, and feature selection was performed with the training set prior to testing. Multivariate feature selection with regularisation methods LASSO and elastic net were used to identify potential feature combinations [40]. Both LASSO and elastic net input variables were scaled to a mean of 0 with variance of 1 with elastic net α = 0.5 and ten-fold cross-validation to identify potential features. Once a reduced feature set was obtained, all possible combinations of the remaining features were explored using quadratic discriminant analysis using the training set to identify classifiers with three or fewer features and the highest sensitivity for a specificity greater than 0.90. The high specificity threshold was selected to reduce the number of false positives (identifying a PVD eye as one with a retinal detachment), as labelling a PVD as retinal detachment was considered less acceptable than vice versa. Classifier performance was then evaluated with the test set. ## 3.1. Subjects Participant demographics and the number of eyes in each group are reported in Table 1. In total, 88 eyes with a PVD, 67 eyes with a retinal detachment (treated using vitrectomy without scleral buckling), and 53 eyes with a retinal tear were imaged. Age ranged from 47 to 84 years, with axial length from 22.14 to 27.27 mm. Imaging was uncomplicated in all eyes, with no significant media opacities noted in the PVD group and the vitreous cavity optically clear post-vitrectomy in retinal detachment eyes. Subjects with a retinal detachment were younger and had larger eyes than those who experienced PVD. Those who experienced a retinal tear had eyes with shorter axial lengths than those who presented with a retinal detachment. ## 3.2. Within-Eye Distribution of Irregularity One-way ANOVA with a post hoc Tukey test indicated that the average irregularity was significantly greater in the inferior retina (mean (SD) 9.70 (7.14) mm) than in other regions (F [3,825] = 37.83, $p \leq 0.001$), with the temporal and nasal retinal irregularity (6.06 (2.51) mm and 6.01 (2.43) mm, respectively) no different from the superior retina (6.04 (3.16) mm) (Figure 2). ## 3.3. Between-Group Differences in Irregularity Total irregularity did not differ between groups (retinal detachment irregularity 28.92 (8.14) mm, PVD 27.17 (8.72) mm, $$p \leq 0.21$$), with retinal tear irregularity intermediate between the two (27.49 (10.11) mm). Within each region, irregularity differed significantly between retinal detachment and PVD eyes in the superior retina (6.61 (3.44) mm vs. 5.53 (2.23) mm, $$p \leq 0.02$$) but not in any other retinal area. ## 3.4. Correlation of Irregularity with Axial Length Axial length correlated weakly with the average total irregularity of all eyes ($$p \leq 0.02$$, ρ = 0.17) and average total irregularity for the PVD group alone ($$p \leq 0.04$$, ρ = 0.23) but not with average irregularity of the retinal detachment or retinal tear eye groups. Within the four individual regions, axial length correlated weakly with the total irregularity from superior ($$p \leq 0.05$$, ρ = 0.14) and inferior ($$p \leq 0.015$$, ρ = 0.18) retinas. In individual diagnostic groups, this correlation only persisted for PVD eyes (superior retina, $$p \leq 0.08$$, ρ = 0.20; inferior retina, $$p \leq 0.03$$, ρ = 0.24) and not with retinal detachment or retinal tear eyes. ## 3.5. Feature Selection Elastic net regularisation identified a group of six shape features with a mean squared error = 0.22. These features were from lower frequency superior retina, axial length, lower frequency from the inferior retina, and three features from higher frequencies in the temporal retina. LASSO identified a group of three features with a mean squared error of 0.24. These three were also identified using elastic net, and all six features were further explored through the training set classifier performance. ## 3.6. Training Set Classifier Performance All possible combinations of one–six features from the six candidate features were identified and used to train the quadratic discriminant classifiers. The three classifiers with the greatest training set sensitivity for specificity are shown in Supplementary Table S1: Performance of tested classifiers. The classifier generated from the fourth frequency bin from the superior retinal scan and two higher frequency bin (23 and 26) shape features from the temporal retinal scan were selected for use. Five-fold randomised cross-validation of the classifier using training set eyes repeated 20 times had an average success rate = 0.66, with the standard deviation of the success rates = 0.07. ## 3.7. Test Set Results Table 2 presents the confusion matrix for the test set eyes. The classifier exhibited a specificity of $84\%$ and a sensitivity of $48\%$ in separating retinal detachment from PVD eyes. The initial receiver operating characteristic curve generated by 5000 bootstrap replicas showed an inverse sigmoid or logit shape, suggesting the predictor had a non-linear (U-shaped) relationship with the outcome [41]. This was corrected by centring the classifier output to its median value, leading to an area under the curve = 0.74 ($95\%$ confidence intervals 0.59–0.85, Figure 3). The classifier sensitivity for retinal tear eye identification was $35\%$. ## 4. Discussion Mid-peripheral retinal shape irregularity identified eyes that had experienced a retinal detachment from a mixed sample of eyes that had experienced either PVD or PVD-related retinal detachment. Sensitivity for retinal detachment approached $50\%$, with a high specificity of $84\%$. An ability to identify half of the retinal detachment eyes is a considerable improvement on the status quo, where currently no test for retinal detachment is available. If this were to be employed as a test for retinal detachment, a high specificity is desirable to ensure eyes that are not at risk of vision loss are not mislabelled. SS OCT retinal irregularity was greatest in the inferior retina and correlated with the axial length in eyes with a PVD. The lack of correlation between axial length and irregularity in retinal tear and detachment eyes, along with the slightly greater average irregularity, suggests that these eyes were simply more irregular regardless of size. The greater inferior irregularity was associated with a variety of retinal contours. In more than one-third of eyes, there was a localised infero-temporal concavity. These “micro-staphylomata” were up to 8 mm wide, but with a depth of less than 1 mm, they would be hard to identify using other imaging techniques. The smallest changes that can be detected with SS OCT are defined by the spatial resolution of the images (an optical axial resolution of 6µm). However, this does not define changes corresponding to the beginning of retinal detachment. Classification is based upon the combination of three shape features in the frequency domain, which does not translate to a particular geometric change in the OCT. The threshold for each feature is not a single value and varies as the magnitude of the other two features changes. ## 4.1. Comparison with SD OCT SS OCT retinal irregularity was greatest in the inferior retina, similar to previously reported SD OCT-determined irregularity [25]. Nasal and temporal retinal irregularities, here sampled vertically, were of a similar magnitude to superior retinal irregularity. This suggests that the lower magnitude irregularity seen in the temporal and nasal regions with SD OCT related more to the horizontal scan orientation rather than to regional differences between the superior and inferior retina and temporal and nasal retina and that mid-peripheral irregularity is greater when measured coronally compared with transversely. The SS OCT classifier results are consistent with the reported findings with a classifier using SD OCT [26], with an increased sensitivity while maintaining high specificity. The improvement appears to be from the incorporation of shape features from coronal plane-imaged temporal retinas, a format that was not possible with the previous generation of SD OCT. Two of the six candidate features for classification (axial length and lower frequency upper retina) were consistent with the SD OCT classifier model. Perhaps surprisingly, in the context of the established association between myopia and retinal detachment, the model performed well without the inclusion of axial length, the only known useful anatomical metric for retinal detachment risk prior to retinal shape analysis. The larger (16 mm) B-scan available with SS OCT enabled the analysis of shape features larger than was possible with SD OCT. These larger features (bins 2 and 3 in the frequency domain) were not important in classification, suggesting that the larger B-scan size is not critical when sampling shapes to classify retinal detachment and PVD eyes. ## 4.2. Association between Irregularity and Retinal Detachment The cause of the association between axial length and retinal detachment and the link between this and local retinal shape are unknown. It is unlikely that irregularity leads directly to retinal tear formation but rather that an underlying property of the eye causes both increased shape irregularity and promotes retinal tear formation. This link between local irregularity and retinal detachment may relate to regional variation in the growth of Bruch’s membrane in the equatorial regions of the eye [42,43,44]. Retinal breaks occurring with PVD are associated with a localised posterior extension of the posterior margin of the vitreous base (Figure 4) at or near the equator. Small segments of the posterior margin of the vitreous base may be drawn posteriorly with Bruch’s membrane expansion during myopisation, either in continuity with or separated from the continuous vitreous base. These posterior points of firm vitreo-retinal attachment are put under extra mechanical strain when PVD occurs up to the vitreous base, leading to hole or tear formation. An expansion of Bruch’s membrane at the equator in the coronal plane may be limited by the more limited expansion of the eye size in this plane during myopisation [45]. Bruch’s membrane expansion may exceed the coronal circumference of the eye within which it is confined, leading to “wrinkles” in this plane, while axial expansion draws local segments of the vitreous base posteriorly, producing the configuration that leads to retinal breaks when PVD occurs (Figure 4). The relationship between retinal tear formation and irregularity is hypothesised to reflect an association arising from multidirectional but locally variable growth of the mid-peripheral Bruch’s membrane within an eye that grows axially more than coronally during myopisation. Alternative explanations include alterations in connective tissue behaviour from collagen variations linked to myopia and retinal detachment-associated genes [46,47]. This may lead to both small-scale changes in scleral rigidity, producing shape irregularity, as well as abnormal vitreo-retinal attachment. Local variation in scleral strength may produce localised weakening and increased irregularity. These “micro-staphylomas” alter the interaction between the vitreous and retina, changing the strength of vitreo-retinal adhesion. In areas of the posterior extension of the vitreous base, this might lead to retinal tear formation when PVD occurs. ## 4.3. Limitations of This Study As all groups analysed here are post-PVD, these results cannot establish that the same shape features prior to PVD will be able to predict retinal detachment. Prior work found no evidence of a change in retinal shape features with SD OCT compared with before and after PVD or retinal detachment surgery. Currently, fellow eyes without a PVD from individuals who have experienced a retinal detachment in one eye are being imaged to assess prospective model accuracy. The literature suggests the risk of retinal detachment in these eyes is 7–$10\%$ [48,49], with $42.4\%$ of eyes that develop a PVD within five years of the cardinal event experiencing either a retinal tear or retinal detachment [50]. Classification accuracy may be improved further if clinical features, such as lattice degeneration, family history, and genetic profile, are considered [46,47,49,51,52]. Discriminant analysis is a suitable algorithm for a moderately-sized sample such as this, which may seem small for those used to deep learning models. All self-optimising classifiers are defined by their sample, so other populations may need their own training sets. ## 5. Conclusions These SS OCT data provide further support for the concept that retinal shape differs between eyes that have had a retinal detachment and those that have experienced a PVD. The data presented here do not identify whether these shape features precede retinal detachment. 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--- title: Mitochondrial DNA Polymorphism in HV1 and HV2 Regions and 12S rDNA in Perimenopausal Hypertensive Women authors: - Wojciech Kwaśniewski - Aleksandra Stupak - Alicja Warowicka - Anna Goździcka-Józefiak - Jerzy Mosiewicz - Jolanta Mieczkowska journal: Biomedicines year: 2023 pmcid: PMC10044999 doi: 10.3390/biomedicines11030823 license: CC BY 4.0 --- # Mitochondrial DNA Polymorphism in HV1 and HV2 Regions and 12S rDNA in Perimenopausal Hypertensive Women ## Abstract Estrogens enhance cellular mitochondrial activity. The diminution of female hormones during menopause may have an effect on the mitochondrial genome and the expression of mitochondrial proteins. Hence, oxidative stress and the pro-inflammatory state contribute to the formation of systemic illnesses including arterial hypertension (AH). This study aimed to determine the types and frequency of mutations in the mitochondrial DNA (mtDNA) nucleotide sequence in the hypervariable regions 1 and 2 (HV1 and HV2) and the 12S RNA coding sequence of the D-loop in postmenopausal women with hypertension. In our study, 100 women were investigated, 53 of whom were postmenopausal and 47 of whom were premenopausal (53.9 ± 3.7 years vs. 47.7 ± 4.2 years, respectively). Of those studied, 35 premenopausal and 40 postmenopausal women were diagnosed with AH. A medical checkup with 24 h monitoring of blood pressure (RR) and heart rate was undertaken (HR). The polymorphism of the D-loop and 12S rDNA region of mtDNA was examined. Changes in the nucleotide sequence of mtDNA were observed in $23\%$ of the group of 100 women. The changes were identified in $91.3\%$ of HV1 and HV2 regions, $60.9\%$ of HV1 segments, $47.5\%$ of HV2 regions, and $43.5\%$ of 12S rDNA regions. The frequency of nucleotide sequence alterations in mtDNA was substantially higher in postmenopausal women ($34\%$) than in premenopausal women ($10.6\%$), $$p \leq 0.016.$$ A higher frequency of changes in HV1 + HV2 sections in postmenopausal women ($30.2\%$) compared to the premenopausal group ($10.6\%$) was detected, $$p \leq 0.011.$$ Only postmenopausal women were found to have modifications to the HV2 segment and the 12S rDNA region. After menopause, polymorphism in the mtDNA region was substantially more frequent in women with arterial hypertension than before menopause ($$p \leq 0.030$$; $37.5\%$ vs. $11.5\%$). Comparable findings were observed in the HV2 and HV1 regions of the AH group ($35\%$ vs. $11.5\%$), $$p \leq 0.015$$, in the HV1 segment ($25\%$ vs. $11.5\%$), $$p \leq 0.529$$, and in the HV2 segment, 12S rDNA ($25\%$ vs. $0\%$). More than $80\%$ of all changes in nucleotide sequence were homoplasmic. The mtDNA polymorphisms of the nucleotide sequence in the HV1 and HV2 regions, the HV2 region alone, and the 12S RNA coding sequence were associated with estrogen deficiency and a more severe course of arterial hypertension, accompanied by symptoms of adrenergic stimulation. ## 1.1. Mitochondrial DNA The research of mitochondrial genome polymorphism began in the 1980s, when new tools in molecular biology made it possible to identify the mtDNA mitochondrial genome sequence [1]. The first mutation in the mitochondrial genome was described in 1988 and involved nucleotide 8344, a shift in the A/G sequence that causes myoclonic epilepsy accompanied with the presence of ragged red fibers in the muscles (MERRF syndrome, myoclonic epilepsy, and ragged red fibers) [2]. In human cells, the genetic material of the mitochondria constitutes $1\%$ of the cell’s DNA [3]. MtDNA is a circular molecule containing 16,569 base pairs (bp) and is situated in the matrix of mitochondria. Each mitochondria carries four to ten copies of mtDNA [4,5]. The arrangement and structure of the mitochondrial genome parallels that of the bacterial genome. Moreover, mitochondrial ribosomes are prokaryotic in nature. Histone proteins are absent in mtDNA. Additional proteins related to mitochondrial DNA form structures known as nucleoids (nt). In both strands of mtDNA, 37 genes have been found in the mitochondrial genome (28 on the H (heavy) strand and 9 on the L (light) strand). Genes are tightly packed in mitochondrial DNA, and the sequences of some genes overlap and overlap (ATP8 and ATP6, ND4L, and ND4); there are few non-coding regions [6,7,8,9,10]. Mitochondria are also responsible for apoptosis’ so-called internal route. Along with this process is the release of cytochrome c and other proteins from the mitochondria. Released from the mitochondria, cytochrome c contributes to the synthesis of the Apoptosome protein complex [11]. Mitochondrial proteins belonging to the BCL-2 family, which are mostly localized in the outer mitochondrial membrane, and factor or endonuclease G are also implicated in the mitochondrial apoptotic process [12]. ## 1.2. The D-Loop in mtDNA The D-loop, which comprises $7\%$ of the mitochondrial genome and regulates replication and transcription of mitochondrial genes, is an important non-coding region in mtDNA. It is composed of 1122 base pairs (from 16,024 nt to 567 nt mtDNA) and is located between the genes encoding the tRNA Pro and the tRNA Phe. The nucleotide sequences responsible for initiating gene transcription and mitochondrial genome replication have been found in this area [6,13,14,15]. In the D-loop region, there are also two hypervariable regions: the first (HV1) from 16,024 nt to 16,383 nt and the second (HV2) from 57 nt to 33 nt. In these places, the nucleotide sequence polymorphism is utilized in forensic investigations and medical diagnostics [16]. Additional variable regions in the D-loop of mtDNA, known as “hot” regions, are situated between 303 and 315 nucleotides and between 16,184 and 16,193 nucleotides [6,13,14,15]. In addition, the D-loop of mtDNA contained multiple point mutations, microsatellite instability alterations, and significant deletions. These modifications may influence mtDNA replication and the transcription of mitochondrial genes [17]. The mitochondrial genome encodes thirteen essential subunits of the oxidative phosphorylation (OXPHOS) machinery in the inner mitochondrial membrane [14]. There are 13 mitochondrial genes that encode respiratory chain-related proteins, 22 that encode transfer RNA (tRNA), and the remaining 2 encode ribosomal RNA (rRNA)—12SrRNA and 16SrRNA [13]. The respiratory chain of the mitochondria has 87 polypeptides. Both the mitochondrial genome (mtDNA) and the nuclear genome encode the proteins that construct the inner mitochondrial membrane respiratory chain (nDNA). The respiratory chain proteins encoded by mitochondrial DNA include complex I—NADH proteins. Complex II, which regulates succinate dehydrogenase, is encoded exclusively by nuclear DNA [18]. The non-coding region of the D-loop is responsible for the control of mtDNA replication and transcription. The straightforward arrangement of mtDNA renders this genome more susceptible to the action of mutagenesis agents, such as those that affect the nuclear genome. Thus, the same cell can contain both normal copies of mitochondrial DNA and their mutants. This occurrence is referred to as heteroplasmia [19]. ## 1.3. Diseases Related to mtDNA Several studies have demonstrated a link between genetic alterations in mitochondrial DNA and human disorders, including coronary heart disease, hypertension, diabetes, endometriosis, and cancer [20,21,22]. The hunt for the reasons of the aging processes has also drawn the attention of numerous researchers on the effect of oxidative stress on mtDNA alterations [23,24]. Mitochondria are the site of cellular energy transformations, and in particular the site of formation and storage of high-energy compounds. Tissues highly dependent on mitochondrial energy production are the heart, skeletal muscle, central nervous system, and kidney. Therefore, the cause of disturbances in the functioning of these organs may be a decrease in the efficiency of mitochondrial respiration caused by mtDNA mutations. The so-called mitochondrial illnesses are caused by mutations in the mitochondrial DNA [25]. Mutations in the structure and function of mitochondria disturb the proper functioning of these organelles and deregulate the apoptotic process, which is the root cause of numerous human disorders [26]. ## 2.1. Study Design The purpose of this study was to determine the types and frequency of changes in the sequence of mitochondrial DNA nucleotides in the HV1 and HV2 regions and in the D-loop region encoding the 12S rDNA in postmenopausal women with essential arterial hypertension and hormonal abnormalities. ## 2.2. Study Population The tests were conducted on 100 women, 53 postmenopausal and 47 premenopausal, at the Department of Internal Diseases, Outpatients Clinic, and Department of Gynecology at The 1st Independent Public Teaching Hospital of Medical University of Lublin, Poland. ## 2.3. Study Variables Among $75\%$ of the women in the study group, arterial hypertension was detected. In each case, the following tests were conducted: 1/medical examination consisting of an interview and physical examination; 2/measurement of blood pressure twice after rest; $\frac{3}{24}$ h blood pressure and heart rate monitoring; and 4/investigation of mtDNA polymorphism in the HV1 and HV2 regions and the 12S rDNA coding region. During two medical appointments, a medical examination was conducted, and a questionnaire was used to collect data from the interview and medical examination. Blood pressure was measured twice in the examined women each time after a 10 min rest, using the mean value of these measurements. Women with severe organic systemic diseases, previously diagnosed neoplastic diseases, previously diagnosed mitochondrial diseases, severe degenerative diseases, previously diagnosed secondary hypertension, previously diagnosed ischemic heart disease, a history of a heart attack or stroke, cardiomyopathies, congenital and acquired heart defects, previously diagnosed peripheral vascular diseases, diabetes, thyrotoxicosis, and thymoma were excluded from research. ## 2.4. Methods of Examination Conducted in Study The postmenopausal phase was determined based on the patient’s medical history (menopausal symptoms such as hot flashes, increased sweating, and amenorrhea lasting over a year) and hormonal status (increase in FSH (follicle stimulating hormone) > 30 U/L in the blood serum). Essential hypertension (AH) was diagnosed based on systolic blood pressure (RRs) 140 mmHg and/or diastolic blood pressure (RRr) 90 mmHg, as well as history (previously diagnosed and/or treated hypertension). Based on the systolic and diastolic blood pressure values, the European Society of Hypertension (ESC) distinguished arterial hypertension groups as optimal pressure, normal, high normal, arterial hypertension 1o, 2o, and 3o [27]. Every patient with arterial hypertension had outpatient 24 h monitoring of blood pressure and heart rate (ABPM). The hours beginning at 6 a.m. to 11 p.m. were taken as the waking and daytime hours, taking into account individual differences. Nighttime rest is 11 p.m. to 6 a.m. During the day, systolic and diastolic blood pressure were measured three times per hour, whereas at night, they were measured twice per hour. Systolic blood pressure (RRs) ≥ 135 mmHg during the day and diastolic blood pressure (RRr) ≥ 85 mmHg during the day were considered to be elevated, according to the recommendations of the European Society of Hypertension, and systolic blood pressure (RRs) ≥ 120 mmHg and diastolic pressure during the night were elevated (RRr) ≥ 70 mmHg. The criteria for a sudden morning rise in blood pressure were increases in systolic and diastolic blood pressure of at least 10 mmHg. The increase is the difference between the mean nighttime measurements taken during sleep and the first two hours after awakening. According to the magnitude of the decrease in systolic blood pressure, the following subgroups were distinguished among women with hypertension before and after menopause: 1. “ dippers”—decrease in systolic blood pressure at night (RRs) compared to the day from 10 to $20\%$, 2. “ non-dippers”—night systolic blood pressure (RRs) decrease in relation to the day to $10\%$, 3. “ extreme dippers”—systolic blood pressure (RRs) drops by more than $20\%$ between day and night, 4. “ reverse dippers”—increase in systolic blood pressure (RRs) at night. ## 2.4.1. Isolation of mtDNA Polymorphism in the Regions of HV1, HV2, and 12S rDNA Blood was collected for genetic testing under standard fasting conditions. Blood cells (lymphocytes) of the patients were isolated using the QIAamp DNA Midi Kit (Qiagen, Hilden, Germany) according to the manufacturer’s isolation protocol [28]. The purity and concentration of DNA were analyzed spectrophotometrically (SynergyTM H1, BioTek, Santa Clara, CA, USA). The obtained DNA was suspended in EB buffer (10 mM TrisHCl pH 8.5) and stored at −20 degrees Celsius for future research. ## 2.4.2. Analysis of the mtDNA D-Loop Mutation MtDNA’s D-loop region was amplified with two PCR primer pairs. The primers had the following sequences: F4 5′ CACAGGTCTATCACCCTATTAACCA 3′ located at 4–28 bp, R599 5′ TTGAGGAGGTAAGCTACAT 3′ located at 599–581 bp, and F15974 5′ ACTCCACCATTAGCACCCAAA 3′ located at 15,974–15,994 bp; R16564 5′ TGATGTCTTATTTAAGGGGAACGT 3′ F4 and R16564 primers had previously been described [14]. In a 30 L reaction volume containing 1 PCR buffer, 1 M of each forward and reverse primer, 1.5 mM MgCl2, 200 M of each dNTP, and 1 U of Taq DNA polymerase, PCR amplifications were performed (Fermentas, Waltham, MA, USA). The PCR conditions were as follows: pre-denaturation at 95 °C for 15 min, followed by 40 cycles at 95 °C for 20 s, 57.6 °C for 45 s, 72 °C for 45 s, and a final extension at 72 °C for 6 min (for the F4 and R599 pair of primers); and pre-denaturation at 95 °C for 5 min, followed by 30 cycles at 95 °C for 30 s. The electrophoresis of PCR products amplified from D-loop mtDNA was carried out on $1.5\%$ agarose gels. Following purification with the QIAquick PCR Purification Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions, all PCR products were sequenced (in forward and reverse directions). The D-loop region’s nucleotide sequence was determined by comparing sequences to the Cambridge reference sequence (rCRS, NC 012920) [29]. ## 2.4.3. DNA Sequencing The PCR-purified DNA samples were sequenced automatically. The Laboratory of Molecular Biology Techniques in Poznan, Poland, was tasked with completing this phase of research [28]. ## 2.4.4. Computer Evaluation Chromas—Pro software (version 1.31) and DNAStar (MegAlign, Madison, WI, USA) were used to interpret the chromatograms of the sequenced DNA samples. The BLAST Align two success program and the BioEdit program were used to compare DNA sequences from different samples in order to detect mutations (NCBI database). NC 012920 was the sequence number selected from the databases as the reference sequence [29]. ## 2.5. Ethics Research procedures were in line with ethical standards for human experimentation. They were in accordance with the opinion of the Bioethics Committee of the Medical University of Lublin (No. KE-$\frac{0254}{185}$/2006, 26 October 2006) as well as the Helsinki Declaration of 1975 and its 2000 amendment. Each of the examined persons gave written informed consent to participate in the experiment. ## 2.6. Statistical Analysis Using the Kolmogorov–Smirnov test (allowing for the assessment of the normality of the distribution), it was determined whether individual analyses including linear variables required parametric tests (Student’s t-test used in the comparisons of two independent groups if the assessed variables had a normal data distribution) or non-parametric tests (U-Mann–Whitney test used in the comparisons of 2 independent groups or the Spearman’s rank correlation test used to assess the correlation between 2 variables if the assessed variables had a distribution other than normal). In comparisons of linear variables, the mean was used as the measure of concentration, and the standard deviation as the measure of dispersion. As a result, the correlation coefficient R was calculated when evaluating the correlation. Moreover, in the case of nonlinear data (classified), their analysis was conducted using the logistic regression method, which included the calculation of Wald 2, odds ratios (ORs), and corresponding $95\%$ confidence intervals ($95\%$CI). In all analyses, alpha (p-value) values less than 0.05 were considered statistically significant. ## 3.1. Age and Anthropometric Information Regarding the Respondents A group of 100 non-smoking and non-alcohol-using women before (47 women) and after menopause (53 women), mean age 51.1 ± 5.0 years, mean body weight 70.3 ± 14.5 kg, BMI (body mass index) (kg/m2)- 27.2 ± 5.3, waist circumference (cm) 87.7 ± 13.7, and WHR waist/hip ratio 0.829 ± 0.061, was examined. The range of time since the last menstrual period was 0 to 7.9 years, with a mean of 4.5 to 7.9 years. No participant had ever utilized Hormone Replacement Therapy. The studied groups of pre- and postmenopausal women did not differ in terms of body weight, BMI, waist circumference, or WHR. A statistically significant difference existed between the age of the group of women studied before and after menopause. Women in the postmenopausal group were older than in the premenopausal group, and the difference was statistically significant ($p \leq 0.01$). The average age of respondents with mtDNA polymorphism (before and after menopause) was 52.0 4.8 years and did not differ significantly from the average age of the other respondents (50.8 5.1 years; $$p \leq 0.131$$). ## 3.2. Analysis of the Sequences of the mtDNA HV1, HV2, and 12S RNA Regions The nucleotide sequence of the most variable regions of the D-loop, HV1 and HV2, and the coding region of the 12S RNA of mtDNA were analyzed in total DNA isolated from the blood cells of 53 postmenopausal women and 47 premenopausal women who served as the reference group. A summary of the observed changes in the studied mtDNA regions in the entire group of women (in the pre- and postmenopausal period) is presented in Table 1. In the group of 100 women (before and after the menopause), changes in the nucleotide sequence in the mtDNA segments studied were found in $23\%$ of cases. The number of females and variations in the nucleotide sequence of mtDNA were as follows:With all mtDNA nucleotide sequence changes in the HV1 and HV2 regions and mtDNA 12S RNA coding sequence—23 women. With nucleotide sequence changes in the HV1 mtDNA region—14 women. With nucleotide sequence changes in the HV2 mtDNA region—11 women. With nucleotide sequence changes in the HV1 and HV2 regions of mtDNA—21 women. With changes in the coding sequence of nucleotides in the 12S RNA region of mtDNA—12 women. The number of observed mtDNA changes in individual cases ranged from 1 to 18. They were present in $91.3\%$ of the hypervariable regions (HV1 and HV2), and more frequently in the HV1 segment ($60.9\%$) than the HV2 segment ($47.5\%$). The number of observed changes in individual cases ranged from one to nine. Changes were present in $43.5\%$ of the 12S RNA coding region, where from two to five changes were observed. Changes in the mtDNA nucleotide sequence affected the HV1 segment in $60.9\%$ of cases, the HV2 segment in $47.8\%$ of cases, and the number of changes observed in individual cases ranged from one to nine. In $47.8\%$ of cases with two to five lesions, mtDNA changes occurred in the 12S RNA coding sequence. Changes in the Nucleotide Sequence of the HV1, HV2, and 12S RNA Regions of mtDNA are Presented in Table 2. ## 3.3. Homoplasmia/Heteroplasmia of mtDNA in the Study Group with Changes in the Mitochondrial Genome in the HV1 and HV2 mtDNA Regions Most of the identified changes in the nucleotide sequence are homoplasmic ($81.8\%$ of respondents with changes in the HV1 region, $90.1\%$ in the HV2 region, and $100\%$ with changes in the region encoding the 12S RNA mtDNA). Heteroplasmic changes concerned the following nucleotides in the HV1 region: 16093TC, 16230AG, and 16286CT occurred in $18.2\%$ of the subjects with changes in the HV1 region. In subjects with the 239TC nucleotide the changes in the HV2 region were detected in $9.9\%$ cases. ## 3.4. Menopause The frequency of nucleotide sequence changes in the mitochondrial genome of the studied postmenopausal women ($34.0\%$) compared to the premenopausal period ($10.6\%$) was significantly higher ($$p \leq 0.016$$). Both changes in the HV2 region and in the 12S RNA coding sequence occurred only in the studied postmenopausal women and were not observed in the studied group of premenopausal women. The frequency of nucleotide sequence changes in both HV1 and HV2 hypervariable segments in postmenopausal women was higher compared to the premenopausal period, and the difference was statistically significant ($$p \leq 0.011$$). However, the incidence of mtDNA changes among the examined postmenopausal women in the HV1 segment only did not differ significantly in comparison to the group of premenopausal women. A comparison of the frequency of nucleotide sequence changes in different mtDNA segments in pre- and postmenopausal women is presented in Table 3. Modifications in the nucleotide sequence in mtDNA occurred in women with hypertension (pre- and postmenopausal) in $25.3\%$ of the subjects. Changes in the nucleotide sequence in mtDNA in postmenopausal women with arterial hypertension occurred in $37.5\%$ of the respondents and in $11.5\%$ of the premenopausal women. The difference was statistically significant ($$p \leq 0.030$$). Table 4 presents a comparison of the frequency of mtDNA nucleotide sequence changes in the hypervariable sections of HV1 and HV2 and the coding sequence of 12S RNA with arterial hypertension in the premenopausal group and the postmenopausal group. The frequencies of nucleotide sequence changes in the HV1, HV2, and 12S rDNA mtDNA regions in pre- and postmenopausal hypertension are presented ($37.5\%$ vs. $11.5\%$, $$p \leq 0.03$$). In subjects with premenopausal hypertension, no changes in mtDNA nucleotide sequence in the HV2 and 12S RNA segment were observed; hence, the influence of menopausal status on the incidence of these changes cannot be statistically expressed. ## 3.5. Parameters of Daily RR Monitoring in Patients with Arterial Hypertension Depending on Changes in mtDNA Nucleotide Sequence Changes in the HV1 and HV2 regions of mtDNA were accompanied by a modification of the parameters of the monitored RR. Statistically significantly higher maximum systolic RR and heart rate/min were observed, as well as a higher frequency of increased values of systolic RR during the day in the subjects (before and after menopause) in the group with nucleotide sequence changes in the HV1 and HV2 segments compared to the group without these changes. Parameters of daily RR monitoring in the subjects (before and after menopause) in the group with changes in the nucleotide sequence in the HV1 and HV2 regions and in the group without these changes are presented in Figure 1 and Figure 2. ## 3.6. Nucleotide Sequence Changes in HV1 and RR Monitoring Alterations in the nucleotide sequence in the HV1 segment were associated with an increase in maximum daytime systolic blood pressure. The mean maximum systolic RR in the patients (before and after menopause) in the group with nucleotide sequence changes in the HV1 hypervariable region was statistically significantly higher compared to the group without these changes. The remaining parameters of the daily RR monitoring did not differ significantly in the studied women (before and after the menopause) between the group with changes in the nucleotide sequence in the HV1 segment as compared to the group without changes. A comparison of these parameters is presented in Figure 3. ## 3.7. Nucleotide Sequence Changes in HV2 and RR Monitoring Modifications in the nucleotide sequence in the HV2 mtDNA region were accompanied by slightly higher mean values of the maximum systolic RR both during the day and night, as well as significantly higher heart rate/min during the day. The comparison of RR monitoring parameters in the subjects (pre- and postmenopausal) in the group with changes in the nucleotide sequence in the HV2 hypervariable region to the group without changes is presented in Figure 4. ## 3.8. Nucleotide Coding Sequence Changes in 12S RNA and RR Monitoring Changes in the nucleotide sequence in the 12S RNA coding region were associated with the modification of the parameters of the monitored RR in their presence, but only in a way close to statistical significance. Maximum daytime and nighttime systolic RR and daytime heart rate were slightly higher in the group of subjects with nucleotide sequence changes in the coding region of the 12S RNA compared to the corresponding values in the group without these changes. The differences in each case were close to statistical significance. The other parameters of the monitored pressure showed no differences between these groups. These data are presented in Figure 5. ## 3.9. MtDNA Nucleotide Sequence Changes and Morning Rises in Blood Pressure Nucleotide sequence changes in the HV1 and HV2 regions were associated with a slightly more frequent occurrence of morning increases in blood pressure, but without statistical significance. Both changes in the HV1 and HV2 regions separately and in the region of the 12S RNA coding sequence were not associated with more frequent morning increases in RR. ## 3.10. Nocturnal Drops in Blood Pressure and Changes in mtDNA Nucleotide Sequence The percentage of reduction in systolic blood pressure at night in the premenopausal group with mtDNA nucleotide sequence changes was $15.7\%$ and in the postmenopausal group with mtDNA changes was $11.1\%$, and the difference was not statistically significant, $$p \leq 0.180.$$ The frequency of the subgroups of hypertension—dippers, extreme dippers, reverse dippers, and non-dippers—was not dependent on changes in the nucleotide sequence in mtDNA. The difference in the incidence of these subgroups of hypertension in the subjects (pre- and postmenopausal) in the group with mtDNA nucleotide sequence changes compared to the group without these changes was statistically insignificant ($$p \leq 0.116$$). These data are presented in Table 5. ## 4.1. Rationale of the Study The demographic structure of European societies has changed dramatically over the past decades. Data from 2020 show that $20.6\%$ of people in European countries are over 65 [30]. The population aged 80 years or above in the EU’s population is projected to have a 2.5-fold increase between 2020 and 2100, from $5.9\%$ to 14.6. This fact changes the tasks of medical care in European societies. Thus, women live nearly 10 years longer than men. The process of individual aging is easier to define in women due to the presence of menopause, which takes a woman from the period of full life and reproductive activity to the postmenopausal period leading to old age. Degenerative diseases, cardiovascular diseases, metabolic diseases, and neoplastic diseases occurring with the aging process determine the quality of life later in life. They have their genesis in molecular changes, in which mitochondrial dysfunctions play a non-negligible role, which may be related to the growth and accumulation of mutations within the mitochondrial genome [31]. ## 4.2. mtDNA Polymorphism and Its Relation to Diseases and Aging Single-nucleotide polymorphism studies allowed to determine genotypes responsible for a specific disease in monogenic diseases, and recently also genotypes with a high risk of multigene diseases [32,33]. One of the possible consequences of an mtDNA mutation—monogenic diseases—is rare. For example, various mtDNA mutations may be responsible for the monogenic mitochondrial disease—Leber’s hereditary optic neuropathy (LHON)—and $90\%$ of patients have one of them: 11778G/A; 3460G/A; and 14484T/C. These mutations in the population are found with a frequency of $\frac{1}{300}$ [34]. Changes in the nucleotide sequence in mtDNA are easy, easier than in the nuclear genome, which is associated with exposure of the mitochondrial genome to contact with continuously produced reactive oxygen species, as well as reduced mtDNA repair possibilities [35,36]. More frequent occurrence of mtDNA mutations in older age groups of patients was observed by Michikawa et al. [ 37]. Changes in the mtDNA nucleotide sequence occurred in the majority (in $57\%$ of the studied patients) of the studied patients over 65 years of age, which, however, were not observed in the groups of younger patients. With age, the progressive increase in mtDNA mutations as a result of reaction to reactive oxygen species is secondary to lipid oxidation and the modification of mitochondrial proteins in the cell’s respiratory chain [38,39]. MtDNA mutations change the structure of the respiratory chain polypeptides encoded in the mitochondrial genome, reducing mitochondrial metabolic activity and the formation of high-energy compounds [40]. Respiratory chain enzymes encoded by altered mtDNAs disrupt electron transport, increase electron leakage from the respiratory chain, and increase the amount of free oxygen radicals produced, further damaging the mitochondria and creating a vicious circle effect. This mechanism leads to the deterioration of the functioning of organs and tissues during the aging process. Modification of apoptosis signaling secondary to mitochondrial damage has been observed in in vivo and in vitro studies. This thesis is confirmed by the results of studies by Wei et al. on skin fibroblasts [41]. The above-mentioned researchers observed greater disturbances in fibroblast bioenergetics in the elderly compared to younger people, which was assessed on the basis of a higher concentration of hydrogen peroxide, a high level of superoxide dismutase activity, and a decrease in the activity of cytochrome c oxidase, as well as the oxygen consumption rate in the older age group. At the same time, a decrease in pyruvate dehydrogenase (PDH) expression and an increase in lactate dehydrogenase kinase were observed. ## 4.3. Study Subjects and Comparison of Results with Previous Studies Despite the relationships of mtDNA polymorphism with age repeatedly described in the scientific literature, our studies did not show significant differences in age between the groups of women studied with changes in nucleotide sequences in the D-loop and without mtDNA changes. MtDNA changes with age, hence, we studied women over 40 (between 40–60 years old). The restricted age range of the research group may have prevented age disparities between women with mtDNA nucleotide sequence alterations and those without. Disturbances in the physiological functions of mitochondria may depend not only on the direct effect of the mutation on the respiratory chain, but may also occur secondary to the existing multigene disease and dysfunction of the mitochondrial respiratory chain in its course. However, it should be emphasized that certain mtDNA mutations may be beneficial. There are publications regarding changes in nucleotide sequences in mtDNA accompanying longevity. Studies by Kokaze et al. found that the mtDNA 5178 C/A polymorphism, which is associated with longevity, may prevent the onset of diabetes [42]. It has been shown that this genotype reduces the number of mtDNA mutations in oocytes, as well as the rate of mtDNA mutation formation and their accumulation in somatic cells in Japanese centenarians. The mtDNA 5178 C/A polymorphism not only prevents diabetes but is probably responsible for inhibiting the development of myocardial infarction [43]. Zhang et al., in the Italian population, studied the frequency of the C150T mutation located near the sequences responsible for mtDNA heavy strand synthesis. Its occurrence was more frequent in older age groups [44]. It appeared in approximately $17\%$ of people ($\frac{33}{52}$) aged 99–106, while in younger people (aged 18–98) only in $3.4\%$ ($\frac{3}{117}$). Howell et al. and others found that mutations associated with multigenetic illnesses commonly occur in the D-loop region, a 1122 bp non-coding stretch of mtDNA containing two hypervariable regions, HV1 and HV2 [45,46]. This area has higher mtDNA polymorphism than others. Del Bo et al. found more mutations in the HV1 and HV2 hypervariable regions of the D-loop than other mtDNA segments in aged people [47]. Mutations in D-loop mtDNA nucleotide sequences, which occur often, disrupt mitochondrial genome replication and transcription. A single-nucleotide polymorphism in the D-loop and 12S RNA coding sequence of mtDNA was detected in $23\%$ of our respondents. Like the aforementioned authors, we found more frequent changes in the hypervariable segments HV1 and HV2 of the D-loop, which occurred in $21.0\%$ of respondents, somewhat more often in the HV1 segment ($14.0\%$ of respondents) than in HV2. In the HV1 area, $85.7\%$ of mutations were homoplasmic and non-coding, and just one patient implicated nucleotide 16319, the beginning site for mtDNA synthesis and light strand engaged in replication. Nucleotide sequence alterations in the HV2 region were homoplasmic in $90.1\%$ of instances, connected to the transcription factor binding site in CBS3 (conserved block) in $36\%$, and occurred in nearly $30\%$ of responses. The 12S rDNA region has $10\%$ non-coding nucleotides, $90\%$ 1438AG and 750AG, and $20\%$ 930GA. Rydzanicz et al. found polymorphisms in the 12S rDNA region (G709A, G750A, G930A, T1243C, T1420C, and G1438A) at a frequency greater than $1\%$ [48]. This study found two mtDNA polymorphisms in the HV2 hypervariable section of the mitochondrial genome that have not been previously reported. A postmenopausal woman with arterial hypertension and metabolic syndrome had a polymorphism. The non-coding nucleotide 340C/A in the H strand origin region between the DNA replication primer and the CBS3 block was changed. The second nucleotide sequence change included the non-coding nucleotide 362T/C and the conserved CBS3 block. Our postmenopausal control patient showed another polymorphism. It was in the mtDNA 12S RNA coding sequence and associated with the non-coding nucleotide 812A/C. Most of the other alterations in the investigated population include mutations associated with multigene illnesses, coronary artery disease, hypertension, diabetes, and neoplastic diseases, according to the literature [26,49,50]. In multigene diseases, the disease process in such cases is not caused by a single change in nucleotide sequences in mtDNA, but by changes in many genes. The presence of mtDNA polymorphisms is not a prerequisite for clinical symptoms of the disease, and changes in the mitochondrial genome occur only in some patients with clinical symptoms of the disease. Homoplastic mutations, which do not always translate into the phenotype of clinical disease symptoms, are often diagnosed at random. This fact is explained by many authors by the direct influence on the mitochondrial genome of the nuclear genome and the influence of epigenetic factors, while the disclosure of heterozygous mutations is conditioned by the proportion of mutated and normal mtDNA [51,52]. Most polymorphisms occur as a variant of the genotype not associated with the occurrence of a given disease, and changes in nucleotide sequences are often located only in the vicinity of genes responsible for a given disease entity. As a result of changes in mtDNA, the same mutation may cause various sets of clinical symptoms or be asymptomatic. In summary, mitochondrial mutations may cause phenotypic effects that are difficult to predict and may occur as pathogenic or only potentially pathogenic mutations. The study found that alterations in mtDNA in HV1, HV2, and 12S rDNA may impact arterial hypertension by increasing blood pressure day and night and heart rate, suggesting an increased adrenergic system tone in these people. The HV2 segment (239TC, 243AG, 247GA, 250TC, 260GA, 277CT, and 284CT) alterations mostly affected transcription factor binding sites and non-coding nucleotides in the 12S RNA and HV1 coding regions. According to Pejovic et al., the nucleotide sequences in the hypervariable D-loop regions that replicate mtDNA and the degree of transcription factor binding can alter mtDNA synthesis and cell number [53]. The examined women’s HV2 region alterations, which impact mtDNA synthesis and transcription factor binding, may affect blood pressure. Several studies link hypertension to mitochondrial metabolism and free oxygen radicals [49,54]. On the other hand, the authors of experimental studies describe various forms of damage to the mitochondrial respiratory chain, leading to an increase in the production of reactive oxygen species that affect the course of hypertension. The source of reactive oxygen species in blood vessels are vascular endothelial cells, fibroblasts, and vascular smooth muscle, in which NAD (P) H or NADH oxidase (nicotinamide adenine dinucleotide in reduced form) catalyzing the reduction of oxygen causes the formation of O2 - and large amounts of other free radical oxygen. NAD (P) H oxidase activation occurs under the influence of TNF-α, angiotensin, and nitric oxide synthase. Hydrogen peroxide is a vasoactive compound with vasoconstrictor properties. According to Rubanyi et al., peroxygen hydrogen chloride in reaction with nitric oxide can transform into peroxynitrite anion (ONOO–), which reduces the availability of nitric oxide and thus contributes to the development of hypertension [55]. According to Pryor et al., hydrogen peroxide directly affects the opening of potassium and calcium channels, and therefore is also responsible for vasodilation. In turn, nitric oxide synthase is a source of not only NO, but also O2, which reduces the availability of NO [56]. According to the authors cited above, the balance between NO and O2, is essential for the damage to the vessel wall, the state of vascular tone, and the development of arterial hypertension. The production of large amounts of free oxygen radicals activates the tyrosine phosphatase and tyrosine kinase pathways, influences the expression of transcription factors and mitogen-activated protein kinases, and changes the activity of ion channels. ROS directly increases the concentration of calcium ions in the cell, leading to vessel wall dysfunction and remodeling. The changes in mtDNA observed in the group of women we have studied, accompanying higher blood pressure values, are probably the result of damage to the mitochondria and the formation of ROS. Many of the available publications on mitochondrial mutations in studied patients with maternal hypertension refer to Asian populations [57,58,59,60,61]. Various degrees of arterial hypertension recognized in the presence of mutations were observed: mutation 4435A > G, with a $30\%$ reduction in mitochondrial metabolism and mitochondrial tRNA transcription (Met); mutation 4263A > G, located at the site of transcription for isoleucine (5’ end of tRNA (Ile)), which decreased the efficiency of the tRNA replication process by about $46\%$; the 4401A > G nucleotide mutation located directly at the 5’ end of the tRNA (Met) and tRNA (Gln), with a reduction in the mitochondrial translation index and a reduction in the mitochondrial respiratory efficiency index; and mutation T3308C on the dehydrogenase subunit (ND1), in which the translation initiating amino acid-methionine has been replaced with tyronine in ND1, with the alteration of RNA precursor strand processing or destabilization of ND1-mtRNA dehydrogenase. The results of this study and other investigations suggest that alterations in the mtDNA nucleotide sequence may cause arterial hypertension. A shortage of postmenopausal sex hormones may damage the mitochondrial respiratory chain, causing mtDNA alterations and hypertension as a multigene illness. The literature shows multigene alterations in mtDNA coding and non-coding nucleotides in arterial hypertension, comparable to our findings [62]. The function of mtDNA D-loop hypervariable regions in arterial hypertension was examined by Liu et al. Changes in the HV2 area 152T-> C, 182C-> T, and 247G-> A and HV1 segment 16187C-> T, 16189T-> C, 16264C-> T, 16270C-> T, and 16311T-> C predispose to essential hypertension. According to these authors, the development of hypertension does not correlate to the severity of the mutation’s influence on the illness’s clinical picture, and the environment and nuclear gene modifiers in these individuals also modify the mutation’s effect on the disease. In a Japanese population investigation on the association between mtDNA alterations and arterial hypertension, Soji et al. found that the mtDNA genotype 16223TC is more prevalent in hypertensive patients than in those without hypertension and is associated with higher hypertension risk [63]. They found no connection with other genotypes, including C16362T. As in this study, studies on the mitochondrial genome generally focused on single or many gene coding or non-coding agents and their link with disease entities, such as hypertension. ## 4.4. Strength and Limitations of the Study One of the limitations of our research is that it involved women from 40 to 60 years of age, who could be expected to have changes in mtDNA with age. Another issue perhaps would be the small age range of the study group, which meant that the differences in the age of the studied women with changes in mtDNA nucleotide sequences compared to the patients without changes did not occur. The investigators did not investigate other factors leading to hypertension, such as nutrition, which is a drawback of the study. Obesity can induce hypertension and therefore have an impact on changes in the mitochondrial genome sequence. Our discovery may have clinical relevance in that Hormone Replacement Therapy may have a protective effect on mtDNA mutation. Several experimental medicines have already reached the clinical phase with extremely promising findings, yet the likelihood of enrolling patients in clinical trials is limited. ## 5. Conclusions Changes in the HV1 and HV2 segments of mitochondrial DNA are accompanied by a more severe course of arterial hypertension with symptoms of adrenergic stimulation (higher maximum systolic pressure during the day and night, more frequent increases in systolic pressure, more frequent morning increases in blood pressure, and higher average heart rate). 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--- title: Increased SIRT1 Concentration Following Four Years of Selenium and Q10 Intervention Associated with Reduced Cardiovascular Mortality at 10-Year Follow-Up—Sub-Study of a Previous Prospective Double-Blind Placebo-Controlled Randomized Clinical Trial authors: - Trine Baur Opstad - Jan Alexander - Jan Aaseth - Anders Larsson - Ingebjørg Seljeflot - Urban Alehagen journal: Antioxidants year: 2023 pmcid: PMC10045001 doi: 10.3390/antiox12030759 license: CC BY 4.0 --- # Increased SIRT1 Concentration Following Four Years of Selenium and Q10 Intervention Associated with Reduced Cardiovascular Mortality at 10-Year Follow-Up—Sub-Study of a Previous Prospective Double-Blind Placebo-Controlled Randomized Clinical Trial ## Abstract Background: Selenium and coenzyme Q10 (SeQ10) possess antioxidant and anti-inflammatory properties, potentially mediated via Sirtuin1 (SIRT1). We aimed to investigate the influence of a SeQ10 intervention on SIRT1 concentration, with potential interactions with microRNAs. Methods: In this sub-study of a prospective double-blind placebo-controlled clinical trial, healthy subjects (mean age 76 years) were randomized to receive an active treatment ($$n = 165$$, combined 200 µg/day of Se and 200 mg/day of Q10) or a placebo ($$n = 161$$). SIRT1 concentration and microRNAs were measured with ELISA and PCR, respectively. Results: After four years, SIRT1 concentration was increased in the active treatment group, with mean (SD) ng/mL of 469 [436] vs. 252 [162], $p \leq 0.001$, and decreased in the placebo group, 190 [186] vs. 269 [172], $$p \leq 0.002$$, and the differences between the groups were significant ($$p \leq 0.006$$, adjusted). Those who suffered CV death during a 10-year follow-up ($$n = 25$$ and $$n = 52$$ in the active treatment and placebo groups, respectively) had significantly lower baseline SIRT1 concentrations compared to the survivors ($p \leq 0.001$). MiR-130a-3p was significantly downregulated during the intervention and correlated inversely with SIRT1 at baseline (r = −0.466, $$p \leq 0.007$$). Conclusion: The increased SIRT1 concentration after the SeQ10 intervention associated with reduced CV mortality, partly mediated via miR-1303a-3p, suggests that SIRT1 is an additional mediator of the intervention, preventing vascular ageing. ## 1. Introduction It is now well accepted that an optimal supply of the essential trace element selenium (Se) has multiple health-promoting benefits, and supplementation may be beneficial in subjects with low Se levels [1,2,3,4]. With its anti-oxidative and anti-inflammatory effects afforded by a number of selenoproteins, Se has been shown to reduce the harm mediated by reactive oxygen species and to reduce inflammation [2,5,6]. Coenzyme Q10 (ubiquonone) is another known regulator of oxidative stress. Coenzyme Q10 is primarily present in the mitochondria and is a component of the electron transport chain but also acts as a lipophilic antioxidant elsewhere. Coenzyme Q10 supplementation has also been shown to be specifically beneficial in the elderly, as the endogenous production of this enzyme declines with age [7,8,9]. There is an important relationship between Se and coenzyme Q10, as the reduction of ubiquinone to its active form ubiquinol is dependent on the selenoenzyme thioredoxin reductase. Their syntheses are both dependent on a functional mevalonate pathway [10]. The main project was a prospective double-blind randomized placebo-controlled study where an elderly community-living population was given combined Se and coenzyme Q10 or a placebo as a dietary supplement for four years. After a median follow-up time of 5.5 years, a significantly reduced cardiovascular (CV) mortality was observed, with a significantly lower concentration of the natriuretic peptide N-terminal proBNP as a sign of less cardiac wall tension, and a significantly better cardiac function [11]. As the results were surprisingly positive, we wanted to follow our study population also after the intervention was terminated and we could report still significantly reduced CV mortality after 10 and 12 years [12,13]. We also reported beneficial effects on inflammatory markers [14,15], oxidative stress [16], endothelial dysfunction [17] and telomere attrition [18] in elderly Swedish citizens with low Se levels. The beneficial properties of Se seem partly to be mediated by the sirtuin system [2,19], which is a class of nicotinamide adenine dinucleotide (NAD+)-dependent deacetylases involved in metabolism, ageing and longevity [20]. The family of sirtuins contains seven enzymes, of which sirtuin1 (SIRT1) is the most investigated. SIRT1 localizes in the nucleus and cytoplasm and is implicated in the regulation of gene transcription [21]. SIRT1 was previously shown to target the nuclear factor kappa-light-chain-enhancer of activated B-cells (NF-κB) [22], the forkhead box O class (FOXO) transcription factor and p53, thus possessing anti-inflammatory and anti-oxidative properties. It also contributes to genome stability [23]. In the elderly, SIRT1 expression and activity are decreased in several tissues and organs, including the CV system, and the lack of SIRT1 has been suggested as a mediator of CV disease (CVD) [24]. Knowledge on the potential effects of Se on SIRT1 is sparse. The downregulation of sirtuins was observed in peripheral blood mononuclear cells in CVD patients with Se deficiency [19]. Recently, increased liver expression of SIRT1 was reported in binge drinking rats when supplemented with Se [25]. Knowledge on the effect of coenzyme Q10 on SIRT1 is also limited, although the Q10 status seems to influence SIRT1 activity [26], as the decline in hepatic SIRT1 expression in diabetic rats was reversed by coenzyme Q10 administration [27]. A selenium and coenzyme Q10 intervention also seemed to have regulatory effects on microRNAs, which are small, non-coding RNAs, functioning as regulatory molecules in post-transcriptional mRNA and protein translation. Our group has recently reported on significant changes in several circulating microRNAs after such supplementation [28], of which some have been reported to target SIRT1 mRNA [29]. The impact of Se and coenzyme Q10 supplementation on SIRT1 needs to be further explored in a clinical experimental setting. We therefore aimed to investigate the potential effects on circulating SIRT1 of a Se and coenzyme Q10 intervention in elderly Swedish citizens with low levels of Se and any influence this potential SIRT1 change may have on the risk of CV mortality. As microRNAs appear to be important regulators of SIRT1 expression, we also aimed to seek associations between SIRT1 and certain microRNAs, as well as markers of inflammation and endothelial function. ## 2.1. Study Population The present investigation is a sub-study of a previous prospective double-blind randomized placebo-controlled single-center trial performed between 2003 and 2010 in the south-east of Sweden [12]. The study included 443 subjects recruited from a rural municipality of 10,300 inhabitants, and the inclusion criterion was being aged >69 years. Exclusion criteria were recent myocardial infarction, planned CV operative procedure within four weeks, serious disease that substantially reduced survival, or expectation that the participant could not cooperate for the full intervention period [12]. The intervention period was four years; 221 participants received the active supplement, and 222 participants received a placebo. The participants were given either a combination of supplements consisting of yeast tablets containing 200 µg/day of organic Se (SelenoPrecise 100 µg, Pharma Nord ApS, Vejle, Denmark, twice daily) and capsules containing 200 mg/day of coenzyme Q10 (Bio-Quinon 100 mg twice daily, Pharma Nord ApS, Vejle, Denmark) or placebo tablets/capsules. The SelenoPrecise® 100 µg tablet is approved in Denmark as a pharmaceutical drug by the Danish Medicines Agency, and the Q10 capsules were identical to Myoqinon® (Pharma Nord ApS, Vejle, Denmark), which is a pharmaceutical drug authorized in European Union Member States (No. OGYI 11494-2010). The placebo tablets for Se and coenzyme Q10 contained bakers’ yeast only and 500 mg of vegetable oil with 3.1 mg of added vitamin E, respectively. All participants were supplemented for 48 months, and the non-consumed study medications were returned and counted as a measure of compliance. In the present sub-study, samples for SIRT1 analysis at inclusion were available from 326 individuals, of whom 165 received the active treatment, and 161 received the placebo (Figure 1). At 48 months, serum for the SIRT1 analysis was available from 103 and 77 subjects, respectively. At inclusion, all study participants were clinically examined using the assessment based on the New York Heart Association functional class (NYHA class) and with an electrocardiogram and Doppler echocardiography. The study was registered at Clinicaltrials.gov and has the identifier NCT01443780. ## 2.2. Blood Sampling Blood samples were collected under fasting conditions both at inclusion and after 48 months. Routine analyses were carried out by conventional methods. Serum was prepared by centrifugation within 1 h from blood collection at 2.500× g for 10 min and stored at −70 °C until SIRT1 determination. Pre-chilled ethylenediaminetetraacetic acid (EDTA) vials were centrifuged at 3000× g, +4 °C, and EDTA plasma was frozen at −70 °C for the measurement of Se concentration, previously analyzed by inductively coupled plasma mass spectrometry (ICP-MS) [3]. ## 2.3. SIRT1 Analysis The Human SIRT1 ELISA kit from LSBio LifeSpan BioSciences lnc., Seattle, WA, USA, was used for the SIRT1 analysis, performed in serum at baseline and after 48 months. The samples collected at both time points from the same individual were analyzed on the same ELISA plate to minimize assay variability between runs. SIRT1 was successfully measured in all available samples, and the inter-assay coefficient of variation was $13.5\%$. ## 2.4. MicroRNA Analysis The data on microRNA profiling after Se/Q10 intervention were retrieved from previous analyses [28]. In short, 25 participants from each randomized group in the main Se/coenzyme Q10 intervention trial [12] were evaluated regarding the levels of 145 microRNAs in the serum [28]. In the present study, approximately 30 samples were available for the interaction analyses with SIRT1, and 126 microRNA were analyzed. ## 2.5. Statistical Methods The descriptive data are presented as percentages or mean ± standard deviation (SD). For continuous variables, a Student’s unpaired two-sided t-test was used, and the chi-square test was used for the analysis of one discrete variable. A slight non-Gaussian distribution of the dataset could be seen, and therefore, the dataset was log-transformed when evaluating continuous variables to obtain a normal distribution. The effect of this transformation was controlled through a Kolmogorov–Smirnov test. Therefore, transformed data were used in the t-test evaluations. All evaluations were performed according to the intention-to-treat principle. Repeated measures of variance were used to assess individual changes in the concentrations of SIRT1. In the analysis of covariance (ANCOVA), both transformed and non-transformed data were applied, with no significant difference in the results. In the multivariable model, the SIRT1 level after 48 months was used as the dependent variable. Adjustments were made for age, C-reactive protein (CRP) fold change, SIRT1 concentration at inclusion, smoking, gender, hypertension, diabetes, ischemic heart disease (IHD) and active treatment. In the correlation analyses between certain circulating microRNAs and serum SIRT1 concentration, non-parametric correlation methods were applied (Spearman RhO). p-values < 0.05 were considered statistically significant, based on a two-sided evaluation. All data were analyzed using standard software (Statistica v. 13.2, Dell Inc., Tulsa, OK, USA). ## 3. Results The baseline characteristics of the study population, divided into an intervention with active substances group and a placebo group, are shown in Table 1. The mean age of the total population was 76 years, and $48.8\%$ of it were females. At inclusion, no significant differences in the clinical characteristics were observed between the randomized groups. No participants presented with NYHA functional class IV, which indicates symptoms also at rest. The plasma Se concentration at inclusion was below the required amounts (~110 μg/L) for the optimal expression of selenoproteins [30,31] and did not differ between the randomized groups, with mean (SD) of 67.4 (14.2) μg/L for the active treatment group and 67.2 (13.2) μg/L for the placebo group, $$p \leq 0.80$$). At baseline, SIRT1 was inversely correlated with the inflammatory markers CRP (r = −0.43, $p \leq 0.001$), P-selectin (r = −0.11, $$p \leq 0.042$$) and osteopontin (r = −0.30, $$p \leq 0.02$$). We also found a significant correlation between the von Willebrand factor, a biomarker also for endothelial function, and SIRT1 ($r = 0.52$, $$p \leq 0.01$$). However, no significant correlation between Se concentration and levels of SIRT1 could be found at baseline ($p \leq 0.05$). ## 3.1. SIRT1 Concentration in Relation to the Se and Coenzyme Q10 Intervention No significant difference in SIRT1 levels between the active treatment group and the placebo group was observed at baseline, with mean (SD) of 252 [162] ng/mL vs. 269 [172] ng/mL, $$p \leq 0.36.$$ After the intervention, the SIRT1 levels increased significantly in the active treatment group, reaching 469 [436] ng/mL, $p \leq 0.001$), whereas the SIRT1 levels decreased in the placebo group to 190 [186] ng/mL, $$p \leq 0.002.$$ The difference in change from baseline to 48 months between the two randomized groups was significant ($$p \leq 0.03$$, applying repeated measures of variance) (Figure 2). When adjusting for age, sex, hypertension, smoking, diabetes, IHD, change in CRP levels and SIRT1 level at baseline in a multivariable model, significantly higher SIRT1 levels could still be observed at 48 months in the active group compared with the placebo group, ($$p \leq 0.006$$, Table 2). ## 3.2. SIRT1 Changes as Related to CV Mortality Ten years after the study, a total of 77 CV deaths were registered, 25 ($15\%$) in the active treatment group and 52 ($32\%$) in the placebo group ($p \leq 0.001$). At the study start, the SIRT1 levels were already higher in the group that ended up as CV survivors compared to that of CV deaths, with mean (SD) of 319 [209] ng/mL vs. 242 [148], $p \leq 0.001.$ To take into account the individual change in SIRT1 concentration, repeated measures of variance were applied to the study population divided into survivors and those who suffered CV death during a follow-up time of 10 years. We observed that the significantly higher SIRT1 levels in the survivor group persisted and that the difference in this change between the two randomized groups was significant ($$p \leq 0.016$$) (Figure 3). Then, we evaluated the active treatment group and placebo group separately. In the placebo group, significantly lower concentrations of SIRT1 were observed in those who suffered CV mortality than in the survivors, i.e., 59 [33] ng/mL vs. 196 [164] ng/mL, $$p \leq 0.01.$$ In the active treatment group, the SIRT1 concentration in the survivor group was higher than in the CV mortality group, i.e., 263 [178] ng/mL vs. 153 [54] ng/mL; however, these figures were not significantly different ($$p \leq 0.17$$). The lack of statistical difference was probably due to a highly restricted sample size in this sub-analysis. ## 3.3. Association between Circulating SIRT1 and microRNAs at Baseline Of the 126 analyzed microRNA, 9 were significantly associated with SIRT1, of which 5 were inversely correlated (Table 3). Three of these microRNAs (bolded) have previously been reported to target SIRT1. ## 4. Discussion The main finding of this investigation is that a four-year intervention with combined Se and coenzyme Q10 significantly increased the serum concentration of SIRT1, and this elevation was associated with reduced CV mortality. This is, to the best of our knowledge, the first time such an influence on SIRT1 has been reported in a clinical setting. We suggest that the observed increase in SIRT1 operates as a mediator and thus contributes to protection against vascular ageing and atherosclerosis. The mechanisms of SIRT1 elevation and protective actions are discussed in the following paragraphs. ## 4.1. Effects of the Se/CoQ10 Intervention on SIRT1 Se has multiple health-promoting properties, especially when given as a supplement to the elderly and in general to subjects with a low Se intake, the latter being frequently encountered in European countries due to the low Se concentration in the soil [1]. Dietary Se is incorporated into selenoproteins and, together with coenzyme Q10, these components play an important role in the body’s redox regulation and antioxidant defense. The beneficial effects of Se seem partly mediated by sirtuins, mainly by enhancing SIRT1 anti-inflammatory effects [2,19]. The observed significant and independent rise in SIRT1 may impact several biomolecules and signaling pathways. SIRT1 deacetylates multiple targets, including histones, leading to reduced transcription of the corresponding DNA, and non-histone proteins, including NF-κB, FOXO transcription factors, p-53, peroxisome proliferator-activated receptor-gamma coactivator (PCG)-1α and endothelial nitric oxide synthase (eNOS) among others. Hence, SIRT1 is implicated in multiple cellular processes such as metabolism, redox state, DNA transcription and repair, maintenance of genomic stability, apoptosis and organism lifespan [20,23,24,32]. Whether circulating SIRT1 reflects all these intracellular processes is not clear [24]. A possible rise in SIRT1 expression or circulating levels, and also its increased activity, does not necessarily reflect the same condition, and eventual differences also seem to be dependent on the type of tissue, organ, disease and the actual mechanism involved [33]. Increased compensating SIRT1 gene expression may also reflect low intracellular SIRT1 activity. To exemplify this controversy, high levels of SIRT1 concentration have been associated with non-alcoholic fatty liver disease, probably due to compensatory mechanisms [34]. We recently reported reduced circulating SIRT1 levels after bariatric surgery 6 and 12 months after the procedure, probably due to the loss of adipose tissue and consequently the reduced expression of SIRT1 mRNA in this site [35]. In the same study, we also showed that the levels of triglycerides were inversely predictive of SIRT1 levels. Recently, a meta-analysis of randomized controlled trials reported that coenzyme Q10 markedly reduced triglycerides, which again might explain the rise in SIRT1 after Se/Q10 supplementation [36]. Our group also previously showed that mRNA SIRT1 expression in circulating leukocytes was significantly reduced in type 1 diabetes patients, which was accompanied by elevated serum SIRT1 levels in diabetes patients with coronary heart disease compared to patients without this condition [37]. We believe that the rise in circulating SIRT1 concentrations in the present study is “genuine” due to the beneficial effects of the intervention. ## 4.2. Effects of SIRT1 in the CV System SIRT1 seems also to be involved in cardiac metabolism and health, but the influence of Se on SIRT1 in CVD has been sparsely reported. However, recently, it was reported that SIRT1 has a major regulatory function in hypoxia-induced oxidative stress in cardiomyocytes [38]. Circulating SIRT1 was also previously shown to be reduced in elderly Italians with CVD in the presence of Se deficiency [19]. In addition, in a review, Packer et al. reported the cardioprotective effects of SIRT1, which would especially benefit heart failure patients [39], and Shengyu et al. reported a positive relationship between Se and the expression of SIRT1, evidencing a protective effect in cardiac hypertrophy [40]. Circulating SIRT1 was further reported to be inversely associated with epicardial fat thickness in obese subjects [41], and the plasma levels of SIRT1 were lower in patients with acute cerebrovascular stroke compared to controls, with no significant difference between ischemic and hemorrhagic groups [42]. Reduced levels were observed in pregnant women developing preeclampsia compared to women with healthy pregnancies, possibly due to increased oxidative stress, endothelial impairment and a reduction in SIRT1 expression [43]. We found that SIRT1 concentration at 48 months was higher in all survivors in comparison with the subjects in the CV mortality group, and the difference in the change from baseline between the groups was highly significant. The lower SIRT1 levels in those who suffered CV mortality within 10 years compared to those in the survivors at study inclusion could be explained by the fact that the negative development ending up with CV mortality had begun long before the study started. ## 4.3. Potential Mechanisms of Increased SIRT1 for Cardiac Protection SIRT1 activity is regulated by NAD+ availability, and the NAD+ levels are thought to decrease with age [44]. Previous studies indicated that the Se/coenzyme Q10 intervention can increase the levels of NAD+. Se-methylselenocysteine, a naturally occurring organoselenium compound found in many plants and selenized yeast [45], was found to restore the NAD+ levels in human mammary epithelial cells when exposed to carcinogens [46]. The endogenous production of coenzyme Q10 also seems to decline with age, at least in some tissues [7,8,9]. With coenzyme Q10 deficit, the cytoplasmic and mitochondrial NAD+/NADH ratio is reduced. Coenzyme Q10 deficiency has also been found to lower SIRT1 mRNA expression [26]. Thus, a potential rise in NAD+ and the NAD+/NADH ratio by the Se and coenzyme Q10 intervention might have raised SIRT1 activity in CV cells, ensuing cardioprotective effects. SIRT1 is highly expressed in endothelial cells, although reduced in endothelial cells from older human arteries compared to those from younger adults [47]. Thus, the rise in SIRT1 after the intervention may have improved the vascular endothelial function via eNOS and increased NO production. The rise in SIRT1 may also have reduced the inflammatory signaling via NF-κB inhibition, resulting in reduced cytokine production in cardiac cells. The observed increase in genomic stability by our group, shown by the stabilization of telomeres after the Se/Coenzyme Q10 intervention [18], may also partly be mediated by SIRT1 through telomere reverse transcriptase induction [48]. With an elevation of the SIRT1 levels, a potential increased deacetylation of FOXO transcription factors and PGC-1α is thought to induce anti-oxidative enzymes, including glutathione peroxidase 1 (GPx1) and selenoprotein P (selenop) [49], thereby lowering cellular and extracellular oxidative stress [32]. We previously reported on the reduced levels of two oxidative stress biomarkers, adrenomedullin (MR-proADM) and copeptin, as an effect of the Se/Q10 intervention [16], indicating its beneficial influence on redox regulation, with a potential impact on SIRT1 activity. Additionally, an elevated SIRT1 concentration may also suppress the formation of foam cells, as SIRT1 has the ability to reduce the uptake of oxidized LDL and increase the reverse cholesterol uptake, thereby preventing plaque progression [32]. ## 4.4. Regulation of SIRT1 by microRNAs The regulation of SIRT1 synthesis may also include epigenetic regulation via microRNAs [50]. Recently, an increase in miR-130 was reported to affect SIRT1 mRNA in organismal and skin ageing [51]. In the study of microRNAs published by Alehagen et al. [ 28], the baseline Se concentration was observed to be inversely correlated with the expression of miR-130a-3p, and miR-130a-3p was significantly downregulated in the active treatment group compared to the placebo group at 48 months after the intervention. We observed that miR-130a-3p was significantly and inversely associated with SIRT1 at inclusion, suggesting that a decrease in this microRNA upon intervention might have contributed to the observed rise in SIRT1. MiR-222-3p has also recently been reported to target SIRT1 in cancer, arthritis and non-alcoholic fatty liver disease [52,53,54]. We observed that SIRT1 was also inversely correlated with miR-122-3p at baseline; however, the influence of the intervention on this relationship is less clear, as miR-122-3p was observed upregulated at 48 months [28]. MiR-181, defined to be a target of SIRT1 [29], was positively correlated with SIRT1 but was not markedly changed after the intervention, which is somewhat difficult to explain. That said, we cannot exclude a false observation due to the limited number of samples. In CVD, miR-199,has been shown to be upregulated in states of hypoxia with association to atherosclerosis and heart failure [50]. The previously reported SIRT1 downregulation during acute ischemia [24] might involve such SIRT1 regulation. We have previously reported that miR-199 was one of the most downregulated microRNAs after four years of Se/coenzyme Q10 supplementation [28], which also could have contributed to the rise in SIRT1 concentration, although no significant association with SIRT1 was observed. The miRs miR-34 and miR-133 known to target SIRT1 were also not associated with SIRT1 in the present study. ## 4.5. Limitations Although highly significant and clear results were achieved overall, the limited number of participants in the sub-group analyses may have been inadequate, and therefore we may have failed to detect potential associations (statistical error Type II). Another limitation is the lack of SIRT1 mRNA measurements, which could have strengthened our results. As RNA sampling was not performed initially, SIRT1 gene expression analysis could unfortunately not be accomplished. Except for the interaction between SIRT1 and miR-130a-3p, which underlines potential mechanisms of the Se/coenzyme Q10 intervention, the associations between SIRT1 and other investigated microRNAs in this context warrant further investigation. In addition, the differential influence of Se and coenzyme Q10 on SIRT1 was not possible to be explored in the present study. ## 5. Conclusions After four years of supplementation with combined Se and coenzyme Q10, we found SIRT1 concentration to be significantly increased, which was potentially mediated by miR-130a-3p downregulation, among other microRNAs, ensuing CV protection with a significant reduction in CV mortality. The importance of Se and coenzyme Q10 in the prevention of CVD and the role of SIRT1 in this context highlight the beneficial effects of SIRT1 on CV functions, suggesting SIRT as a target for potential prevention. ## References 1. Rayman M.P.. The importance of selenium to human health. Lancet (2000) 356 233-241. 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--- title: Caffeic Acid Phenethyl Ester Suppresses Oxidative Stress and Regulates M1/M2 Microglia Polarization via Sirt6/Nrf2 Pathway to Mitigate Cognitive Impairment in Aged Mice following Anesthesia and Surgery authors: - Yue Wang - Ziwen Cai - Gaofeng Zhan - Xing Li - Shan Li - Xuan Wang - Shiyong Li - Ailin Luo journal: Antioxidants year: 2023 pmcid: PMC10045012 doi: 10.3390/antiox12030714 license: CC BY 4.0 --- # Caffeic Acid Phenethyl Ester Suppresses Oxidative Stress and Regulates M1/M2 Microglia Polarization via Sirt6/Nrf2 Pathway to Mitigate Cognitive Impairment in Aged Mice following Anesthesia and Surgery ## Abstract Postoperative cognitive dysfunction (POCD) is a severe neurological complication after anesthesia and surgery. However, there is still a lack of effective clinical pharmacotherapy due to its unclear pathogenesis. Caffeic acid phenethyl ester (CAPE), which is obtained from honeybee propolis and medicinal plants, shows powerful antioxidant, anti-inflammatory, and immunomodulating properties. In this study, we aimed to evaluate whether CAPE mitigated cognitive impairment following anesthesia and surgery and its potential underlying mechanisms in aged mice. Here, isoflurane anesthesia and tibial fracture surgery were used as the POCD model, and H2O2-induced BV2 cells were established as the microglial oxidative stress model. We revealed that CAPE pretreatment suppressed oxidative stress and promoted the switch of microglia from the M1 to the M2 type in the hippocampus, thereby ameliorating cognitive impairment caused by anesthesia and surgery. Further investigation indicated that CAPE pretreatment upregulated hippocampal Sirt6/Nrf2 expression after anesthesia and surgery. Moreover, mechanistic studies in BV2 cells demonstrated that the potent effects of CAPE pretreatment on reducing ROS generation and promoting protective polarization were attenuated by a specific Sirt6 inhibitor, OSS_128167. In summary, our findings opened a promising avenue for POCD prevention through CAPE pretreatment that enhanced the Sirt6/Nrf2 pathway to suppress oxidative stress as well as favor microglia protective polarization. ## 1. Introduction Postoperative cognitive dysfunction (POCD), a severe neurological complication after anesthesia and surgery, with a high prevalence in the elderly, prolongs hospital stays and raises disability and mortality [1,2,3,4,5]. Worldwide, the aging population grows rapidly, and as a result, the demand for effective treatments of POCD is increasing. Unfortunately, there is currently no effective clinical pharmacotherapy to prevent or cure POCD due to its unclear pathogenesis. Thus, it is essential and urgent to explore the exact mechanisms and develop effective therapeutic strategies of POCD. Microglia, diffusely distributed throughout the brain, are involved in lots of major innate immune functions to sustain homeostasis in the central nervous system. Increasing evidence from recent studies indicated that microglia could respond to various stimuli and stress by releasing inflammatory factors and removing debris [6,7,8]. During the process of neurodegenerative diseases, microglia will be activated and dynamically change their morphology to classic (M1) or alternative (M2) activation phenotypes. Although the transformation of microglia is a continuous process and the supposed dichotomy between M1 and M2 phenotypes is now recognized as an oversimplification, this classification remains useful for understanding the function of microglia in various brain diseases [9,10,11]. Specifically, M1 phenotype microglia causes neuronal damage by releasing proinflammatory mediators. On the contrary, M2 phenotype microglia release anti-inflammatory mediators, thereby exerting beneficial effects [12,13,14]. Thus, promoting microglial activation toward the M2 phenotype appears to be a meaningful strategy for POCD treatment. Sirtuin 6 (Sirt6) belongs to the sirtuin family of class III histone deacetylases dependent on nicotinamide adenine dinucleotide. Accumulating evidence has shown that Sirt6 participated in DNA repair, inflammation, senescence, and lipid metabolism, especially, and was also involved in aging and age-related diseases [15,16,17,18,19]. Additionally, it has been reported that the upregulation of Sirt6 could cause mice to show a drop in mature neurons and an increase in immature neurons, without disrupting glial differentiation [20]. In addition, energy restriction-induced upregulation of Sirt6 could inhibit microglial activation and enhance angiogenesis by suppressing TXNIP in cerebral ischemia [21]. Moreover, Tailin et al. reported that priming Sirt6 suppressed microglial activation, which in turn reduced LPS-induced neuroinflammation and brain ischemia injury [22]. As known, nuclear factor erythroid 2-related factor 2 (Nrf2) is a vital substrate of Sirt6, which could be activated to protect the body against oxidative damage via scavenging reactive oxygen species (ROS) [23,24]. However, whether the Sirt6/Nrf2 signaling pathway participates in the neuropathological mechanisms of POCD is still unknown. Caffeic acid phenethyl ester (CAPE) is an activated phytochemical obtained from propolis and numerous medicinal plants. It displays vast properties, particularly antioxidation, anti-inflammation, and anti-apoptosis [25,26]. Several studies revealed a potential protective effect of CAPE for neurodegenerative diseases [27,28]. Specifically, it was revealed that CAPE alleviated cognitive impairment by modulating the activity of glycogen synthase kinase 3 beta in AD mice [29]. At present, the efficacy of CAPE in POCD mice and its potential underlying mechanisms remain to be elucidated. Accordingly, our study aimed to evaluate the neuroprotective benefits of CAPE on aged POCD mice and its potential underlying mechanisms. We explored whether CAPE could mitigate microglia-mediated oxidative stress and favor microglia-protective polarization to alleviate POCD. Furthermore, we examined the role of the Sirt6/Nrf2 pathway induced with CAPE in combating oxidative stress and favoring microglia protective polarization in vivo and in vitro. ## 2.1. Animals A total of 66 male C57BL/6J mice (20 months old, weighing 30 to 35 g) were provided by the Laboratory Animal Center of Tongji Medical College, housed in a standard room (12 h light/dark cycle, 45–$65\%$ humidity, 22–25 °C temperature, water and food ad libitum). In our study, the mice were assigned into three groups: [1] Naive; mice received no treatment ($$n = 22$$); [2] A + S + Vehicle; mice only received an equivalent volume injection of solvent before anesthesia and surgery ($$n = 22$$); [3] A + S + CAPE (Selleck, S7414, Houston, TX, USA); mice were intraperitoneally injected with CAPE (i.p., 10 mg/kg) before anesthesia and surgery for 10 consecutive days ($$n = 22$$) [29] (Figure 1). CAPE was uniformly dissolved in $10\%$ dimethyl sulfoxide, $40\%$ PEG300, and $50\%$ saline. The vehicle was a mixture of $10\%$ dimethyl sulfoxide, $40\%$ PEG300, and $50\%$ saline. The number of animals for each group was predetermined according to numbers reported in published studies or our prior experiment, and accurate sample sizes (n) indicated in figure legends refer to the number of animals [30,31,32,33,34,35,36,37]. Notably, efforts were made to minimize animal suffering and the number of animals used. ## 2.2. Establishment of POCD Model Anesthesia and surgery are usually performed in mice or rats to establish the POCD model [38,39,40,41,42,43,44,45]. Referring to published studies and our previous studies, we used isoflurane anesthesia and tibial fracture surgery as the POCD model [38,39,45,46,47]. Specifically, after one week of acclimatization, under isoflurane anesthesia, surgery, including a tibial fracture and intramedullary fixation, was performed on mice. After skin disinfection, the left tibia was revealed, fixed with a 0.3 mm pin, and then osteotomized. Afterward, a 5-0 Vicryl thread was used to suture the incision, and the incision was locally infiltrated with $0.5\%$ bupivacaine (1 mg/kg). Subsequently, lidocaine cream was locally applied twice daily for 3 days post-surgery for incision pain. Additionally, we used a heating blanket to maintain the temperature (37 ± 0.5 °C) during the procedure. Two people completed isoflurane anesthesia and tibial fracture surgery in mice. ## 2.3. Behaviors Assessment All of the behavioral tests were performed in a dark, quiet, and soundproof room with a comfortable temperature. All behavioral measurements were recorded using a tracking system (Zongshi Technology, Beijing, China). Each behavioral test was performed by two people who were blinded to the groups. ## 2.3.1. Open Field Test (OFT) The mouse was put in the neutral zone of the white chamber (50 cm × 50 cm × 50 cm) and then permitted to explore freely within 5 min. The box was sprayed with alcohol between the individual mice to eliminate odor interference. The total distance (cm) moved was measured. ## 2.3.2. Y-Maze Test (YMT) The YMT was carried out to assess short-term spatial working memory [48,49]. The apparatus was composed of three white acrylic arms (40 cm × 5 cm × 10 cm) separated at 120° angles. Each mouse was gently put in the distal end of an arm, and then the arm entries were recorded over 8 min. The percentage of spontaneous alternation (%SA) was calculated: %SA = [(number of alternations)/(total arm entries − 2)] × 100. Mice with less than 15 total alternations during the test were not taken into the final data. ## 2.3.3. Morris Water Maze Test (MWMT) The MWMT was carried out to assess long-term spatial memory function [48]. Briefly, the circular pool (diameter 1.2 m, height 50 cm) was filled with water (38-cm depth) and white tempera paint was evenly mixed to the water. A hidden white platform (diameter 10 cm) was immersed 1 cm beneath the water. The test was composed of three trials every day for 5 consecutive days and a probe test. Specifically, the mouse was gently released and permitted to search for the platform within 1 min. Mice that successfully found the platform would stay on the platform for 15 s, and mice that failed were guided to remain for the same 15 s. The time spent reaching the hidden platform (escape latency) was measured. On the sixth day, a 60-s probe trial was performed to assess reference memory, in which the platform was removed. The number of platform crossings after removing the platform was determined [50,51,52,53]. ## 2.4. Reactive Oxygen Species (ROS) in the Hippocampus The ROS levels were measured using the fluorescent probe, dihydroethidium (Beyotime, S0063, Shanghai, China). Dihydroethidium (DHE), a fluorescent probe, could be dehydrogenated with reactive oxygen species (ROS) to produce ethidium, and then ethidium could bind to RNA or DNA to produce red fluorescence. The intensity of red fluorescence is proportional to the ROS levels. Thus, the detection of red fluorescence could determine the ROS level. Following behavioral tests, mice were perfused transcardially with PBS under anesthesia, and then the brains were removed and frozen rapidly. Subsequently, the brains were sliced into 20-μm slices in a freezing microtome (Leica, CM1900, Wetzlar, Germany). Referring to published studies, frozen hippocampal sections were incubated with 5 μM dihydroethidium at 37 °C for 30 min [54,55]. Fluorescently labeled samples were imaged with a CaiZeiss confocal microscope (CaiZeiss, LSM800, Wetzlar, Germany). Specifically, the objective selected was Plan-Apochromat 20 × /0.80 Ph 2 M27, the laser power was 561 nm $0.60\%$, the detector gain was 643 V, and the field’s width of vision was 638.9 μm. In addition, three slides were used for analysis per mouse. ## 2.5. Oxidative Stress Indicators in Mice Plasma Following behavioral tests, blood was collected via thoracotomy under anesthesia, and then plasma was obtained via centrifuging at 2000 rpm for 10 min. Subsequently, the plasma levels of catalase (CAT), malondialdehyde (MDA), glutathione (GSH), and superoxide dismutase (SOD) were measured using corresponding biochemical assay kits (Jiancheng Biochemical, A001-1, A006-1, A007-1, A003-1, Nanjing, China). The specific methods were performed according to the instructions. ## 2.6. Cells BV2 cell line was applied to study microglia in our in vitro studies. BV2 cells were plated in cell culture plates (Corning Costar, 3516, Cambridge, MA, USA) and cultured with DMEM (Gibco, C119955000BT, Billings, MT, USA) containing $10\%$ fetal bovine serum (Gibco, 10099, Billings, MT, USA). Next, the cells were firstly divided into two groups: [1] CON; cells were not treated; [2] H2O2 (Sigma, 18304, St. Louis, MO, USA); cells were treated with H2O2 (100 μM) for 24 h. Subsequently, we divided the BV2 cell populations into four groups in the further experiment: [1] CON; cells were not treated; [2] H2O2; cells were treated with H2O2 (100 μM); [3] H2O2 + CAPE; cells were pretreated with CAPE (20 μM) for 24 h and subsequently treated with H2O2 (100 μM); [4] H2O2 + CAPE + OSS_128167 (Selleck, S8627, Houston, TX, USA); cells were pretreated with CAPE (20 μM) and OSS_128167 (20 μM) [56] for 24 h, and subsequently treated with H2O2 (100 μM) (Figure 1). Sample sizes (n) indicated in figure legends refer to the number of biologic replicates. ## 2.7. Proliferation and Viability Assay of BV2 Cells Exposed to H2O2 and CAPE To determine the maximum safety concentrations of H2O2 and CAPE, we performed a Cell counting kit8 (Beyotime, C0038, Shanghai, China) assay and live/dead assay (Supelco, 3106135, Bellefonte, PA, USA). Briefly, BV2 cells were seeded with three replicates per sample. When the detection time point was reached, cells were incubated with CCK8 solution for 3 h at 37 °C in $5\%$ CO2 conditions after being washed. The absorbance at 450 nm was measured using a reader (Thermo Fisher, Multiskan FC, Waltham, MA, USA). For further viability detection, cells were seeded in the confocal dishes and treated with different concentrations of H2O2 or CAPE. After rinsing with DPBS, cells were incubated with Calcein-AM and PI for 30 min in the dark at 37 °C under $5\%$ CO2 conditions. Images were taken with a microscope (Carl Zeiss, AXIO observer 7, Oberkochen, Germany) and analyzed using ZEN software (Carl Zeiss, Oberkochen, Germany). ## 2.8. Reactive Oxygen Species (ROS) in BV2 Cells The ROS levels were measured with a flow cytometer (BD Biosciences, FACSCanto II, San Jose, CA, USA) using a ROS assay kit (Beyotime, S0033M, Shanghai, China). In short, cells were seeded, exposed to various concentrations of H2O2 and CAPE, and then incubated with DCFH-DA indicator for 30 min at 37 °C. Flowjo software was utilized to analyze the data. ## 2.9. Flow Cytometry (FCM) As previously mentioned, flow cytometry was performed. In brief, firstly, cells were digested by accutase. After being washed with DPBS, a membrane-breaking fixative solution (BD Bioscience, 554714, San Jose, CA, USA) was used to fix for 15 min. Flow cytometry antibodies PE CD86 (R & D Systems, FAB741P, Minneapolis, MN, USA) and APC CD206 (R & D Systems, FAB2535A, Minneapolis, MN, USA), Rat IgG2A PE-conjugated Antibody (R & D Systems, IC006P, Minneapolis, MN, USA) and Goat IgG APC-conjugated Antibody (R & D Systems, IC108A, Minneapolis, MN, USA) were used in accordance with the instructions. Isotype controls were used in all analyses. Flow cytometry measurements were performed with FACS Calibur (BD Biosciences, FACSCanto II, San Jose, CA, USA) and analyzed using Flowjo software. All antibodies are listed in Table 1. ## 2.10. Immunofluorescence (IF) Mice were perfused transcardially with PBS and $4\%$ paraformaldehyde 24 h after anesthesia and surgery. Brains were excised completely, fixed with $4\%$ paraformaldehyde, and immersed in $30\%$ sucrose to dehydrate. Subsequently, the brains were sliced into 20-μm slices in a freezing microtome (Leica, CM1900, Wetzlar, Germany). Similarly, cells were first fixed with $4\%$ paraformaldehyde. After being penetrated with $0.2\%$ Triton X-100 and blocked using $5\%$ donkey serum, the samples were incubated with appropriate primary antibodies, corresponding secondary antibodies, and DAPI successively. Fluorescently labeled samples were imaged with a CaiZeiss confocal microscope (CaiZeiss, LSM800, Oberkochen, Germany). Quantitative analysis of immunofluorescence staining images was performed by a blinded investigator using ZEN software. Specifically, we opened the original image file with ZEN software, circled Iba1+ microglia using the rectangle tool, and then obtained the average intensity of red light-labelled Sirt6 or Nrf2 using the measurement function. Additionally, three slides were used for each mouse, and ten microglia were randomly selected from each region for quantitative analysis per piece. The mean value was taken to represent the fluorescence intensity of Sirt6 or Nrf2 in the microglia. All primary and secondary antibodies are listed in Table 1. ## 2.11. Real-Time Quantitative PCR (RT-qPCR) Total RNA was extracted from hippocampi and cells using an RNA extraction kit (FORGENE, RE-O3113, Beijing, China). Subsequently, reverse transcription was performed using HiScript III-RT SuperMix (Vazyme, R323-01, Nanjing, China). Standard RT-qPCR was performed with the ChamQ Universal SYBR qPCR Master Mix (Vazyme, Q711-02, Nanjing, China) on the Step One Plus thermal cycler (Applied Biosystems, Mississauga, ON, Canada). For the design of gene-specific primers, we first searched and selected primers sequence with validation results in Primerbank. When the primers could not be retrieved from Primerbank, the NCBI website was used for primer design, and then the designed primers were blasted to verify the specificity. Only the primers specific to the target genes were selected in this study. All primers are listed in Table 2. ## 2.12. Statistical Analysis All data are shown as the mean ± standard error of the mean (SEM). Comparisons of results between the two groups were accessed with a t-test. Comparisons of results among multiple groups were accessed via one- or two-way ANOVA, followed by a Tukey post hoc test. The non-normally distributed data of platform crossing times were accessed using the Kruskal–Wallis nonparametric test and Dunnett’s post hoc test. A p-value below 0.05 was represented as statistically significant (* $p \leq 0.05$; $p \leq 0.01$; * $p \leq 0.001$; ** $p \leq 0.001$; NS regarded as not significant). GraphPad Prism software 9.0 was used to analyze the data. ## 3.1. CAPE Pretreatment Ameliorates Cognitive Dysfunction following Anesthesia and Surgery To investigate whether CAPE pretreatment could ameliorate cognitive dysfunction following anesthesia and surgery, we randomly divided the 20-month mice into three groups: one receiving nothing, one receiving the vehicle, and another one receiving CAPE before anesthesia and surgery. Until the fifth day after anesthesia and surgery, the behavior tests were carried out (Figure 1). Firstly, OFT was performed to evaluate whether each group’s locomotor activity was consistent. The results indicated no obvious difference in the total distance (Figure 2A). Next, we performed Y-maze and MWMT to access short- and long-term spatial learning and memory function, respectively. Based on the results from the Y-maze, anesthesia and surgery reduced the percentage of spontaneous alternation behavior in aged mice, which could be abolished via CAPE pretreatment (Figure 2B). In addition, the MWMT results showed that CAPE pretreatment significantly shortened the escape latency and enhanced the number of platform crossings (Figure 2C–E). Altogether, these results demonstrated that CAPE ameliorated short- and long-term cognitive impairment following anesthesia and surgery. ## 3.2. CAPE Pretreatment Suppresses Oxidative Stress Caused by Anesthesia and Surgery In view of the crucial role of oxidative stress and neuroinflammation in POCD, we sought to investigate whether CAPE pretreatment could reduce oxidative stress induced via anesthesia and surgery. Here, we used the fluorescent probe dihydroethidium to detect ROS generation in the hippocampus and revealed that anesthesia and surgery increased ROS generation. Furthermore, we found that CAPE pretreatment notably eliminated ROS generation in the hippocampal CA1, CA3, and DG regions (Figure 3A,B). In addition, we assessed the antioxidant levels, including superoxide dismutase (SOD), glutathione (GSH), and catalase (CAT), as well as the levels of fatty acids lipid peroxidation product such as malondialdehyde (MDA). The findings suggested that anesthesia and surgery triggered a marked increase in MDA, one of the most popular and reliable indicators of oxidative stress in clinical settings, and a significant diminution in SOD, GSH, and CAT in the plasm. In contrast, CAPE pretreatment dramatically increased the antioxidant (CAT and SOD) levels and reduced the MDA level in the plasm, compared with the A + S + Vehicle group (Figure 3C–E). Additionally, CAPE pretreatment raised GSH levels but was not statistically significant (Figure 3F). Collectively, the findings demonstrated that CAPE pretreatment reduced oxidative stress in the hippocampus and plasm caused by anesthesia and surgery. ## 3.3. CAPE Pretreatment Promotes the Switch of Hippocampal Microglia from the M1 to the M2 Type after Anesthesia and Surgery Microglial polarization is known to be a response to oxidative stress and neuroinflammation. To further investigate whether CAPE could modulate M1/M2 microglia polarization in the aged POCD model, we applied confocal microscopy and three-dimensional (3D) reconstitution to analyze the morphology [57] and number of microglia in the hippocampus. As presented in Figure 4A,B, we found an obvious increase in microglial number in the hippocampal CA1 and DG regions following anesthesia and surgery, whereas the number of microglia has no significant difference in the hippocampal CA3 region. By contrast, CAPE pretreatment attenuated the alteration of microglia caused by anesthesia and surgery. Furthermore, we observed that the anesthesia and surgery-induced decrease in microglial ramification was weakened with CAPE pretreatment (Figure 4A). Based on these results, we estimated that CAPE pretreatment may facilitate the switch of hippocampal microglia from the M1 to the M2 type. To test this hypothesis, we performed RT-qPCR to evaluate the changes in microglial polarization biomarkers. As shown in Figure 4C–G, anesthesia and surgery caused an obvious elevation of M1 biomarkers (CD86, iNOS, and CD32) and pro-inflammatory cytokines (TNF-α and IL-1β). Simultaneously, the M2 biomarkers, including CD206 and TGF-β, and anti-inflammatory cytokines, including IL-4 and IL-10, were reduced after anesthesia and surgery (Figure 4H,J–L). Additionally, anesthesia and surgery reduced the level of the M2 biomarker ARG-1; however, it was not statistically significant (Figure 4I). Interestingly, CAPE pretreatment could obviously reduce the elevation of M1 biomarkers (CD86, iNOS, and CD32) and reversed the decrease in M2 biomarkers (CD206, ARG-1, and TGF-β) (Figure 4C–E,H–J). Furthermore, RT-qPCR indicated that CAPE pretreatment reversed the increased pro-inflammatory cytokines, including IL-1β and TNF-α (Figure 4F,G). Simultaneously, the decline in anti-inflammatory cytokines, including IL-4 and IL-10, was reversed with CAPE pretreatment (Figure 4K,L). Thus, our study indicated that CAPE pretreatment facilitated the switch of hippocampal microglia from the M1 to the M2 type after anesthesia and surgery. ## 3.4. CAPE Pretreatment Enhances Hippocampal Sirt6/Nrf2 Signaling Pathway following Anesthesia and Surgery Sirt6 is a pivotal regulator of antioxidant response. However, whether Sirt6 contributes to POCD and its role in the efficacy of CAPE remains unclear. Here, we sought to investigate whether CAPE pretreatment could play an effective role through the Sirt6/Nrf2 pathway. Firstly, after anesthesia and surgery, we evaluated the levels of Sirt6 and Nrf2 expression in the hippocampus. The findings showed that CAPE pretreatment rescued the reduced expression levels of Sirt6 and Nrf2 caused by anesthesia and surgery (Figure 5A,B). Additionally, immunofluorescent staining analysis further demonstrated that CAPE pretreatment markedly enhanced the Sirt6 and Nrf2 expression levels of microglia in the hippocampal CA1, CA3, and DG regions following anesthesia and surgery (Figure 5C–F). Taken together, we suggested that CAPE pretreatment may mitigate cognitive dysfunction following anesthesia and surgery through enhancing the Sirt6/Nrf2 signaling pathway. ## 3.5. CAPE Alleviates H2O2-Induced ROS Generation in BV2 Cells To further confirm whether CAPE mitigates cognitive impairment through the Sirt6/Nrf2 signaling pathway, we performed a series of cellular experiments. Firstly, we developed H2O2-induced BV2 cells as the microglial oxidative stress model. We used the CCK-8 assays to detect the viability of BV2 cells exposed to various concentrations of H2O2 for the indicated time. These results suggested that the concentrations of H2O2 under 400 μM did not affect the viability of BV2 cells (Figure 6A). Similarly, we measured the viability of BV2 cells exposed to various concentrations of CAPE for the indicated time, demonstrating that concentrations of CAPE under 80 μM were safe for BV2 cells (Figure 6B). Live/dead staining of BV2 cells treated with H2O2 or CAPE supported the observations from the CCK-8 assays (Figure 6C,D). Since ROS is generally a reliable biomarker of oxidative stress, we applied flow cytometry to evaluate the ROS level in BV2 cells exposed to different concentrations of H2O2. The results revealed that 100 μM of H2O2 for 24 h led to the most ROS production in BV2 cells, compared with the other groups (Figure 6E). Thus, 100 μM of H2O2 for 24 h was selected to induce oxidative stress in BV2 cells. Furthermore, flow cytometry indicated the pretreatment with CAPE significantly inhibited ROS production induced by H2O2 in BV2 cells. Specifically, the H2O2-induced BV2 cells pretreated with 20 μM of CAPE show the most noticeable decrease in ROS production (Figure 6F). Altogether, CAPE pretreatment effectively alleviated H2O2-induced ROS generation in BV2 cells, and we chose 20 μM of CAPE for 24 h in subsequent experiments. ## 3.6. CAPE Pretreatment Increases Sirt6/Nrf2 Expression Levels in H2O2-Induced BV2 Cells Motivated by the observation that CAPE pretreatment enhanced hippocampal Sirt6/Nrf2 signaling pathway and suppresses oxidative stress in aged mice after anesthesia and surgery, we further explored whether the Sirt6/Nrf2 pathway plays a vital role in microglia-mediated oxidative stress. We examined Sirt6 and Nrf2 expression levels between the CON group and the H2O2 group, demonstrating that the levels of Sirt6 and Nrf2 were markedly decreased in H2O2-induced BV2 cells (Figure 7A,B). Next, we evaluated the expressions of Sirt6 and Nrf2 in H2O2-induced BV2 cells pretreated with CAPE and OSS_128167, a specific Sirt6 inhibitor. The results indicated that CAPE pretreatment markedly attenuated the decreased levels of Sirt6 and Nrf2 in BV2 cells induced by H2O2. However, this effect could be significantly inhibited by OSS_128167 (Figure 7C,D). Consistently, immunofluorescence staining supported the above results (Figure 7E–H). These results suggested the importance of the Sirt6/Nrf2 pathway in the efficacy of CAPE pretreatment in H2O2-induced BV2 cells. ## 3.7. CAPE Suppresses ROS Generation through Activating Sirt6 in H2O2-Induced BV2 Cells The above results have demonstrated that CAPE could alleviate H2O2-induced ROS generation in BV2 cells, but whether this effect is worked via the Sirt6 pathway is still unclear. Here, we used a specific Sirt6 inhibitor, OSS_128167, to inhibit Sirt6 in H2O2-induced BV2 cells. Moreover, we applied flow cytometry to analyze whether the effect of CAPE on ROS generation would be attenuated by OSS_128167. As shown in Figure 8A,B, OSS_128167 obviously weakened the inhibitory effect of CAPE on ROS generation. Therefore, we suggested that CAPE suppressed ROS generation through activating Sirt6 in H2O2-induced BV2 cells. ## 3.8. CAPE Promotes the Switch of H2O2-Induced BV2 Cells from the M1 to the M2 Type through Activating Sirt6 To further confirm whether CAPE promotes the switch of microglia from the M1 to the M2 type through Sirt6/Nrf2 pathway in H2O2-induced BV2 cells, we evaluated the status of microglia among the CON, H2O2, H2O2 + CAPE, and H2O2 + CAPE + OSS_128167 groups. Flow cytometry analyses revealed a strong increase in M1-type microglia (CD86+) in H2O2-induced BV2 cells, with no significant difference regarding M2 ones (CD206+). Furthermore, pretreatment with CAPE promoted the switch of microglia from the M1 to M2 type. However, this effect was weakened by OSS_128167 (Figure 9A–D). Then, immunofluorescence accessed the expressions of CD86 and CD206. As presented in Figure 9E–H, CAPE pretreatment could decrease CD86 expression and enhance CD206 expression in BV2 cells following H2O2 exposure. In addition, RT-qPCR confirmed that pretreatment with CAPE reduced the expression of M1 biomarkers (CD86, iNOS, and CD32) (Figure 9I–K) and increased the expression of M2 biomarkers (CD206, Arg-1, and TGF-β) (Figure 9L–N) in the H2O2-induced BV2 cells, which also could be suppressed by OSS_128167. We further demonstrated that CAPE decreased the expression of TNF-α (Figure 9O) and elevated the expression of IL-4 (Figure 9P). Thus, these findings indicated that CAPE promoted the switch of H2O2-induced BV2 cells from the M1 to M2 type through the Sirt6 pathway. ## 4. Discussion Acquiring an effective prophylactic medication is crucial to the treatment of POCD. Currently, there is no effective clinical pharmacotherapy to prevent POCD. CAPE, a natural constituent of propolis and numerous medicinal plants, shows powerful biological properties. A previous study has revealed that CAPE ameliorated cognitive dysfunction and dementia in AD mice by upregulating the Nrf2/HO-1 pathway [29]. In addition, accumulating studies showed that CAPE could upregulate the PI3-kinase-dependent pathway and downregulate the JAK/STAT pathway, thereby ameliorating cognitive impairment caused by drug toxicity [58,59]. Therefore, we further explored its role in the aged POCD model and expanded the application scope of CAPE. In the present study, we revealed that CAPE pretreatment could ameliorate short- and long-term spatial learning and memory impairment after anesthesia and surgery in aged mice. More importantly, our mechanistic studies revealed that CAPE pretreatment could suppress oxidative stress and facilitate the switch of microglia from the M1 to M2 type by enhancing the Sirt6/Nrf2 pathway in the hippocampus to ameliorate cognitive dysfunction after anesthesia and surgery. Thus, CAPE pretreatment may be a meaningful therapeutic strategy for POCD prevention in the future. Oxidative stress and prolonged neuroinflammation have been considered the main pathological factors contributing to POCD development [39,60,61,62]. A few studies have suggested that anesthesia and surgery resulted in the increase in malondialdehyde and oxidative damage in the elderly brain and antioxidants could attenuate cognitive dysfunction after anesthesia and surgery [45,63,64,65]. Moreover, emerging evidence suggested that suppression of neuroinflammation could attenuate cognitive deficits caused by laparotomy and cardiopulmonary bypass surgery under isoflurane anesthesia [66,67]. In this study, we found that CAPE pretreatment notably eliminated ROS generation in the hippocampus and reversed the elevation of catalase (CAT) and the decrease in glutathione (GSH) and superoxide dismutase (SOD) in the plasma, thereby ameliorating cognitive dysfunction following anesthesia and surgery. Mounting studies reported that microglia are merged as a critical gatekeeper of brain homeostasis, which exerts its regulatory effects in oxidative stress and neuroinflammation [68,69,70]. Microglial phenotypes could influence disease progression in the brain through their balance between M1 and M2-activated states [71]. Thus, we focused on the alternation of microglia and its related molecular mechanisms in aged mice undergoing anesthesia and surgery. Previous studies from our team and other laboratories indicated that anesthesia and surgery led to a notable increase in microglial activation in the hippocampi of rats [30,61,72]. Here, we demonstrated that CAPE pretreatment facilitated the switch of microglia from the M1 to M2 type in the hippocampi of aged mice to ameliorate cognitive dysfunction following anesthesia and surgery. However, the underlying molecular mechanism of microglial polarization still warrants exploration. Sirt6 is a deacetylase and plays a vital role in inhibiting oxidative stress and driving macrophage polarization toward the M2 type [73,74]. Specifically, it has been reported that the upregulation of the Sirt6/Nrf2 pathway ameliorated alcoholic liver disease and APAP-induced hepatotoxicity via protecting against oxidative stress [74,75]. Additionally, Song et al. demonstrated that adipose Sirt6 maintained systemic insulin sensitivity by deriving macrophage polarization toward M2 [73]. Moreover, recent studies showed that Sirt6 overexpression inhibited the inflammatory response and thus ameliorate neurological deficits in intracerebral hemorrhage rats and cerebral ischemia and reperfusion rats [21,76]. Additionally, endothelial Sirt6 could exert a meaningful effect in ischemic strokes by guarding blood–brain barrier (BBB) integrity [77]. Based on our results that CAPE pretreatment reduced oxidative stress and facilitated the switch of microglia from M1 to M2 polarization in the hippocampi of aged mice after anesthesia and surgery, we further evaluated the vital role of the Sirt6/Nrf2 pathway in these effects. As expected, we found that CAPE pretreatment could reduce ROS production and promote the expression of M2 phenotype markers via enhancing the Sirt6/Nrf2 pathway in vivo and in vitro, however, the effects of CAPE would be obviously weakened by the specific Sirt6 inhibitor, OSS_128167, in H2O2-induced BV2 cells. Therefore, we suggested that CAPE pretreatment decreased oxidative stress and favored microglia transforming toward the M2 type via activating the Sirt6/Nrf2 signaling pathway, thereby ameliorating cognitive impairment. Actually, there are still many issues awaiting further investigations in the future. Firstly, although we demonstrated that CAPE pretreatment could ameliorate cognitive dysfunction following anesthesia and surgery through facilitating the transformation of microglia toward M2 type via the Sirt6/Nrf2 signaling pathway, other cells besides microglia may also contribute to contributing to the effects. The effect of the Sirt6/Nrf2 pathway in neurons and astrocytes still needs to be investigated. Secondly, we mainly focused on the changes in Sirt6 expression and microglial polarization in the early stage of POCD. It is of great significance to further explore the related molecular changes in the late stage. Additionally, due to the complexity of the in vivo environment and technological restrictions, it is difficult to match the in vitro concentration of CAPE with the in vivo dose. Furthermore, we did not explore the differential effect of surgical procedures and anesthesia on the expression of Sirt6, as we used the model to mimic the clinical settings where it is rare to separate the two steps. However, the effect of surgical procedures and anesthesia on oxidative stress and neuroinflammatory response is variable [78,79]. Moreover, a published study has reported that CAPE could reverse cadmium-induced cognitive impairment in mice through the AMPK/Sirt1 pathway [80]. Furthermore, our previous study has found that resveratrol could alleviate cognitive impairment by activating Sirt1 in aged rats after anesthesia and surgery [31]. Thus, it is worth exploring whether CAPE also mediated the Sirt1 pathway to alleviate cognitive impairment in elderly mice after anesthesia and surgery. ## 5. Conclusions In summary, we found that CAPE pretreatment alleviated cognitive impairment in aged mice following anesthesia and surgery. 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--- title: Differential Expression of microRNAs in Serum of Patients with Chronic Painful Polyneuropathy and Healthy Age-Matched Controls authors: - Antonio Pellegrino - Sophie-Charlotte Fabig - Dilara Kersebaum - Philipp Hüllemann - Ralf Baron - Toralf Roch - Nina Babel - Harald Seitz journal: Biomedicines year: 2023 pmcid: PMC10045018 doi: 10.3390/biomedicines11030764 license: CC BY 4.0 --- # Differential Expression of microRNAs in Serum of Patients with Chronic Painful Polyneuropathy and Healthy Age-Matched Controls ## Abstract Polyneuropathies (PNP) are the most common type of disorder of the peripheral nervous system in adults. However, information on microRNA expression in PNP is lacking. Following microRNA sequencing, we compared the expression of microRNAs in the serum of patients experiencing chronic painful PNP with healthy age-matched controls. We have been able to identify four microRNAs (hsa-miR-3135b, hsa-miR-584-5p, hsa-miR-12136, and hsa-miR-550a-3p) that provide possible molecular links between degenerative processes, blood flow regulation, and signal transduction, that eventually lead to PNP. In addition, these microRNAs are discussed regarding the targeting of proteins that are involved in high blood flow/pressure and neural activity dysregulations/disbalances, presumably resulting in PNP-typical symptoms such as chronical numbness/pain. Within our study, we have identified four microRNAs that may serve as potential novel biomarkers of chronic painful PNP, and that may potentially bear therapeutic implications. ## 1. Introduction The most prevalent conditions of the peripheral nervous system in adults [1], polyneuropathies (PNP), are a mostly-chronic condition known to be therapeutically challenging, potentially disabling, and even lethal [2,3,4]. Resulting from a lesion or disease of the peripheral nervous system, PNPs become clinically apparent through the so-called stocking-glove pattern [5]: symmetric, initially-distally-distributed sensorimotor signs and symptoms, such as hypoesthesia or hypoalgesia, that may spread further proximally in the course of disease progression. In case of painful PNPs, patients may also experience gain of function, e.g., spontaneous burning sensations or evoked pain. Interestingly, not all of the PNP patients develop neuropathic pain (NP) [2]. The possible mechanisms leading to a specific clinical phenotype are subject to ongoing investigations, with several hypotheses having been posed so far [1,3,4,5]. What is becoming increasingly clear is that a complex molecular interplay contributes to this process, of which many components seem to be well-orchestrated by microRNAs (miRNAs) [6,7,8]. Non-coding RNAs, called microRNAs, are around 22 nucleotides long and act as transcriptional and post-transcriptional regulators of gene expression [9]. Through binding, deactivation, and/or degradation, they control the messenger RNAs (mRNAs) that they are targeting [10,11]. While numerous distinct miRNAs can bind the same mRNA, a single miRNA often binds to multiple mRNAs [12]. Although miRNAs are mostly found in cells, there are populations that are exported outside: circulating miRNAs. These can be found in various body fluids, including blood, urine, cerebrospinal fluid, saliva, and tears [13,14]. They can be released through active passage, in microvesicles, exosomes, or through being bound to a protein [15,16,17,18]. In addition, they may also be passively released during cell injury, thus potentially reflecting the extension of neural damage. Hence, miRNAs might serve as potential biomarkers of injuries causing acute pain, and as in most cases of PNP, chronic pain. So far, information on miRNA expression in PNP is lacking and only few miRNAs, involved in the process of chronic painful PNP, have recently been examined. For example, miR-132-3p showed a pro-nociceptive effect in peripheral neuropathies with chronic neuropathic pain [6]. In addition, depending on the examined site, miRNAs miR-146a and miR-155 were expressed aberrantly when compared to healthy controls [19]. In another study, miR-223 was associated with the attenuation of neuronal activity in pain pathways [7]. However, these findings were determined via qPCR using, inter alia, highly invasive sural nerve biopsies and nucleus pulposus tissue. Since PNP is currently diagnosed mostly based on characteristic clinical signs and symptoms that are to be validated through further, at best objectifiable examination methods [20], the diagnostic workup can be time-consuming, expensive, often unsuccessful, and requires qualified medical professionals [21]. Thus, a fast, easy, and non-invasive but also accurate, reliable, and objective method of detection is urgently needed. The aim of our study was to non-invasively determine the entire differential expression of miRNAs in the serum of chronically pain-suffering PNP patients, in order to suggest novel disease biomarkers and novel disease mechanisms, as well as to identify the most likely candidates for novel directions in therapy development. ## 2.1. Patient Selection, Serum Separation and Storage Patients were recruited at the University Hospital Schleswig-Holstein Campus Kiel (research group around Philipp Hüllemann) and the Marien Hospital Herne (research group around Nina Babel). In total, 30 patients with chronically painful PNP (ø 59 years) as well as 30 age-matched healthy patients (ø 58 years) were selected. Each group contained 10 men and 20 women. The average height in the control group was 175.63 ± 9.59 cm, with an average weight of 72.28 ± 8.83 kg, compared to 177.98 ± 8.80 cm and 85.51 ± 16.87 kg in the patient group. PNP patients were selected with an average pain of ≥ 4 on a numerical scale (0–10), a chronification score on the Mainz Pain Grading System of ≥ II, and a pain duration ≥ 6 months. The PNP etiologies were distributed as follows: chemotherapy-induced: $$n = 3$$; diabetic: $$n = 3$$; vitamin deficiency: $$n = 1$$; chronic inflammatory demyelinating polyneuropathy: $$n = 3$$; hereditary: $$n = 1$$; unclear etiology: $$n = 19$.$ For serum separation, whole blood in a primary blood-collection tube (without clot activator and without anticoagulants) was collected. For complete clotting, tubes were left at room temperature for 30 min and centrifuged for 10 min at 1900× g and 4 °C. Subsequently, the serum phase was transferred to a new tube. Serum samples in new tubes were centrifuged again for 15 min at 3000× g and 4 °C. After centrifugation, the cleared supernatant was carefully transferred to a new tube. For storage, the separated serum was kept frozen in aliquots at −80 °C. Before processing, room-temperature-thawed serum samples were centrifuged for 5 min at 3000× g and 4 °C to remove cryoprecipitates. ## 2.2. miRNA Purification Purification of cell-free total RNA, primarily miRNA and other small RNA, from the serum was performed using the miRNeasy Serum/Plasma Advanced Kit (QIAGEN, Hilden, Germany). The purification was performed according to the manufacturer’s protocol, with a starting volume of 200 µL serum. The procedure combined guanidine-based lysis of samples, an inhibitor removal centrifugation step, and a silica-membrane-based purification of total RNA. The purified total RNA was then eluted in 20 µL RNase-free water. ## 2.3. miRNA Pre Library Preparation Quality Control Pre library preparation quality control was performed using the QIAseq miRNA Library QC PCR Panel (QIAGEN, Hilden, Germany). The primary purpose was to control the quality of the isolated RNA in any next-generation sequencing experiment. The addition of the QIAseq miRNA Library QC Spike-Ins during RNA isolation enabled monitoring of the comparability and reproducibility from RNA isolation to sequencing. The quality control was performed according to the manufacturer’s protocol, with 0.5 μL QIAseq miRNA Library QC Spike-Ins per 200 μL serum and 0.5 μL UniSp6 Spike-In per reverse transcription reaction. To avoid contamination, all samples were prepared under sterile conditions. Each assay was transferred into a LightCycler capillary (Roche, Mannheim, Germany), capped, and briefly centrifuged. Analysis of the samples was performed with the LightCycler 2.0 Instrument (Roche, Mannheim, Germany). After conducting the qPCR-based quality control, the data were compared, outlier samples identified, and considered for exclusion in the library preparation. ## 2.4. miRNA Library Preparation Library preparation was performed using the QIAseq miRNA Library Kit (QIAGEN, Hilden, Germany), enabling unbiased next-generation sequencing of mature miRNAs using the MiSeq instrument (Illumina, Berlin, Germany) for differential-expression analysis of PNP vs. control samples. The library preparation was performed according to the manufacturer’s protocol, with the recommended starting volume of 5 µL total RNA of the RNA eluate, when 200 µL of serum had been processed using the miRNeasy Serum/Plasma Advanced Kit (QIAGEN, Hilden, Germany). The procedure started with the sequentially-adapter ligation to the 3′ and 5′ ends of miRNAs. Subsequently, universal cDNA synthesis with unique molecular index assignment, cDNA cleanup, library amplification, and library cleanup were performed. The cleaned miRNA library was then eluted in 17 µL RNase-free water. ## 2.5. Adapter Dimer Removal and miRNA Library Pre Sequencing Quantification/Quality Control Adapter-dimer removal was performed using the BluePippin instrument (Sage Science, Beverly, MA, USA) with $3\%$ agarose cassettes and internal standards, to automatically separate the miRNA sequencing library from their adapter dimers. The BluePippin optical system was calibrated and the continuity test was completed before every run. The size selection was performed according to the manufacturer’s protocol, with the size-selection mode set to tight 180 bp. After adapter-dimer removal, the cleaned miRNA-sequencing library was subjected to the Bioanalyzer 2100 system (Agilent, Santa Clara, CA, USA) for pre sequencing quantification/quality control. Therefore, 1 µL of each miRNA sequencing library was analyzed using a High Sensitivity DNA chip according to the manufacturer’s instructions. ## 2.6. Next Generation Sequencing The Illumina MiSeq instrument (Illumina, Berlin, Germany) was used to enable next-generation sequencing of mature miRNAs for differential-expression analysis of PNP vs. control samples. Therefore, quality-controlled miRNA-sequencing libraries were diluted to 0.5 nM, and four samples at a time were pooled according to the Illuminas Index Adapter Pooling Guide, considering the color balance. Subsequently, pooled 0.5 nM libraries were denatured and diluted to 20 pM, analogously to the NextSeq System Denature and Dilute Libraries Guide performing the Standard Normalization Method. Denatured and diluted library pools were loaded onto a MiSeq Reagent Kit v3 cartridge with 13 pM and $1\%$ phiX, according to the MiSeq System Denature and Dilute Libraries Guide and performing the Standard Normalization Method from step “Dilute Denatured 20 pM Library”. The sample sheet, the flow cell, the PR2 bottle, the waste bottle, as well as the reagent cartridge were prepared analogously to the MiSeq System Guide. For sequencing, FASTQ Only and TruSeq Small RNA with a 75 bp single reading was chosen to include the added unique molecular indices. Resulting FASTQ files were analyzed with a CLC Genomics Workbench 22 (QIAGEN, Hilden, Germany) and the Biomedical Genomics Analysis plugin 22.0.4 (QIAGEN, Hilden, Germany). ## 2.7. Sample-to-Sample Correlation The comprehensive set of QIAseq miRNA Library QC Spike-Ins allowed thorough quality control of the NGS data by assessing the reproducibility and linearity of the mapped reads. The 52 QIAseq miRNA Library QC Spike-Ins are synthetic 5′-phosphorylated miRNAs of plant origin and bear no significant homology to human miRNAs. Following mapping of the QIAseq miRNA Library QC Spike-In reads, they were normalized to the total number of reads per sample. After this normalization to individual sample reads was done for all spike-ins in all samples, they were evaluated for normality via Shapiro-Wilks Normality Test, and a Spearman Correlation matrix was plotted for sample-to-sample correlation using Prism 9.1.2 (GraphPad, Boston, MA, USA). ## 2.8. miRNA Differential Expression Analysis, GO Analysis and miRDB Target Prediction CLC Genomics Workbench 22 (QIAGEN, Hilden, Germany) and the Biomedical Genomics Analysis plugin 22.0.4 (QIAGEN, Hilden, Germany) were used to analyze the FASTQ files of each miRNA library, and to perform a differential expression analysis. For quantification of the miRNA libraries, the QIAseq miRNA Quantification workflow, with default settings, was utilized to annotate the miRNA reads using miRbase v22. For differential expression analysis of the miRNA libraries, the QIAseq miRNA Differential Expression workflow was used with the following settings: “Expression tables: Grouped on mature; Test differential expression due to: Group; While controlling for: Age; Comparisons: Against control group; Control group: Control”. In addition, a minimal reading count of 5 was used for the analysis. Significant results included only those differentially expressed miRNAs with a Bonferroni corrected p-value ≤ 0.05 and a fold-change FC ≥ 2.0. The GO enrichment analysis (biological process) was performed on targets of all differentially-expressed miRNAs identified with the following settings: GO annotation table: goa_human_rna_20181212; exclude computationally inferred GO terms; allow gene name synonyms; ignore gene name capitalization and ignore features with mean RPKM below: 5.0. As GO enrichment analyses with a Bonferroni corrected p-value, FDR corrected p-value or p-value ≤ 0.05, and a fold-change FC ≥ 2.0 were not successful, GO enrichment analysis parameter were changed to p-value ≤ 0.05 and a fold-change FC ≥ 1.5. Significant results included only those GO terms with a p-value ≤ 0.05 and differentially expressed genes DE > 2. The miRNA target prediction was performed using miRDB human (Version: 6.0, Prediction Tool: MirTarget V4, miRNA Source: miRBase 22); miRDB is an online database for miRNA target prediction and functional mapping. All the targets in miRDB are predicted by MirTarget, which was developed by analyzing thousands of miRNA-target interactions. From these interactions, common features associated with miRNA binding/target downregulation have been identified and used to predict miRNA targets using machine learning methods [22]. Therefore, the differentially-expressed and filtered miRNAs were subjected to miRDB, and results included only the top 3 targets for each miRNA. ## 3.1. Patients and Controls We obtained serum from 30 patients with chronic painful PNP and 30 age-matched control subjects. Each group contained 10 men and 20 women. The average height in the control group was 175.63 ± 9.59 cm, with an average weight of 72.28 ± 8.83 kg compared to. 177.98 ± 8.80 cm with 85.51 ± 16.87 kg in the patient group. The average age of patients vs. controls was 59 vs. 58 years. The following are the PNP etiologies of the patients: chemotherapy-induced: 3; diabetes: 3; vitamin deficiency: 1; chronic inflammatory demyelinating polyneuropathy: 3; hereditary: 1; the remaining etiologies are unknown. In terms of the heterogeneity of etiologies in our cohort, it must be considered that the modern approach to neuropathic pain is to follow a mechanism-based classification and to aim at individualized treatments rather than dividing patients by etiologies [4]. Thus, the aim of our study was to identify biomarkers for neuropathic pain in general without targeting a specific etiology. ## 3.2. Next Generation Sequencing Prior to library preparation, samples were quality controlled using a miRNA library QC PCR panel. The qPCR assay provided insight into RNA isolation efficiency, cDNA synthesis efficiency, and controlled for endogenous miRNAs as well as for hemolysis. Since all quality controls of all samples were successful, library preparation was performed. After library preparation and ahead of next generation sequencing, samples were cleaned of adapter dimers by automated size selection and quantified/quality controlled. Since the quantification/quality control of all samples was successful, next generation sequencing was performed. Therefore, FASTQ Only and TruSeq Small RNA with a 75 bp single read was chosen using the MiSeq instrument, with 4 samples per flow cell. Resulting FASTQ files were analyzed using CLC Genomics Workbench 22 (QIAGEN, Hilden, Germany) and the Biomedical Genomics Analysis plugin 22.0.4 (QIAGEN, Hilden, Germany). Hence, the reads were annotated with the miRNA quantification workflow with default settings using miRbase v22b. On average, 6.5 M reads (1.5 M unique molecular index grouped reads) were generated per sample and $45.73\%$ of these reads could be annotated. ## 3.3. Sample-to-Sample Correlation As an additional quality control, all samples were spiked with 52 miRNA library QC spike-ins before miRNA isolation from serum. These spike-ins are artificial, 5′-phosphorylated, plant-derived miRNAs, and they are not significantly homologous to human miRNAs [23]. The miRNA library QC spike-in reads were mapped, and their counts were normalized to total reads per sample. All spike-ins in all samples were normalized to individual sample reads. Subsequently, they were evaluated for normality after which a correlation matrix was plotted to allow sample-to-sample correlation. For all samples, the spearman r values ranged from 0.86–0.99, showing strong correlation through all sample preparations (Figure 1). ## 3.4. Differential Expression For differential expression analysis, the resulting quantification of each miRNA library was utilized and grouped based on being PNP or control. Although the groups were age-matched, differential expression analysis took age into account to exclude age-based false-positive results. In addition, a minimal reading count of 5 was used for the analysis. Only differentially expressed miRNAs with a fold-change FC ≥ 2.0 and a Bonferroni adjusted p-value ≤ 0.05 were included in the significant results overview, which is given by Table 1. The hsa-miR-3135b was determined with the largest fold change of -6.30 and a highly significant Bonferroni corrected p-value of 5.94 × 10−11. Two other miRNAs were downregulated as well: hsa-miR-584-5p with the smallest fold change of −2.62 and a Bonferroni corrected p-value of 1.43 × 10−6, as well as hsa-miR-12136 with a fold change of −3.80 and a Bonferroni corrected p-value of 7.50 × 10−4. The only upregulated miRNA was hsa-miR-550a-3p with a fold change of 4.27 and a significant Bonferroni corrected p-value of 0.02, respectively. ## 3.5. GO Enrichment Analysis and miRNA Target Prediction of Differentially Expressed miRNAs Subsequently, a GO enrichment analysis (biological process) was performed on targets of all differentially-expressed miRNAs identified. As GO enrichment analyses with a Bonferroni corrected p-value, FDR corrected p-value or p-value ≤ 0.05, and a fold-change FC ≥ 2.0 were not successful, the following GO enrichment analysis was performed with a p-value ≤ 0.05 and a fold-change FC ≥ 1.5. The summary of the GO enrichment analysis is given by Table 2. GO biological-processes analysis included 13 terms. Among them, vascular endothelial growth factor/vasculature development/angiogenesis pathways, also signal transduction/cell communication pathways and cell apoptotic process/developmental process related pathways, indicated that blood vessels and their surrounding tissues, such as smooth muscle cells and neurons, are actively degenerating concurrently with PNP progression. In-depth miRNA target prediction supported these findings by determining targeted genes/mRNAs/proteins. This target prediction was performed using miRDB’s human target prediction (Version: 6.0, Prediction Tool: MirTarget V4, miRNA Source: miRBase 22) [22]. The summary of the miRNA target prediction is given in Table 3 and shows the top three predictions for each differentially expressed and filtered miRNA: The hsa-miR-3135b target prediction revealed the gene “leucine rich repeat containing 27” (LRRC27) as a potential target. LRRC27 is expressed in platelets and its overexpression is associated with preeclampsia, which, among other symptoms, manifests in high blood pressure [24]. In addition, the gene “formin like 3” (FMNL3) is assigned to hsa-miR-3135b. FMNL3 is expressed in endothelial cells and is a known cytoskeletal regulator of angiogenesis [25]. Finally, the gene “tetratricopeptide repeat domain 21B” (TTC21B) is potentially targeted by hsa-miR-3135b. TTC21B is expressed in the primary cilium and some variants are associated with arterial hypertension [26]. The hsa-miR-584-5p target prediction revealed the gene “USP6 N-terminal like” (USP6NL) as a potential target. USP6NL is a GTPase-activating protein that functions as a deubiquitinating enzyme, regulating endocytosis and signal transduction [27]. Furthermore, the gene “arginine vasopressin receptor 1A” (AVPR1A) is assigned to hsa-miR-584-5p. AVPR1A is expressed in peripheral blood vessels, encoding a receptor for arginine vasopressin that helps blood vessels to constrict and control blood pressure [28]. Eventually, the gene “SET domain containing 5” (SETD5) is potentially targeted by hsa-miR-584-5p. SETD5 seems to control neural cell proliferation and synaptic activity/connectivity [29,30]. The hsa-miR-12136 target prediction revealed the gene “immunoglobulin superfamily member 11” (IGSF11) as a potential target. IGSF11 is required for synaptic development [31]. In addition, the gene disheveled associated activator of morphogenesis 1 (DAAM1) is assigned to hsa-miR-12136. DAAM1 has been shown to aid in the development of neuronal systems, through nucleating, elongating, and possibly bundling actin [32,33]. Finally, the gene “syntaxin binding protein 5” (STXBP5) is potentially targeted by hsa-miR-12136. STXBP5 is expressed in human endothelial cells and is associated with venous thromboembolism that manifests with blood clots, and leads to secondary changes in the blood vessels and alterations to the blood flow [34]. The hsa-miR-550a-3p target prediction revealed the gene “myosin heavy chain 10” (MYH10) as a potential target. It has been found that MYH10 participates in the critical developmental process of coronary-vessel formation, and that MYH10 is able to interact with sodium channels, modulating their current density and gating properties [35,36]. Furthermore, the gene “synaptotagmin 4” (SYT4) is assigned to hsa-miR-550a-3p. SYT4 regulates membrane traffic in neurons and seems to be important for dendrite growth [37]. Eventually, the gene “myelin transcription factor 1 like” (MYT1L) is potentially targeted by hsa-miR-550a-3p. MYT1L promotes axonal development/differentiation, neurite outgrowth/proliferation, synaptic transmission, extracellular matrix composition, as well as remyelination after induced demyelination [38,39,40]. ## 4. Discussion Under the chosen criteria, our analysis of the differential expression of miRNAs revealed a significant difference for four annotated miRNAs in the serum of chronic painful PNP patients compared to healthy, age-matched controls. Considering that this number represents only a relatively small percentage, these findings appear to be indicative of possible biomarkers with high accessibility, also possibly-high specificity and sensitivity, for the diagnosis of chronic painful PNP. The identification of the hereby-presented miRNAs may be the first step towards identifying unknown disease mechanisms and towards eventually developing innovative therapeutic approaches. The differentially-expressed miRNAs’ GO biological-processes analysis points to their potential participation in signal transduction, blood-flow regulation, and degenerative processes. A potential diagnostic role of the hereby-identified miRNAs is supported by our findings that the differentially expressed miRNAs identified in the serum of PNP patients were linked to genes/mRNAs/proteins via miRDB that could potentially trigger PNP (and explain its related symptoms). Confirming the potential targets and their effects/pathways found by our miRDB and the GO analysis, our literature research yielded the first hints of their involvement in hemorheological abnormalities such as hypertension/coronary artery calcification (hsa-miR-3135b), pulmonary arterial hypertension (hsa-miR-584-5p), angiogenesis/hypoxia (hsa-miR-12136), and damaged vascular smooth muscles (hsa-miR-550a-3p) [41,42,43,44,45,46]. Since the control group was lighter than the patient group (72.28 ± 8.83 kg vs. 85.51 ± 16.87 kg), the hsa-miR-584-5p and hsa-miR-550a-3p results may be due to this difference, as both miRNAs have also been linked to obesity/insulin resistance [47,48]. However, logistic regression analyses showed that the differences seen between the control group and the patient group were not due to difference in body weight (Table S1). Nevertheless, extensive manual literature research via PubMed/PubMed Central, using search terms such as “miRNA polyneuropathy” and “miRNA”, where miRNA is the corresponding differentially expressed miRNA, did not yield results to verify the determined miRNAs in the context of PNP or chronic pain [49]. Thus, to our best knowledge, our data provide further research targets for PNP and chronic pain that might possibly serve as novel disease biomarkers. Eventually, this might help develop new therapeutic options in the future. These results lead to the hypothesis that a combination of high blood flow/pressure and neural activity dysregulations/imbalances could lead to chronic painful PNP. According to our data, one could speculate that vasoconstriction/high blood pressure induced by upregulation of LRRC27, AVPR1A, TTC21B, STXBP5, downregulated coronary vessel formation (MYH10), as well as overactive angiogenesis/EGFR (FMNL3/USP6NL), lead to the degeneration of small sensory neurons due to the lack of nourishment/restricted blood flow [24,25,26,27,28,34,35]. In addition, neural dysregulation in terms of overactive synaptic development/proliferation and activity/connectivity mediated by upregulation of SETD5, IGSF11, and DAAM1, as well as downregulated dendrite extension/maintenance of neuronal identity (SYT4/MYT1L), could contribute to the degeneration of sensory neurons as well [30,31,32,37,40]. Altogether, overactive synaptic development/proliferation and activity/connectivity, as well as the lack of nourishment/restricted blood flow due to vasoconstriction/high blood pressure, could lead to the degeneration of small sensory neurons resulting in the neuropathy’s typical symptoms such as numbness/pain. In line with our findings, in a review examining the association of vascular changes with clinical symptoms in animal and human models, the authors identified metabolic changes accompanied by vascular dysfunction as a possible cause (and therapeutic target) of diabetic neuropathy [50]. As for the mechanism, vasoconstriction/high blood pressure is reported to result in reduced nerve blood flow/nerve hypoxia and presumably neuropathy, since the degree of neuropathy was shown to be closely correlated with the number of closed vessels [51]. Perhaps surprisingly, both adult neurogenesis and neuroplasticity have been reported to contribute to the maintenance of long-lasting NP (i.e., chronicity) [52] and NP that may occur adjacent to or remotely from any injury site [53]. ## 5. Conclusions Taken together, the strong correlation between the reported miRNAs (i.e., hsa-miR-3135b, hsa-miR-584-5p, hsa-miR-12136 and hsa-miR-550a-3p) and chronic painful PNP is promisingly suggestive of a possible diagnostic and therapeutic usefulness of these biomarkers in the future. In addition, the GO enrichment analysis and the miRNA target prediction suggest possible molecular links between degenerative processes, blood flow regulation, and signal transduction, that might eventually cause PNP and presumably result in typical symptoms such as chronic numbness/pain. However, a qRT-PCR validation would have been desirable but the serum samples were completely used for NGS, and therefore qRT-PCR validation was not possible. 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--- title: A Retrospective Study on the Role of Metformin in Colorectal Cancer Liver Metastases authors: - Miran Rada - Lucyna Krzywon - Stephanie Petrillo - Anthoula Lazaris - Peter Metrakos journal: Biomedicines year: 2023 pmcid: PMC10045020 doi: 10.3390/biomedicines11030731 license: CC BY 4.0 --- # A Retrospective Study on the Role of Metformin in Colorectal Cancer Liver Metastases ## Abstract Colorectal cancer liver metastases (CRCLMs) have two main histopathological growth patterns (HPGs): desmoplastic (DHGP) and replacement (RHGP). The vascularization in DHGP tumours is angiogenic, while the RHGP tumours exert vessel co-option vasculature. The presence of vessel co-option tumours is associated with poor response to anti-angiogenic agents and chemotherapy, as well as a worse prognosis. Metformin has been shown to influence the progression and vasculature of tumours in different cancers. However, its role in CRCLM is poorly understood. Herein, we conducted a retrospective cohort study to examine the role of metformin in CRCLM. A dataset of 108 patients was screened, of which 20 patients used metformin. The metformin user patients did not use metformin as an anticancer agent. We noticed a significantly lower percentage of CRCLM patients with vessel co-opting RHGP tumours in the population that used metformin compared to CRCLM patients who did not use metformin. Similar results were obtained when we compared the ratio of recurrence and extrahepatic metastases incidence. Moreover, the metformin user patients had significantly higher survival outcome compared to nonusers. Collectively, our data suggest that metformin administration is likely associated with better prognosis of CRCLM. ## 1. Introduction Colorectal cancer (CRC) is the second most lethal cancer [1] and is linked with approximately $10\%$ of cancer-related death among men and women worldwide [2]. The development of metastatic diseases is the main cause of death in CRC patients, which over $50\%$ of the patients will develop liver metastases (LM) during the course of their disease [3]. Surgical resection is the only chance to cure patients with colorectal cancer liver metastasis (CRCLM) [4]. However, only 15–$20\%$ of CRCLM patients are eligible for hepatic resection [5]. The unresectable patients are referred to chemotherapy combined with targeted therapies, including anti-angiogenic agents (e.g., Bevacizumab) [6,7]. However, the effect of the treatments is limited due to the acquired resistance [8]. CRCLM tumours exert two major histopathological growth patterns (HGPs) including desmoplastic HGP (DHGP) and replacement HGP (RHGP) [8,9]. The cancer cells in DHGP lesions are separated from the liver parenchyma via the desmoplastic ring [8]. However, the desmoplastic rim is absent around RHGP tumours, and the cancer cells are in direct contact with the liver parenchyma [8,10]. Moreover, the DHGP tumours use angiogenic vascularization, whereas the RHGP tumours obtain blood supply through vessel co-option vascularization [8,10]. In vessel co-opting CRCLM, the cancer cells infiltrate through liver parenchyma and hijack the pre-existing sinusoidal vessels [8,11]. Importantly, vessel co-option is associated with acquired resistance against chemotherapy and antiangiogenic drugs in CRCLM [8] and other types of cancers, such as hepatocellular carcinoma and glioblastoma [12]. Metformin is a 1,1-dimethylbiguanide hydrochloride that is extracted from the legume Galega officinalis [13]. Metformin is widely used to treat type 2 diabetes [14]. Metformin decreases hepatic gluconeogenesis and induces skeletal muscle glucose uptake via triggering the activation of AMP-activated protein kinase (AMPK), a master energy sensor that modulates energy homeostasis at both cellular and whole-body levels [15]. Metformin uses two different pathways for AMPK activation: [1] inhibition of NADH-ubiquinone oxidoreductase (complex I), the largest component of oxidative phosphorylation/electron transport chain, followed by reduction in the ATP/AMP ratio; and/or [2] activation of liver kinase B1 (LKB1), a protein that mediates AMPK phosphorylation and activation [15,16]. Cancer cells acquire metabolism reprogramming to obtain sufficient energy to maintain viability and build new biomass [17]. Importantly, inhibition of complex I [18], as well as induction of LKB1/AMPK pathway [19], antagonize metabolism reprogramming. Since metformin mediates both pathways [20], it has gained increasing interest as a potential anticancer agent [21,22]. Xu et al. [ 23] have performed a study to validate metformin repurposing as an anticancer agent and assess its role in reducing the mortality of different cancer patients. Intriguingly, the mortality was significantly lower among those cancer patients that used metformin compared to cancer patients that were not on metformin [23]. Previous studies have reported the inhibitory effect of metformin on cell proliferation, motility, and epithelial to mesenchymal transition (EMT) in cancer cells [24,25,26]. Furthermore, metformin also blocks significant signaling pathways of tumorigenesis, such as TGFβ [27] and PI3K/AKT [28] pathways. It is worth mentioning that TGFβ1 signaling pathway is significantly upregulated in vessel co-option CRCLM tumour and contributes to the expression of the proteins that mediate the development of vessel co-option, such as runt-related transcription factor-1 (RUNX1) [11]. Previous studies have proposed that low-dose metformin (250 mg/day) has potential clinical efficacy for CRC chemoprevention [29,30]. Metformin usage is also associated with better overall survival of CRC patients [31]. Metformin has multiple effects on CRC cells, including blocking cell proliferation, EMT, and motility, as well as inducing apoptosis [32]. Using CRC xenograft nude mice, Sang et al. [ 33] have proposed metformin as a potential antimetastatic agent in CRC, which inhibited metastases to the intestine, omentum, and renal capsule. The susceptibility of CRC cells to metformin has been correlated to different factors including the expression of miR-18b-5p, miR-145-3p miR-376b-5p, miR-718, and miR-676-3p [34]. Accordingly, the upregulation of miR-18b-5p, miR-145-3p, miR-376b-5p, and miR-718 facilitates the function of metformin in cell cycle arrest, while overexpression of miR-676-3p improves both proapoptotic and cell cycle arrest activity of metformin in CRC cells [34]. Metformin has been shown to inhibit tumour angiogenesis through various mechanisms [35]. Using metastatic breast cancer models, Wang et al. [ 36] have demonstrated significant inhibition of tumour angiogenesis upon treatment with metformin. Additionally, Moschetta et al. [ 37] have reported downregulation of hypoxia-inducible factor-1 (HIF-1) and vascular endothelial growth factor (VEGF), the key angiogenic markers in breast cancer after treatment with metformin. However, the role of metformin in the development of vessel co-option vascularization has not been studied yet. In this manuscript, we conducted a retrospective cohort study to identify the anticancer effect of metformin in CRCLM. We assessed the distribution of the patients with different HGPs, recurrence, extrahepatic metastases, and 5-year overall survival (OS) rate upon metformin usage. ## 2.1. Clinical Data and Patient Samples The study was conducted in accordance with the guidelines approved by McGill University Health Centre Institutional Review Board (IRB). The data of this study were collected from all CRCLM patients who had consented to contribute to the McGill University Liver Disease Biobank research program. The presented data were collected from 108 patients who had surgical resection of their liver metastases between January 2009 and December 2020 at McGill University Health Center (MUHC), and the HGP of their tumours was determined by histopathologists. HGPs were determined after surgical resection. All patients were intended to be followed until death. Patient data was updated and reviewed through July 2022. We excluded patients with a lack of follow-up information and unknown HGPs. The HGPs of the patients were evaluated according to international consensus guidelines for scoring the HGPs of liver metastasis [38]. Tumours with more than $50\%$ of a specific growth pattern, i.e., DHGP or RHGP, were assigned predominately that HGP. If a patient had multiple liver tumours with different dominant growth patterns, the patient would then be designated as a patient with mixed HGP tumours. ## 2.2. Study Population This retrospective cohort study consists of patients with pathologically confirmed CRCLM diagnoses of patients without ($$n = 88$$) and with diabetes mellitus ($$n = 20$$) (Table 1). Diabetes was defined as individuals with a self-reported history of diabetes or use of antidiabetic medications. Half of diabetic population ($$n = 10$$) was administered only metformin as antidiabetic drug, while the other half ($$n = 10$$) used metformin with at least one other antidiabetic drug before and after surgery. The metformin user patients did not use metformin as anticancer agent, while they used metformin as antidiabetic agent. All patients had surgical resection of their liver metastases between January 2009 and December 2020 at McGill University Health Center (MUHC) and their follow-up information was collected by McGill University Liver Disease Biobank research program. ## 2.3. Data Collection Trained personnel collected demographic and clinical variables of the consented patients via medical record review using an established abstraction form. We collected information on gender, age, weight, and height, as well as administration and names of the oral antidiabetic medications. For diabetic patients, the duration of antidiabetic usage was not available. ## 2.4. Statistical Analysis Statistical analysis was performed using GraphPad Prism software version 9.0 (GraphPad Software, La Jolla, CA, USA) software. In all instances, p-values of < 0.05 were considered statistically significant. We divided CRCLM patients into two major groups according to their usage of metformin, regardless of the dose and duration of metformin use and other combinational therapies they had received. The two categories were as follows: patients who did not receive (-metformin) and those who received metformin (+metformin). All metformin user CRCLM patients were diabetic. Categorical data were compared using chi-squared test. Cox proportional hazards regression model was used to estimate the hazard ratios and $95\%$ confidence intervals. Overall survival estimates were calculated from the date of diagnosis of liver metastases to the date of death or to the date of the last follow-up. Patient data was updated and reviewed through July 2022. Kaplan–Meier analysis with log-rank tests was used to estimate survival curves and statistical significance. ## 3.1. The Distribution of CRCLM Patients with Different HGPs, Recurrence, and Extrahepatic Metastases upon Metformin Usage We established a local cohort of CRCLM from 108 patients (Supplementary Table S1) and categorized patients based on their usage of metformin; 20 ($18.5\%$) of the patients used metformin and 88 patients ($81.5\%$) did not use metformin. Next, we categorized both populations according to the HGPs of their tumours. Of note, the HGPs of the tumours were scored by histopathologists using $50\%$ cut-off predominant HGPs scoring following international consensus guidelines for scoring the HGPs of liver metastasis [38]. We divided our patients into three groups of HGPs as follows: predominant vessel co-opting RHGP ($44.4\%$), predominant angiogenic DHGP ($34.3\%$), and mixed ($21.3\%$), as shown in Table 1. Patients with mixed tumours are those patients who had had multiple liver tumours with different dominant growth patterns. Interestingly, we noticed a lower percentage of CRCLM patients with vessel co-option RHGP or mixed tumours in the population that used metformin compared to the population that did not take metformin (Figure 1a). Moreover, the percentage of patients with angiogenic DHGP tumours was $31\%$ in the non-metformin population, while this ratio was $50\%$ for the population that used metformin. Tumour recurrence is a thorny problem in clinical tumour therapy [39]. However, limited anticancer agents have shown a significant postoperative inhibition for tumour recurrence [40]. Our data suggested that recurrence significantly increases mortality in CRCLM patients (Supplementary Figure S1a). To further assess the impact of metformin in CRCLM, we analyzed the recurrence incidence in CRCLM patients upon the usage of metformin. Interestingly, the usage of metformin was significantly associated with the reduction of recurrence incidence. As shown in Figure 1b, the percentage of recurrence incidence was $24\%$ in the population of the patients that were administered metformin, whereas this ratio was significantly higher ($47\%$) in the group that was not administered metformin. Another factor associated with poor prognosis in CRCLM is the development of extrahepatic metastases, and it has been reported that $38\%$ of CRCLM patients develop extrahepatic metastases [35]. Our data further confirmed these results and suggested significant reduction in the survival rate of CRCLM patients upon the presence of extrahepatic disease (Supplementary Figure S1b). Therefore, we decided to analyze our cohort to identify the influence of metformin on the presence of extrahepatic tumours in CRCLM. According to our data, $61\%$ of the patients who did not use metformin have developed extrahepatic tumours (Figure 1b). However, this ratio was significantly decreased to $40\%$ in the group of patients who were administered metformin. While the majority of the previous studies mainly focused on metformin as an antidiabetic drug with anticancer activity, other types of antidiabetic drugs have been shown to suppress tumour progression [41]. Gliclazide [42], Sitagliptin [43], and Canagliflozin [44] are among the antidiabetic drugs that showed anticancer function. Our data showed that $50\%$ of the CRCLM patients who used metformin were using other types of antidiabetics (Supplementary Table S1). Therefore, we decided to compare the impact of metformin and metformin combined with other antidiabetics on HGPs, recurrence, and extrahepatic incidence. Interestingly, only $20\%$ of the patients had vessel co-option tumours in the population that used only metformin as an antidiabetic drug, while this ratio was higher ($40\%$) in the group that used metformin combined with other antidiabetics (Supplementary Figure S2a). The percentage of patients with recurrence incidence in both populations was similar, at $20\%$ (Supplementary Figure S2b). The ratio of patients with extrahepatic metastases was significantly lower in the population that combined metformin and other antidiabetics compared to the group that used metformin alone (Supplementary Figure S2c). Importantly, the difference between both groups in five-year overall survival (OS) was statistically nonsignificant (Supplementary Figure S2d). Taken together, the distribution of the patients suggests a significantly lower percentage of CRCLM patients with vessel co-opting RHGP tumours, recurrence, and extrahepatic metastases upon metformin usage. However, further investigations are required to verify these results and identify the molecular mechanisms underlying the role of metformin in CRCLM. ## 3.2. The Survival Rate of CRCLM Patients upon Metformin Usage CRCLM is associated with a poor survival rate. Previous studies suggested that the median survival of CRCLM patients who underwent hepatic resection was 37.7–42.0 months after initial hepatectomy [45,46]. To examine whether metformin administration has any survival benefits for CRCLM patients, we analyzed the five-year OS in our cohort. Firstly, we compared the survival of the patients with RHGP tumours ($$n = 6$$) who used metformin to non-metformin patients with RHGP tumours ($$n = 42$$). We found a slightly better survival rate for the patients who were administered metformin compared to those who were not (Figure 2a). However, this difference was statistically nonsignificant. Similar results were obtained for patients with DHGP (Figure 2b) or mixed (Figure 2c) tumours. Next, we compared the five-year OS of CRCLM patients who were not administered metformin to those who were administered metformin. Our data demonstrated that the survival of metformin user CRCLM patients was significantly ($$p \leq 0.0048$$) higher than the rest of the patients (Figure 2d). The hazard ratio was also estimated for those using metformin individually or combined with other antidiabetics (hazard ratio (HR) = 0.8906, $95\%$ confidence interval (CI): 0.2067–2.6274; hazard ratio (HR) = 0.2314, $95\%$ confidence interval (CI): 0.0112–1.855, respectively). Altogether, our results proposed that metformin user CRCLM patients had significantly lower mortality than CRCLM patients who did not use metformin. ## 4. Discussion It has been reported that CRCLM patients with vessel co-opting RHGP tumours have the worst prognosis [8,47]. The lack of angiogenic vascularization in vessel co-option tumours is linked to their resistance to antiangiogenic agents, such as Bevacizumab [8]. Importantly, vessel co-option CRCLM tumours also showed limited response to chemotherapy [8]. Hence, impairing the development of vessel co-option tumours or converting their vasculature to angiogenic significantly increases their response to antiangiogenic agents and chemotherapy, consequently improving the prognosis of CRCLM patients. Previous preclinical and clinical studies have proposed metformin as a potential anticancer agent [29,30]. Additionally, the usage of metformin has been associated with lower mortality in different types of cancer including colorectal, pancreas, hepatocellular, breast, and lung [48]. Tumour vascularization is crucial for tumour growth [49]. Therefore, it has become an attractive target for therapy. Tumour vascularization is divided into angiogenic and nonangiogenic [49,50]. The antiangiogenic function of metformin has been reported in various cancers, such as breast, lung, melanoma, hepatocellular, and colorectal cancer [51]. However, the effect of metformin on nonangiogenic tumour vascularization, including vessel co-option, is unknown. In the current manuscript, we found a lower percentage of CRCLM patients with nonangiogenic vessel co-option tumours upon metformin usage. Our previous publications suggested that higher levels of cancer cell proliferation, EMT, and motility [11], as well as the upregulation of TGFβ1 and PI3K/AKT pathways are essential for the development of vessel co-option CRCLM lesion [52,53,54]. Interestingly, metformin has been shown to block these pathways in various tumours [27,28]. Consequently, we postulate that the function of metformin against vessel co-option vascularization is likely mediated via attenuating the aforementioned pathways. However, this hypothesis warrants further investigation. Tumour recurrence remains one of the major problems after hepatic resection in CRCLM and it is the main cause of death in CRCLM patients [55]. Approximately $75\%$ of CRCLM patients experience tumour recurrence after hepatic resection [56]. Importantly, we observed a significantly lower percentage of CRCLM patients with tumour recurrence in the CRCLM population that was administered metformin compared to the CRCLM population that did not use metformin. In agreement with our findings, the usage of metformin has been linked with lower levels of tumour recurrence in patients with gastric cancer [57] and hepatocellular carcinoma [58]. Krzywon et al. [ 59] previously suggested that CRCLM patients with nonangiogenic tumours are more likely to develop extrahepatic metastasis. Herein, we found a smaller percentage of CRCLM patients with extrahepatic metastases upon metformin usage. Of note, CRCLM concomitant with extrahepatic metastasis is difficult to manage and associated with poor prognosis [60]. Indeed, the inverse correlation between the usage of metformin and the development of extrahepatic metastases is potentially associated with the survival benefit of metformin in CRCLM. It has been reported that metformin reduces the serum levels of low-density lipoprotein cholesterol (LDL-C) by suppressing the transcription of proprotein convertase subtilisin/kexin type 9 (PCSK9) [61]. Importantly, we previously showed upregulation of PCSK9 in the liver parenchyma of vessel co-opting CRCLM specimens [62]. Moreover, our preclinical data suggested that using hypocholesterolemic drugs, including anti-PCSK9 (Evolocumab), significantly attenuates the development of vessel co-opting CRCLM tumours [62]. Therefore, we postulate that metformin may also affect the generation of vessel co-option tumours in CRCLM via regulation of PCSK9 and LDL-C levels. Indeed, future studies will be needed to examine this hypothesis. Our results support prior findings that suggested the role of metformin in lowering the risk of overall mortality in cancer patients [14,51]. We hypothesize that the function of metformin in CRCLM patients is likely driven by reducing the development of vessel co-option, tumour recurrence, and extrahepatic metastases. ## 5. Limitations of the Study The presented study has several limitations due to its retrospective design. First, the small number of patients—specifically, the patients who used metformin—may have limited proper evaluation of the correlation between HGP of CRCLM tumours and metformin usage. Second, although the data determined the usage and type of antidiabetic drugs used by CRCLM patients, they did not clarify the duration and dosage of their administration. Third, this study lacks other important information, such as the status of other comorbidities and glycemic control. Therefore, further studies are warranted to fully understand the role of metformin in CRCLM and, specifically, its impact on the development of vessel co-option tumours. ## 6. 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--- title: Genome-Wide Association Study of Growth and Sex Traits Provides Insight into Heritable Mechanisms Underlying Growth Development of Macrobrachium nipponense (Oriental River Prawn) authors: - Mengchao Wang - Shubo Jin - Shuai Liu - Hongtuo Fu - Yunfeng Zhao - Li Jiang journal: Biology year: 2023 pmcid: PMC10045025 doi: 10.3390/biology12030429 license: CC BY 4.0 --- # Genome-Wide Association Study of Growth and Sex Traits Provides Insight into Heritable Mechanisms Underlying Growth Development of Macrobrachium nipponense (Oriental River Prawn) ## Abstract ### Simple Summary Macrobrachium nipponense is an important economic aquaculture animal, and due to its delicious taste, it is a popular food in South China. The growth of male and female Macrobrachium nipponense is significantly different. The female grows slowly due to precocious puberty, whereas males have a clear growth advantage. Therefore, due to their fast growth, males are often the subject of genetic breeding by scientists. For this study, we obtained single-nucleotide polymorphisms (SNPs) that could affect growth and sex differentiation to assist artificial selection in genetic breeding. This study entailed a genetic evaluation of 10 growth traits and one sexual trait using genome-wide association analysis (GWAS), while some significant SNPs associated with growth and sexual traits were detected. Moreover, genetic correlations were also found between traits, especially between growth and sexual traits, which greatly simplified genetic selection or made multiple joint selection convenient for more than one trait. At the same time, multiple SNPs were found to be located in two chromosomes and greatly contributed to a high heritability. These results illustrate the genetic nature of growth and sex traits in Macrobrachium nipponense. ### Abstract Male hybrid oriental river prawns grow significantly faster than hybrid females. In this study, the growth and sex traits of 181 individuals of Macrobrachium nipponense were recorded, and each individual genotype was evaluated using the 2b-RAD sequencing method. The genetic parameters for growth and sex traits were estimated. A genome-wide association analysis (GWAS) of these traits was performed. In total, 18 growth-related SNPs were detected from 12 chromosomes using a mixed linear model. The most significant loci of weight are located on the position of the SNP (102638935, chromosome 13), which can explain $11.87\%$ of the phenotypic variation. A total of 11 significant SNPs were detected on four chromosomes associated with sex trait (three on chromosome 4, one on chromosome 7 and seven on chromosome 17). The heritability of this trait is 0.8998 and belongs to the range of ultra-high heritability. Genetic correlations were prevalent among the 11 traits examined, the genetic coefficient between sex and body weight reached a significant level of −0.23. This study is the first GWAS for sex of binary and growth traits in oriental river prawn. Our results provide a set of markers for the genetic selection of growth traits and help us to further understand the genetic mechanisms of growth in Macrobrachium nipponense. ## 1. Introduction Macrobrachium nipponense is a commercial freshwater prawn species, generally known as oriental river prawn and widely distributed worldwide [1]. In aquaculture, the oriental river prawn has great potential to develop due to its strong tolerance of low temperatures in winter and fast growth rate in a typical pond culture environment. It exhibits a sexual size dimorphism starting due to sex differentiation, with male body weight reaching two or three times that of females. However, the body size of females is very small due to early sexual maturation before reaching marketable specifications. As a result, the economic benefits of a mixed culture of Macrobrachium nipponense, females and males are greatly reduced due to the growth inhibition of females. Therefore, intensive research should be directed towards the controlled establishment of prawn mono-sex populations [2]. The construction of the all-male or all-female populations via all kinds of physical, chemical or hybrid processing methods is the target of breeding for many species in which a growth dimorphism exists between male and female individuals [3,4]. However, the genetic properties of growth traits in many aquatic species are still unclear. In particular, there are few studies on the genetic correlations between growth traits and between sex and growth traits, which causes many difficulties regarding the improvement of the genetic mechanism of sex and growth traits in aquatic animals. Research on oriental river prawn mostly focused on the optimization of previous nutrition formulae [5] or the impact of certain nutrients on biochemical and physiological progress [6]. Furthermore, the candidate gene method was used to determine the gene function of similar homologous species in Macrobrachium nipponense [7]. An important breakthrough in the genome assembly of Macrobrachium nipponense was recently achieved, providing rich genomic data for genetic analysis. A genome-wide association study [8] based on a single-nucleotide polymorphism can be conducted to estimate the genetic parameters of every trait and identify genes via the position of SNP on a chromosome. this method is widely applied in animal breeding [9], disease analysis [10] and phenotype prediction [11,12]. In GWAS, according to the features of the phenotypes, phenotypes with category differences, such as diseases, were defined as binary traits [13]. The phenotypes with only numerical differences were defined as continuous traits and other special traits, such as time-to-event traits [14]. Sex, just like disease, is one of the binary traits normally analyzed using a generalized linear model [15] or generalized linear mixed model, which both account for polygene effects [16]. Generalized linear mixed-model association tests (GMMAT) [17], as well as the scalable and accurate implementation of a generalized mixed model (SAIGE) [18] based on generalized linear mixed model, have already become the first choice for the analysis of binary traits. In this paper, a SAIGE was applied to analyze the binary trait of sex since the significance test of GMMAT assumes a Gaussian distribution, which is unsuitable for the analysis of binary traits [19]. Quantitative traits [20], such as body length and its differences, were only measured as numerical values and did not have clear classification features. In this study, we used the efficient mixed-model association expedited (EMMAX) [21] method to analyze all quantitative traits related to growth. Oriental river prawn is an important aquaculture species native to South China with a huge market and economic benefits. However, few SNPs or quantitative trait loci (QTLs) of growth and sex traits were reported due to the genomic complexity of this species. We first applied genotyping by sequencing in oriental river prawn and used the methods of GWAS, SAIGE and EMMAX to obtain associated SNPs involved in sex and growth traits. The correlation coefficient between traits was evaluated, providing guidance for subsequent breeding programs. ## 2.1. Animal Population and Collection of Phenotype The populations of the tested river oriental prawn derived from the same family that underwent four generations of successive genetic selection in an aquaculture farm in Xinghua, Jiangsu, China. A total of 81 male and 100 female Macrobrachium nipponense individuals with complete phenotype records were randomly selected. Growth traits, including body weight (BW), ration of meat yield (RMY), length of beard (LB), length of hepatopancreas (LH), chest circumference (CC), length of fifth leg (LFL), body length (BL), head width (HW), tail width (TW) and length of pliers (LP) were analyzed. In addition, tissue of each sample from the test population were kept in a cryopreservation tube and placed in a refrigerator at −80 °C for DNA isolation and genotyping by resequencing. ## 2.2. Isolation of DNA and SNP Calling DNA was extracted from the tissues of samples according to a standard phenol–chloroform protocol [22]. A $1\%$ agarose gel was used to evaluate DNA quality, and a spectrophotometer was utilized to measure its concentration. Then, its concentration was adjusted to 2.5 μg/μL. The purified DNA samples were sent to be sequenced. The quality control for each sample was conducted using an NGS QC Toolkit [23]. Sequence alignment with the reference genome was performed using the software BWA0.4 [24] and SAMtools [25]. The sequences were sorted, and replicates were removed using the software, Picard (https://github.com/broadinstitute/picard accessed on 12 March 2022). The filtering of mutation sites and screening of SNP were carried out. Next, the results were converted to VCF files using GATK software (https://github.com/broadinstitute/gatk accessed on 9 May 2022). Finally, the files of binary traits and other quantitative traits comprising the genotyping dataset were produced using PLINK1.9 software (https://www.cog-genomics.org/plink2/ accessed on 18 July 2022). ## 2.3. Genome-Wide Association Analyses Classic linear mixed-model association analyses were performed using GEMMA for genome-wide association analyses [26], which treats the phenotype as a fixed factor and the additive polygenic effect as a random effect. The genome-wide, significantly associated SNPs were picked out using a Bonferroni correction, and the heritability of traits was calculated by GEMMA. A sex trait is a typical binomial trait that does not follow a normal distribution; therefore, a linear mixed model was not suitable for the gene mapping of this trait. For the above reasons, the state-of-the-art method SAIGE [18], based on a generalized linear mixed model, was selected in a genome-wide genetic analysis of sex traits of Macrobrachium nipponense. SAIGE is usually used in two steps. The first step is to fit the null logistic mixing model through genomic markers without considering any covariates. In the second step, a univariate association test was carried out for variation data according to the method of leave-one-chromosome-out. ## 2.4. Statistical Test Principal component analysis (PCA) using EIGENSOFT was performed to evaluate population structure [27]. The independent SNP dataset was analyzed via a PCA analysis using PLINK. We employed a generalized linear mixed model in our association analysis using SAIGE [28] for the binary trait of sex. The genetic correlation analysis between traits was conducted using the R package PerfomanceAnalytics [29]. The Bonferroni-corrected significance threshold (p ≤ 0.05) was used to identify the candidate SNPs associated with each trait. The tested SNP was considered as a candidate for a significant SNP when its p-value was over the threshold. Significant SNP markers were visualized in a Manhattan plot using Haploview4.2 software [30]. p-value distributions (expected vs. observed p-values on a −log10 scale) are shown in a quantile–quantile plot (Q–Q plot). ## 3.1. Phenotype and Genotype, SNP Calling and Quality Control The descriptive statistics of all the traits are shown in Table 1. The SNP calling of 181 samples was executed using a software package that includes BWA, Bcftools, Samtools, Picards, GATK, etc. After quality control assessment, 7793 SNPs were kept for further genome-wide analysis (Table 2). The significant threshold at the 0.05 level in the multiple tests was 6.42 × 10−6 ($\frac{0.05}{7793}$), and the significance threshold at the 0.01 was 1.28 × 10−4 ($\frac{0.01}{7793}$). A principal components analysis is shown in Figure S1, and the top 10 principal components (lambda > 0.5) were used for stratified population correction. ## 3.2. Genome-Wide Association Analysis A GWAS was performed for these 11 traits (1 binary and 10 quantitative) and evaluated their heritability through methods of GEMMA, respectively (Table 3 and Table 4). The heritabilities of 10 growth traits ranged from 0.16 (BL) to 0.99 (BW, RMY), indicating that most of them had a medium and high heritability. For the genome-wide association analysis of binary sex trait, three SNPs on chromosome 4, one SNP on chromosome 7, and seven SNPs on chromosome 17 were found. Detailed information on these candidate SNPs is shown in Table 3, and the visual results of GWAS are shown in Figure 1. The heritability of the sex trait was 0.8998, which was significantly higher than the sum of the heritability (0.7577) of all SNPs detected. This result indicates that there was interaction between genes or among multiple SNPs, which were possibly involved in the development of sex traits. Candidate SNPs associated with sex traits were found on the same chromosome with close relative distances and similar significance levels, suggesting that linkage disequilibrium may interfere with the results of GWAS analysis (Table 3). For quantitative growth traits, significant SNPs were detected for each trait in the quantile–quantile plot (Q–Q plot) and Manhattan plot (Figure 2). At the same time, no significant SNPs were detected for some growth traits: RMY, LB, CC, LRL, HW, TW, LP (Figure 3, Table 4). In total, 18 SNPs were detected for three growth traits: one SNP was associated with body weight, four were associated with length of hepatopancreas, and thirteen were associated with body length, according to GEMMA-0.98-1 software (Table 4). The positions of candidate SNPs on chromosomes are shown in Table 5. ## 3.3. Correlation Analysis among Traits A genetic correlation analysis showed that there was a general genetic correlation among these traits (Figure 4), in which sex traits had a significant negative correlation with other growth traits, except LH and BL. Most of the other growth traits had a significant genetic correlation, among which the trait of body weight had a very strong correlation with other growth traits, except LH. The correlation coefficients between BW and others were 0.86 (RMY), 0.71 (LB), 0.76 (CC), 0.74 (FL), 0.33 (BL), 0.90 (HW), TW (0.83) and 0.89 (LP), respectively, indicating a high genetic correlation between body weight and other growth traits. ## 4. Discussion Sex is one of the many limitations of efficiency in aquaculture farming [30], and sex differences often lead to large differences in growth performance regarding body weight, body length and growth rate. In general, males have a faster growth rate [2] than females; therefore, breeders use special methods such as sexual reversal to achieve all-male breeding and reduce economic losses caused by the slow growth of females [31]. Thus far, studies related to the sex differentiation [32] of Macrobrachium nipponense focused on the cloning and functional analysis of candidate genes [33,34], which have a potential influence on sex determination [35]. However, genetic-associated SNPs across the whole genome remain unclear, which limits the utilization of these genes and selectable markers for breeding. We identified ten significant SNPs associated with sex traits across the genome, mainly located on chromosomes 4 and 17 (Figure 1 and Table 3). The total heritability of the sex trait reached 0.8998, indicating a super high heritability that facilitates easier breeding using the genetic nature of loci associated with sex traits. A GWAS of the growth-related traits was carried out using GEMMA, and eighteen SNPs associated with the growth traits were identified across the whole genome. Of these significant SNPs, one is associated with body weight. The estimated heritability of the sex trait reaches 0.99, indicating a super-high heritability. Thirteen SNPs were identified associated with BL and four SNPs associated with LH trait, the estimated heritability of them is 0.45 and 0.16, respectively, they exhibited high and moderate heritability, respectively. The estimated heritabilities of other growth traits were generally high, indicating that the effect of genetic factors for each trait was much greater than that of environmental factors, which is conducive to genetic selection by genome selection and provides theoretical support for the genetic breeding of Macrobrachium nipponense. This is the first study concerning Macrobrachium nipponense to identify SNPs associated with sex and growth traits at a genome-wide level. Our results showed that sex and body weight traits are highly genetically correlated; the genetic correlation between sex and BW trait was 0.23, which indicates a significantly negative correlation, meaning that the detected QTNs can participate in the processes of both sex and growth development. Furthermore, the location of these candidate QTNs can be used for functional gene mapping for target trait control, which is one of the main functions of GWAS. This high correlation between growth traits was similar to the correlations found for Pacific abalone (Haliotis discus hannai.) [ 36]. Interestingly, the genetic correlation between weight and other growth traits was much more positive, which is very suitable for selection breeding in oriental river prawn. The advantage of a high genetic correlation among traits of Macrobrachium nipponense can provide a more measurable index and greatly simplify the complexity of genetic selection, which is also used in the selection of the feed conversion of abalone [37]. The significant correlation between various growth traits of Macrobrachium nipponense showed that, in the online GWAS using GEMMA [37], multiple growth traits can be jointly selected at the whole-genome level, but this was not suitable for binary traits with other growth traits. The reasons for this could be as follows: First, GEMMA is designed for quantitative traits and is not suitable for binary traits, such as sex traits or survival traits. In addition, the calculation rate is greatly reduced when analyzed in combination with growth traits. The heritability [38,39] of the traits is the basis of genetic selective breeding. Heritability (mathematically expressed as h2) is the ratio of genetic variance to total variance. The total variance is also known as the phenotypic variance (the sum of genetic variance and environmental variance). Heritability can also be understood as the influence of genetic factors on traits. The values of heritability calculated using the ratio of genetic to phenotypic variance are not equal to the total variance of all SNPs, which may be due to a number of reasons: firstly, the genetic variance was calculated from all genetic markers, some markers are not significant when tested individually. Secondly, the missing heritability perhaps suggests that the linkage disequilibrium (LD) heterogeneity among regions has an adverse effect on genomic prediction and heritability estimation. As illustrated in our study, the estimated heritability of BW reached 0.99, which was higher than that of the total SNPs (0.91), and such similar cases of “missing” or “hidden” heritability are mentioned in several studies [40,41,42]. On chromosome 17, seven SNPs were found to have significant genetic correlation with sex traits at the whole genome level, indicating that some SNPs had relatively significant effects on chromosome 17. These SNPs enhanced the effects of other detected SNPs that were not significantly correlated and caused them to have a significant genetic correlation. Such a linkage disequilibrium affects the interpretation of GWAS [42]. In addition, there may be interactions between markers that contribute to genetic variance. Since the interaction effect is widespread at the whole genome level, a single step analysis cannot detect such an effect, but it will increase the genetic variance; therefore, the interaction effect often interferes with the analysis of GWAS, which is why a GWAS analysis should be conducted from multiple perspectives. Therefore, improving the detection efficiency and accuracy of GWAS is essential. In conclusion, this study helps us to further understand the genetic structure of growth and sex traits of oriental river prawn by GWAS. In this study, a total of 11 SNPs related to sex traits were found, suggesting that some genes near these loci may have a significant sex differentiation potential. These candidate genetic markers of SNP may provide valuable resources for further understanding sex determination in oriental river prawn. In addition, 18 SNPs related to growth traits, such as BW, LH and BL, were detected in this study. Interestingly, these SNPs may also affect sex differentiation, based on the results of the correlation relationship between sex and other growth traits. This discovery provides a convenient tool for genetic selection and makes the breeding process more efficient. ## 5. Conclusions In summary, for the first time, we found some SNPs that were significantly correlated with growth traits and sex traits of oriental river prawn at a genome-wide level. We also found that there was a wide range of high genetic correlations between growth traits and between growth and sex traits. Some growth traits and sex traits of oriental river shrimp not only have a medium and super high heritability, they also demonstrate a high genetic correlation among traits, which provides a more convenient and efficient scheme for genetic breeding, such as multi-trait joint selection. This study provided a basis for understanding the genetic mechanism of the disparity in growth performance between male and female in Macrobrachium nipponense. With the publication of detailed genomic data in the future, the genetic markers associated with these traits will be developed into genetic units that can be used for different purposes: genetic selection, identifying genes and their traits, exploring gene networks, and studying their biological functions. Therefore, we can facilitate the genetic selection and improvement of production efficiency for Macrobrachium nipponense. ## References 1. Yu J., Sun J., Zhao S., Wang H., Zeng Q.. Transcriptome analysis of oriental river Prawn (. 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--- title: Applicability of International Autoimmune Hepatitis Group (IAIHG) Scoring System for Autoimmune Hepatitis in Pediatrics authors: - Vorada Sakulsaengprapha - Paul Wasuwanich - Gayathri Naraparaju - Yelena Korotkaya - Supharerk Thawillarp - Kiyoko Oshima - Christine Karwowski - Ann O. Scheimann - Wikrom Karnsakul journal: Biology year: 2023 pmcid: PMC10045027 doi: 10.3390/biology12030479 license: CC BY 4.0 --- # Applicability of International Autoimmune Hepatitis Group (IAIHG) Scoring System for Autoimmune Hepatitis in Pediatrics ## Abstract ### Simple Summary Autoimmune hepatitis (AIH) is a difficult liver disease to diagnose, and researchers developed the International Autoimmune Hepatitis Group (IAIHG) scoring system to aid the diagnosis of AIH. The scoring system was originally designed for adult patients; thus, we aim to evaluate the performance of this scoring system in children for accurately diagnosing AIH. We found liver biopsies were an essential component of the IAIHG scoring system and that specific liver biopsy features including interface hepatitis and predominant plasma cells were significantly associated with AIH. Incorporating liver biopsy data improves the performance of the IAIHG scoring system. Although, the degrees of importance of each specific biopsy feature are more distinct in the children population compared to those of the adult population. Overall, we determined that the IAIHG score is effective at diagnosing AIH in children, but could be improved. ### Abstract Introduction: Many hepatologic pathologies mimic autoimmune hepatitis (AIH). Researchers developed the International Autoimmune Hepatitis Group (IAIHG) scoring system to compensate for the lack of specific diagnostic tests for AIH. The scoring system was not designed with pediatric patients in mind, so there are limits to its pediatric use. Additionally, there is limited information on the value of a liver biopsy in conjunction with its use. Methods: In this retrospective study, we evaluated the effect of liver biopsy scores on the IAIHG scoring system in patients that were 0–18 years old with suspected AIH. We also analyzed demographic data and laboratory values associated with a final AIH diagnosis. Results: We found that interface hepatitis and predominant plasma cells found during the biopsy were significantly associated with a final AIH diagnosis. We also found that abnormal laboratory values were associated with an AIH diagnosis. We found that IAIHG scores calculated post-liver biopsy showed a greater area under the receiver operating characteristic curve (AUROC) of 0.95, which was compared to 0.88 for the scores calculated before a liver biopsy. Including biopsy metrics lowered the optimized cutoff score and test specificity. Conclusion: Incorporating liver histopathological features improved the performance of the IAIHG scoring system. Further studies to identify other potential elements in liver histology may improve the performance metrics of the IAIHG test in the pediatric population. ## 1. Introduction It is challenging for clinicians to diagnose autoimmune hepatitis (AIH) because there is no diagnostic gold standard for this disease. The International Autoimmune Hepatitis Group (IAIHG) developed a scoring system in 1992 and updated it in 1999. The scoring system uses clinical history, biochemistry, serologic tests for viral hepatitides, autoimmune markers, and liver histopathologic findings [1]. The 1999 update improved the ability to exclude a diagnosis of AIH in patients with autoimmune biliary diseases such as primary biliary cholangitis and primary sclerosing cholangitis [2,3]. Clinicians apply the IAIHG scoring system to patients with a high suspicion index for AIH. The results guide subspecialty referral decisions. The overall diagnostic sensitivity and specificity for the IAIHG scoring system range from $97\%$ to $100\%$ and $44\%$ to $87\%$, respectively [4]. However, multiple elements of the IAIHG scoring system lack relevance for pediatric patients. For example, alcohol intake is less relevant when one is evaluating children [5]. Further, gamma-glutamyl transpeptidase (GGT) is a more sensitive and specific indicator of cholestasis in pediatric biliary disorders than alkaline phosphatase (ALP) is. Growing children often have mild elevations in ALP levels due to bone-related longitudinal growth. Additionally, non-alcoholic fatty liver disease (NAFLD) is increasing worldwide across pediatric age groups. Overlapping clinical and biochemical presentations between NAFLD and AIH populations reduces the effectiveness of IAIHG scoring [6]. The study’s objective is to evaluate the relevance of liver histopathology features when they are used with IAIHG scoring to assess children with suspected AIH. ## 2.1. Study Population We conducted a retrospective study of patients from 0 to 18 years of age evaluated and treated at Johns Hopkins Children’s Center. These patients had an initial diagnosis of AIH, with or without sclerosing cholangitis or immune-mediated cholangitis, between 1 January 1990 and 24 May 2019. The study was approved by the Institutional Review Board at Johns Hopkins University School of Medicine. All patients were initially suspected of having autoimmune hepatitis with or without primary sclerosing cholangitis. Their final diagnosis as AIH (case group) was made based on clinical history, blood laboratory tests, liver histopathology, and their responses to AIH treatment. The control group consisted of patients under investigation for AIH who did not have a final diagnosis of AIH. We excluded patients with uncorrected coagulopathy at the time of diagnosis (and thus, with an increased bleeding risk) and patients who did not undergo a liver biopsy examination before treatment initiation. We also excluded patients with cholelithiasis and underlying immunodeficiencies such as agammaglobulinemia or severe combined immunodeficiency syndrome. ## 2.2. Data Collection We collected demographics from the time of diagnosis (i.e., race, sex, and age). We also collected clinical data, including body mass index, fasting lipid panel, insulin level, hemoglobin A1C, serum ALP level, aspartate transaminase (AST) level, alanine transaminase (ALT) level, GGT level, serum total, and direct bilirubin levels, total immunoglobulin G (IgG) level, antinuclear antibody (ANA) level, anti-smooth muscle antibody (ASMA) level, anti-mitochondrial antibody (AMA) level, IgG subclasses, hepatitis viral markers, drug history, average alcohol intake, liver histology at diagnosis, other autoimmune diseases, family history of autoimmune diseases, other defined autoantibodies, and HLA-DR carrier status if they had been tested. Autoantibody tests were conducted at our institution using indirect immunofluorescence, and the results are reported in titers [7]. Positivity for an autoantibody was determined using cutoff values from our institution’s laboratory. For each patient, we calculated a score using the IAIHG revised scoring system; the score was based on laboratory values, drug and alcohol use, histopathology, and response to therapy. Based on the score, patients were classified as definitely having AIH, probably having AIH, or other, both before and after the AIH treatment. Definitely having AIH requires scores of greater than 15 pre-treatment and 17 post-treatment, and probably having AIH requires scores of 10–15 pre-treatment and 12–17 post-treatment if they were receiving treatment for AIH [4]. Response to AIH treatment was determined using pre- and post-treatment IAIHG scores. In addition to the IAIHG scoring system, we also evaluated a more recently proposed, simplified AIH scoring system that has been externally validated in adult patients by Muratori et al. [ 8]. Histopathology data were reviewed by our senior pathologist, K.O. Histopathological features that were included in the IAIHG scoring system including interface hepatitis, predominant lymphoplasmacytic infiltrate, rosetting of liver cells, and biliary changes were of particular focus. We based the diagnosis of sclerosing cholangitis on the findings of magnetic resonance cholangiopancreatography (MRCP) or endoscopic retrograde cholangiopancreatography (ERCP). We diagnosed overlap syndrome if the patient had clinical and histopathologic findings that were consistent with AIH and had sclerosing cholangitis confirmed by MRCP or ERCP. We also collected IAIHG scores taken before and after liver biopsies to determine the impact of biopsies on the scores. ## 2.3. Statistical Analysis We summarized data using frequencies with percentages or medians with interquartile ranges (IQRs). We analyzed associations using logistic regression. We constructed receiver operating characteristic (ROC) curves and the area under the ROC curve (AUROC). We used the ROC curves to determine optimal cutoffs for how well IAIHG scores predicted the final diagnosis of AIH. We defined optimal cutoffs as having the highest summation of specificity and sensitivity. We tested normality with the Shapiro–Wilk test, where $p \leq 0.05$ indicated non-normal data. We reported non-normal data as the median and IQR. We performed analyses using Program R Version 3.2.3 (R Foundation for Statistical Computing, Vienna, Austria). We assumed that missing data were randomly missing. All reported p-values were two-sided, and $p \leq 0.05$ was considered to be statistically significant. ## 3. Results We included a total of 61 patients in our study, including those in the control group (Table 1). The median age of this group was 13 (IQR: 8–16), with 32 ($52\%$) of them being female. The race distribution was $51\%$ White, $20\%$ Black, and $30\%$ other or unknown. From this cohort, we identified a total of 32 children who had a diagnosis of AIH (Table 1). Of the 32 children, 20 ($63\%$) were female, and 12 ($38\%$) were male. The median age was 14 years (IQR: 8–15), ranging from 1 to 17 years (Table 1). None of the patients were subjected to HLA-DR testing. There were 29 patients in the control group, and they had the following diagnoses: NAFLD (14; $48\%$), drug-induced liver injury (3; $10\%$), primary sclerosing cholangitis (2; $7\%$), parenteral nutrition-associated liver disease (2; $7\%$), and liver enzyme elevation with a normal liver biopsy (8; $28\%$). We determined the AUROC for IAIHG score variants (Table 2, Figure 1 and Figure 2). The pre-liver biopsy IAIHG scores had an AUROC of 0.88 ($95\%$ CI: 0.80–0.97), and the pre-treatment scores (post-liver-biopsy) had an AUROC of 0.95 ($95\%$ CI: 0.91–1.00). We could not generate AUROC curves post-treatment, given the absence of AIH treatments in the control group. The optimized cutoff was ≥7 for the pre-treatment scores, with $97\%$ sensitivity and $81\%$ specificity. The optimized cutoff was ≥9 for the pre-liver-biopsy scores, with $72\%$ sensitivity and $94\%$ specificity. We also evaluated a simplified AIH scoring system and found the AUROC to be 0.91 ($95\%$ CI: 0.84–0.98). The optimized cutoff was ≥5, with $69\%$ sensitivity and $97\%$ specificity. We analyzed the sensitivity and specificity of historic IAIHG cutoffs for both definite and probable AIH. Definite AIH cutoffs for the pre-treatment scores (>15) had $24\%$ and $97\%$ sensitivity and specificity, respectively. Probable AIH cutoffs for the pre-liver biopsy scores (10–15) had $59\%$ and $97\%$ sensitivity and specificity, respectively. Probable AIH cutoffs for the pre-treatment scores (10–15) had $52\%$ and $94\%$ sensitivity and specificity, respectively. We also evaluated the cutoffs provided by the creators of the simplified AIH scoring system, with a cutoff of ≥6 for probable AIH and a cutoff of ≥7 for definite AIH. Applying the probable cutoff yielded a sensitivity of $59\%$ and a specificity of $97\%$, while the definite cutoff yielded a sensitivity of $31\%$ and a specificity of $97\%$. Certain race categories formed a risk factor for AIH (Table 3). More specifically, Black patients were more likely to be diagnosed with AIH than other groups were (OR: 7.89, $$p \leq 0.013$$), while patients who identified as Asian or an unknown race were not likely to have the diagnosis (OR: 0.07, $$p \leq 0.001$$). Age and sex were not associated with AIH diagnosis. None of the patients had a history of alcohol consumption. Of note, two patients were younger than five years of age and were later diagnosed with very early onset inflammatory bowel disease. One of these patients was diagnosed with overlap syndrome after AIH treatment. One patient developed systemic lupus erythematosus after the AIH treatment. Co-infection with hepatitis and other viruses was not associated with a final diagnosis of AIH (Table 3). Laboratory values such as ANA, ASMA, elevated total serum IgG, AST, GGT, ALP, total and direct bilirubin, hemoglobin, and INR were associated with a final diagnosis of AIH. In contrast, other values such as anti-LKM-1, AMA, p-ANCA, ALT, C-reactive protein, erythrocyte sedimentation rate, serum ceruloplasmin, alpha-1 antitrypsin, white blood cell count, and platelet count were not found to be significantly associated with an AIH diagnosis (Table 3). Of those with a final diagnosis of AIH, the histopathologic report indicated that 13 patients ($41\%$) had interface hepatitis, 23 ($72\%$) had predominant plasma cells, 8 ($25\%$) had biliary changes, and 15 ($47\%$) had hepatic fibrosis and regenerating nodules. Of these, interface hepatitis and predominant plasma cells were associated with a final diagnosis of AIH (Table 3). The feature with the highest sensitivity and specificity was predominant plasma cells ($72\%$ and $97\%$, respectively) (Table 4). Biopsies interpreted as being consistent with AIH had the same sensitivity and specificity (Table 4). ## 4. Discussion Autoimmune hepatitis (AIH) is a chronic, progressive immune-mediated inflammatory liver disorder. Its initial presentation can be acute, subacute, or chronic. AIH is considered to be rare in children and adolescents, so it can be missed due to a low index of suspicion [9]. There are three main types of AIH that can be distinguished by liver autoantibodies [10]. Type 1 (AIH-1) is characterized by antinuclear (ANA) or anti-smooth muscle (ASMA) antibodies. Type 2 (AIH-2) is characterized by antibodies to liver-kidney microsome type 1 (anti-LKM-1) or antibodies to liver cytosol type 1 (anti-LC1). Type 3 (AIH-3) is characterized by anti-soluble liver antigen/liver-pancreas antibodies. AIH is extremely rare in patients under two years of age. The highest incidence occurs between 10 and 30 years old, affecting females more than it does males [11,12,13]. While the AIH can occur in all decades of life, there are several notable distinctions in the clinical and laboratory features. AIH in elderly patients tends to be more asymptomatic, more likely to be associated with a positive ANA, and more likely to be associated with HLA-DR4 [14]. It has been theorized that the high frequency of ANA in elderly/older patients reflects the increased incidence of autoantibodies with age in the normal population [14]. AIH is clinically characterized by hypergammaglobulinemia, elevated liver enzymes, the presence of autoantibodies, and histological changes. Its diagnosis is confirmed by clinical findings, laboratory and histopathology tests, and the exclusion of other causes of chronic liver disease [11,12]. In our study, high laboratory values (i.e., serum IgG, ANA, and ASMA) were associated with a final diagnosis of AIH. ANA, ASMA, and anti-LKM-1 have been found to constitute the conventional serological repertoire for an AIH diagnosis [15]. ANA has been found to be present in $80\%$ of White North American adults with AIH at presentation; $63\%$ have positive ASMA and $3\%$ have positive anti-LKM-1 [15], which may reflect our findings of ANA and ASMA being significantly associated with an AIH diagnosis. In our study, only one ($3\%$) patient from the AIH group had anti-LKM-1, which is a similar to rate to that reported in the adult AIH population; the low case number of patients with anti-LKM-1 is the primarily reason for lack of statistical significance in that variable in our study. The low prevalence of anti-LKM-1 in our study could be potentially explained by distinct genetic backgrounds in different geographic locations. Muratori et al. reported that anti-LKM-1 rarely occurs in North America, which is likely related to the lower frequency of HLA DR7 in North America compared to that in the Italian population [16]. Autoantibodies may be negative or present at low titers at the disease onset stage [17]. Yet, autoantibodies may become detectable at a later follow-up with acute or fulminant presentations before liver biopsy procedures. Measuring autoantibody titers during this later period may improve both the sensitivity and specificity. Laboratory values such as AST, GGT, ALP, total and direct bilirubin, hemoglobin, and INR were significantly associated with a final diagnosis of AIH (Table 3). Occasionally, AIH can sometimes present with a cholestatic picture [18]. Additionally, certain laboratory values, such as ALP and GGT, may also be indicative of overlapping features of AIH with other entities such as primary biliary cholangitis. This mixed picture emphasizes the importance of including diagnostic parameters such as history, biochemical markers, and biopsy findings reflected in scores. In our study, we investigated both ALT and AST; however, only AST was found to be significantly associated with AIH. While ALT and AST are related enzymes, their distribution across the body is unique. AST isoenzymes are present in the mitochondria and cytosol of cells and can be found in the liver, skeletal muscle, cardiac muscle, kidneys, brain, pancreas, lungs, leukocytes, and red blood cells. On the other hand, ALT is a cytosolic enzyme that mainly occurs in significant concentrations in the liver. As such, ALT has been generally considered to be more sensitive and specific for liver disease and injury [19]. Despite that, AST is more commonly used in AIH diagnosis and disease monitoring [20]. A major reason is the shorter half-life of AST (approximately 17 h) compared to that of ALT (approximately 47 h) [21]. Because AIH is an ongoing and progressive liver injury, the shorter half-life of AST makes it relatively more useful than ALT is. AST is superior for measuring the current state of liver inflammation and injury, being more associated with diseases where liver inflammation and injury are continuous and not intermittent. As such, even though both ALT and AST levels were elevated in our cohort, AST elevations were more likely to be specific to pediatric patients with AIH rather than the controls with non-AIH liver disease. Diseases such as hepatitis A, B, C, E, Wilson’s Disease, NAFLD, and drug-induced liver injury (DILI) share histopathologic features with AIH, including false-positive liver autoantibodies [17]. Excluding these diseases is vital before a diagnosis of AIH can be made. Some of these pathologies were present in a minority of our AIH cohort, but were not the primary etiology of liver disease. In order to improve the exclusion ability of the IAIHG scoring system, we propose the utilization of RUCAM (Roussel Uclaf Causality Assessment Method), a scoring system commonly used to quantify the likelihood of DILI [22]. Currently, the IAIHG scoring system only includes a binary option of hepatotoxic drug history; the integration of RUCAM could potentially improve the performance of the IAIHG scoring system by quantifying the likelihood of significant drug involvement in the liver disease or could be used to exclude or confirm hepatotoxic or potentially hepatotoxic drugs for liver disease involvement. NAFLD, a common liver disease, is associated with elevated ASMA and total serum IgG levels [23]. In NAFLD, we see a female predominance, elevated autoantibodies, the presence of ANA and ASMA, polyclonal hypergammaglobulinemia, interface hepatitis on biopsy, and a good response to immunosuppression [15,24,25]. NAFLD patients could be falsely diagnosed with AIH due to these overlapping characteristics. Histopathologic findings from a liver biopsy are standard criteria for diagnosing AIH in children [17,18]. We used a pre-liver-biopsy score to determine whether the IAIHG scoring system was sensitive enough to predict AIH diagnosis without information from a liver biopsy. We achieved an AUROC of 0.88, with a sensitivity of $72\%$ and specificity of $94\%$, using an optimized cutoff score of ≥9 (Table 2). Pre-treatment IAIHG scores using histopathologic features outperformed the pre-liver-biopsy scores, with an AUROC of 0.95, sensitivity of $97\%$, and specificity of $81\%$, using a lower optimized cutoff score of seven (Table 2). This provides strong evidence that using liver biopsy within the IAIHG system helps to predict AIH diagnosis in children with abnormal liver enzymes. When one is including biopsy findings, the scoring system performed well for ruling in AIH, and it was even better at ruling out non-AIH diagnoses. As the IAIHG scoring system is rather extensive, a simplified scoring system for AIH has been proposed. This scoring system was developed by Hennes et al. in 2008 [26], and it was externally validated by Muratori et al. the following year in an Italian adult population [8]. Muratori et al. reported the overall sensitivity and specificity for the AIH diagnosis at a cutoff score of ≥6 to be $91.8\%$ and $94.3\%$, respectively [8]. In our pediatric cohort, while we reported similar specificities at the cutoff of ≥6, the sensitivity was found to be notably lower in our cohort, $59\%$. However, with an AUROC of 0.91 in our study, this simplified AIH score has potential for application in the pediatric population. With an optimized cutoff of ≥5, the sensitivity was increased to $69\%$, while maintaining a specificity of $97\%$. A lower cutoff for this simplified AIH scoring should be considered when one is applying it to the pediatric population. Nevertheless, as the sensitivity is relatively low even when it has been optimized, the score may be more useful for confirming a diagnosis of AIH rather than ruling out non-AIH diagnoses. We found that interface hepatitis and a predominance of plasma cells predicted an AIH diagnosis (Table 3), which is consistent with previous studies identifying these features as hallmarks of AIH [15]. Interface hepatitis is characterized by dense inflammatory infiltrates composed of lymphocytes and plasma cells. However, it is important to note that histopathological features are not diagnostic in isolation; while they are common in pediatric patients with AIH, they are not exclusive to AIH [15,17,27]. Compared to the current IAIHG scoring system, which provides more weight to interface hepatitis than predominantly lymphoplasmacytic infiltration, our study found the opposite, with a predominance of plasma cells being more strongly associated with AIH. Emperipolesis, the presence of an intact cell within another cell, is another histopathological feature that has been widely described in patients with AIH, although it is not explicitly included in the IAIHG scoring system. Miao et al. conducted a retrospective histological evaluation of 101 patients with AIH with 184 controls using confocal staining for CD4, CD8, CD56, CK$\frac{8}{18}$, and cleaved caspase-3 [27]. They reported emperipolesis in $65\%$ of the patients with AIH using hematoxylin and eosin-stained slides, which was significantly higher than in the patients with a drug-induced liver injury ($26\%$), primary biliary cirrhosis ($18\%$), and chronic hepatitis B ($15\%$). Additionally, they found that emperipolesis was associated with more advanced fibrosis and more severe necroinflammatory features. The emperipolesis of CD8 T cells induced cleaved caspase-3 expression and was prominent in areas of apoptosis. Emperipolesis is a characteristic feature of AIH, which is often seen in conjunction with interface hepatitis, plasmocytic infiltration, and hepatocyte rosetting and is associated with more severe necroinflammatory and fibrotic changes. Emperipolesis is predominantly mediated by CD8 T cells in AIH, and it appears to induce apoptosis and may be another mechanism of autoimmune-mediated hepatocyte injury. Miao et al. reported that the combination of emperipolesis with interface hepatitis, plasma cell infiltrates, and hepatocyte rosettes achieved a sensitivity of $81\%$ and a specificity of $84\%$ for diagnosing AIH [27]. However, a detailed look at the data published from many studies suggested that these two features carry a lower sensitivity due to difficulties in being identified or determined by light microscopy. Further study using immunostaining of CD8 T cells, along with confocal or electron microscopy, may help to assess the importance of emperipolesis. In recent years, novel non-invasive biomarkers for AIH have been reported, but none of them have yet become part of routine clinical practice nor have replaced the liver biopsy. These biomarkers include adenosine deaminase, cytokeratin-18 death marker m65, transforming growth factor-ß1, tumor necrosis actor family B-cell activating factor (BAFF), anti-asialoglycoprotein receptor, FOXP3/RORɣt ratio, DNAse 1, ferritin, CD74:MIF ratio, and the vitamin D receptor [28]. The study was limited by a small sample size, variability in laboratory data, and its retrospective nature. By increasing sample size, the true effect of other factors such as race may be further elucidated as well; race is increasingly being recognized as a social construct, and the differences seen in this study may be more representative of socioeconomic status rather than a true biological difference. Given the lack of HLA data, we could not determine their significance in the IAIHG scoring system. Currently, HLA data are optional additional parameters of the score calculator. To improve the efficacy and efficiency of the IAIHG scoring system, follow-up research should gather additional data regarding the importance of including or excluding the HLA status. ## 5. Conclusions This study provides evidence of the utility of the IAIHG scoring system in the pediatric population and the importance of liver histopathology from biopsy for confirming the diagnosis of AIH and excluding other diagnoses, but not all liver histopathological features were equally predictive of AIH, and weights may need to be adjusted for the pediatric population. While the IAIHG scoring system includes some parameters that are not applicable to the pediatric population, such as alcohol use, many of its parameters were significantly associated with an AIH diagnosis. Further studies are needed to identify other elements related to liver histopathology. Studies on using HLA data to modify the IAIHG score could also increase the score’s specificity for diagnosing AIH in pediatric patients. ## References 1. Johnson P.J., McFarlane I.G.. Meeting report: International autoimmune hepatitis group. Hepatology (1993) 18 998-1005. DOI: 10.1002/hep.1840180435 2. 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--- title: 'cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network' authors: - Huidong Ma - Cheng Zhong - Danyang Chen - Haofa He - Feng Yang journal: BMC Bioinformatics year: 2023 pmcid: PMC10045035 doi: 10.1186/s12859-023-05243-x license: CC BY 4.0 --- # cnnLSV: detecting structural variants by encoding long-read alignment information and convolutional neural network ## Abstract ### Background Genomic structural variant detection is a significant and challenging issue in genome analysis. The existing long-read based structural variant detection methods still have space for improvement in detecting multi-type structural variants. ### Results In this paper, we propose a method called cnnLSV to obtain detection results with higher quality by eliminating false positives in the detection results merged from the callsets of existing methods. We design an encoding strategy for four types of structural variants to represent long-read alignment information around structural variants into images, input the images into a constructed convolutional neural network to train a filter model, and load the trained model to remove the false positives to improve the detection performance. We also eliminate mislabeled training samples in the training model phase by using principal component analysis algorithm and unsupervised clustering algorithm k-means. Experimental results on both simulated and real datasets show that our proposed method outperforms existing methods overall in detecting insertions, deletions, inversions, and duplications. The program of cnnLSV is available at https://github.com/mhuidong/cnnLSV. ### Conclusions The proposed cnnLSV can detect structural variants by using long-read alignment information and convolutional neural network to achieve overall higher performance, and effectively eliminate incorrectly labeled samples by using the principal component analysis and k-means algorithms in training model stage. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12859-023-05243-x. ## Background According to the number of base pairs (bp) changed, genomic variants can be divided into single-nucleotide polymorphisms (SNPs), short insertions and deletions (Indels), and structural variants (SVs). Different from SNPs and Indels, SVs consist of more varied nucleotides. In addition, SVs often have a greater impact on organisms [1]. Various diseases have been confirmed to be closely related to genomic SVs [1, 2]. The accurate detection of multi-type SVs can provide data support for subsequent genomic analysis to uncover the relationship among SVs, gene expression and human evolution. However, the detection of structural variants still faces some challenges, such as inevitable errors from sequencing technologies and sequence alignment tools, and more complex SV types including insertions (INSs), deletions (DELs), inversions (INVs), duplications (DUPs), and translocations (TRAs) [3]. Therefore, genomic structural variant detection is an essential and challenging issue. Compared with the short reads generated by next-generation sequencing (NGS) platforms, the long reads generated by the third-generation sequencing (TGS) technologies can not only more accurately align with the genome, but also cover entire SV and describe the breakpoints [4]. Existing studies have shown that long-read based callers perform better than short-read based callers in human genomic SV detection [4, 5]. Sniffles [6] first estimates and adjusts the parameters to fit the datasets used, then extracts the features of each SV and performs clustering to distinguish SVs types. The tool PBSV developed by PacBio [7] realigns abnormal reads to the reference genome to further analyze types of SVs. SVIM [8] transforms features from intra-alignment and inter-alignment information into graphs, and executes graph clustering method to classify SVs. CuteSV [9] extracts features of different kinds of SVs, uses feature similarity based unsupervised clustering method to cluster features and recognize the types of SVs, and applies clustering-and-refinement approach to improve the accuracy of breakpoints to reduce the number of false positives in detection results. In addition to the four classical multi-type SV detection methods mentioned above, there are also some callers for specific SV type or sequencing data, such as rMETL [10], NanoVar [11] and SKSV [12]. However, the performance of some existing methods using long-read alignment information depends on selection of features and parameters for detecting multiple types of SVs. Convolutional neural network (CNN) [13] is a classical deep learning model commonly used for the analysis of two-dimensional data (e.g., images), and has been widely applied to researches in biomedical field such as skin segmentation [14], brain tumor classification [15], and diabetic retinopathy severity level prediction [16]. In recent years, deep learning models like CNN have also been applied to the detection of genomic variants similarly. By encoding alignment information for short reads to represent variant features, DeepVariant [17] applies the neural network to detect SNPs and Indels for the first time. DeepSV [18], DeepSVFilter [19] and Cnngeno [20] use the short-read alignment information to encode DELs and filter the detection results to improve accuracy. In Clairvoyante [21], a multi-task convolutional neural network model was proposed to overcome the impact of long-read sequencing errors on SNP detection and realize genotyping. InvBFM [22] and BreakNet [23] use support vector machine (SVM) and bidirectional recurrent neural network (BRNN) to detect INVs and DELs, respectively. And SVision [24], one deep learning based multi-object-recognition framework, was proposed to detect complex structural variants from long reads. If appropriate feature encoding strategy can be designed, the deep learning based detection method will autonomously learn the features of variants and have the fault-tolerant ability in detecting variants to a certain extent. Most of the existing deep learning based methods mainly focus on a specific type of variants. Hence, it is very promising to further investigate multi-type structural variant detection methods using long-read alignment information. The main contributions of our work are as follows: An encoding strategy for four types of structural variants is designed, and a method called cnnLSV is proposed to detect multi-type structural variants by encoding long-read alignment information and using convolutional neural network. The cnnLSV integrates the initial results of existing callers to a callset to improve detection sensitivity, constructs a classification model to filter the obtained callset to eliminate false positives to improve detection precision. To increase the training effect and prediction accuracy, the combined use of principal component analysis (PCA) [25] and unsupervised clustering algorithm k-means [26] is conducted to eliminate samples with incorrect labels in the training model stage. The remainder of this paper is organized as follows. “ Methods” section describes the proposed detection method in detail. “ Results” section reports experimental results on the simulation and real datasets. “ Discussion” section discusses the advantages and shortages of our proposed method. “ Conclusion” section concludes the paper and gives future research directions. ## Procedure of proposed method We propose a long-read based SVs detection method shown in Fig. 1.Fig. 1Excuting procedure of our proposed method. Conv and FC represent convolutional layer and fully connected layer, respectively The proposed method includes two stages of training filtering model and detecting structural variants. The initial datasets of the detection methods consist of the long-read alignment files, which are divided into dataset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Dset_{1}$$\end{document}Dset1 for training model and dataset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Dset_{2}$$\end{document}Dset2 for detecting. In the training model stage, we first execute existing callers on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Dset_{1}$$\end{document}Dset1 to obtain initial detection results and merge the results into one callset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{1}$$\end{document}Cset1, compare \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{1}$$\end{document}Cset1 with the ground truth set containing real variant information during the training model to obtain the training sets, and select true positives and false positives as the positive and negative training samples respectively. Secondly, we encode long-read alignment information around SVs into images according to the encoding methods related to different kinds of SVs. Thirdly, we execute the PCA and the k-means algorithms to eliminate the negative samples whose features are similar to that of positive samples due to label errors to obtain filtered training sets. Finally, the filtered training sets are input into CNN to train the classification model. In the SV detection stage, we first execute existing callers on the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Dset_{2}$$\end{document}Dset2 to obtain the initial detection results, and merge the results into another callset \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2. Secondly, we encode the long-read alignment information around SVs in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2 into one image set Imgset. Finally, we use the trained model to filter Imgset to obtain the final detection results. ## Encoding long-read alignment information The performance of some existing long-read based SVs detection methods is mainly affected by some parameters. For example, one important parameter is the minimal number of supporting reads in Sniffles, SVIM, and cuteSV. If the value of the minimal number of supporting reads is too large, the detection results will achieve high precision but low recall; and if its value is too small, the opposite is true. In addition, different types of SVs are also suitable for different parameter values. This means that users will face the problem of selecting parameters. Therefore, we encode the long-read alignment information around the variant region into images according to the variant features, and then assign a large number of images containing positive and negative examples to the CNN for training the filtering model. For a new image generated by a variant, the trained model will autonomously judge whether the variant is the true positive or false positive. The image describes the arrangement of reads with variant characteristics. The deep learning based detection method has fault tolerance ability to select the features of variants. According to the characteristics of SVs and long-read sequencing data, we present an improved encoding method. The encoding method determines the range of searching long-read alignment information according to the length of SV. In this way, we can obtain enough read alignment information for variants of any length to generate images. The images generated by real variants of different lengths will have similar features, which is conducive to improve the training effect of the subsequent binary classification model. Compared with the existing deep learning based encoding method, our improved encoding method only represents those read fragments with the feature of variant in images. It is well known that INSs and DELs are the two most abundant SVs in the genome. For the fragments where INSs or DELs occur frequently, the two adjacent variants are too close to each other to fully represent the true positive features. Our encoding method dynamically adjusts the image to a uniform size according to the coverage of each variant fragment. By visualizing the long-read alignment information in IGVtools, we found that there were different coverages in different fragments of the genome. Our method uses normal alignment information to calculate the coverage to dynamically adjust the depth of read stack to encode long-read alignment information in distinct fragments with different coverages. In order to obtain the images with the same size, our method scales each image according to search region, depth of read stack, and length and width of image. To distinguish the true positives and the false positives of the SVs in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2, we propose feature extracting and image encoding methods for four variants INS, DEL, NVI, and DUP shown in Fig. 2. For the convenience of understanding our proposed encoding methods, we describe them in detail as follows. Fig. 2Process of encoding long-read alignment information, where pos, svl, ori represent the position, length, and the orientation of the variant fragment, respectively ## Encoding for two variants INSs and DELs We select \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[bpt_{left}-svl, bpt_{right}+svl]$$\end{document}[bptleft-svl,bptright+svl] and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[INS_{pos}-svl, INS_{pos}+2\times svl]$$\end{document}[INSpos-svl,INSpos+2×svl] as long-read alignment information search region for DELs and INSs respectively, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$bpt_{left}$$\end{document}bptleft and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$bpt_{right}$$\end{document}bptright denotes positions of the left and right breakpoints of DEL, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$INS_{pos}$$\end{document}INSpos is the position of INS, and svl is the length of SV. INS and DEL are characterized by the CIGAR string and split read alignment. For the features in the CIGAR string, we extract the variant fragments represented by “I” (INS) and “D” (DEL) in the CIGAR string of each read in search region, and obtain the starting and ending positions of each variant fragment. For split read alignment, if the distance between two segments from the same read increases or decreases over 50 bps before and after alignment, the genomes covered by these two segments may have DEL or INS variant. At this time, we obtain the starting and ending positions of the DEL or INS in the variant fragment. We assign values of three RGB channels to each variant fragment according to the following rules: if each variant fragment appears completely in the search region, the B channel is set to 255; if the midpoint for each variant fragment is between the two breakpoints, the G channel is set to 255; and if the length of each variant fragment is less than two times of svl, the R channel is set to 255. ## Encoding for variant INVs The lengths of some INVs will reach hundreds of thousands of bps. To accelerate encoding process for INV and reduce memory usage, we set a threshold \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α to determine the search region. If svl is less than or equal to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[bpt_{left} - svl, bpt_{right} + svl]$$\end{document}[bptleft-svl,bptright+svl] is used as the search region; if svl is greater than \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α, SV information surrounding the left and right breakpoints is searched. The left and right search regions are \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[bpt_{left} - \alpha, bpt_{left}+\alpha /2]$$\end{document}[bptleft-α,bptleft+α/2] and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[bpt_{right}+\alpha /2, bpt_{right}+\alpha]$$\end{document}[bptright+α/2,bptright+α] respectively, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$bpt_{left}$$\end{document}bptleft and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$bpt_{right}$$\end{document}bptright denote positions of the left and right breakpoints of INV, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\alpha$$\end{document}α represents the threshold length of INV. According to the characteristics of INV, the long reads in the search region are encoded by the following rules. If the read in this region is split into multiple segments, the B channel of each segment is set to 255; if the midpoint of each segment is between the two breakpoints, the G channel is set to 255; and if the orientation of each segment is inconsistent with that of other segments, the R channel is set to 255. ## Encoding for variant DUPs We select \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$[bpt_{left}-svl, bpt_{right}+svl]$$\end{document}[bptleft-svl,bptright+svl] as long-read alignment information search region for DUPs, where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$bpt_{left}$$\end{document}bptleft and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$bpt_{right}$$\end{document}bptright denote positions of the left and right breakpoints of DUP. Similar to the INV, DUP features are derived from split read alignment. If two segments of the same read overlap after the read is aligned to reference genome, the aligned segment region of reference genome may have tandem duplication. At this time, we achieve the starting and ending positions of the two segments. If each segment completely presents in the search region, the channel B is set to 255; if the midpoint of each segment is between the two breakpoints, the G channel is set to 255; if the variant length is less than two times of the svl, the R channel is set to 255. ## Eleminating incorrectly labeled samples Those structural variants detected by callers but not recorded in the ground truth sets are called false positives. However, the actual situation is that some false positives show the characteristics of true positives, which may be affected by the accuracy of aligners and the confidence of the ground truth sets. Figure 3 shows the long-read alignment information around some false positives. It illustrates that this part of false positives are labeled incorrectly. In order to prevent the model from judging true positives as false positives and filtering out them, we eliminate samples with incorrect labels by executing the PCA and k-means algorithms to improve the prediction accuracy of the filtering model. Fig. 3False positives with the same features as true positives in NA19240 dataset. A INS detected by Sniffles [5-17,587,397-1713], PBSV [5-17,587,396-1666], SVIM [5-17,587,396-1668], and cuteSV [5-17,587,397-1758]. B DEL detected by Sniffles [7-4,179,134-85], PBSV [7-4,179,136-88], SVIM [7-4,179,144-87], and cuteSV [7-4,179,146-88]. C INV detected by Sniffles [17-5,886,159-266], PBSV [17-5,886,159-266], SVIM [17-5,886,159-266], and cuteSV [17-5,886,163-272]. D DUP detected by SVIM [9-140,739,516-1424] and cuteSV [9-140,739,517-1422]. The content chr-pos-svl in parentheses represents the chromosome, position, and the length of the SV As shown in Fig. 4, in order to eliminate the incorrectly labeled samples in negative training samples, we first convert the 2-D RGB matrix parsed from the i-th image in set Imgset into an array \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ArrE_{i}$$\end{document}ArrEi by the following formula:1\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \begin{aligned} ArrE_{ij}=\sum _{$k = 1$}^{row}R_{kj}\times \omega ^{2}+G_{kj}\times \omega +B_{kj} \end{aligned} \end{aligned}$$\end{document}ArrEij=∑$k = 1$rowRkj×ω2+Gkj×ω+Bkjwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ArrE_{ij}$$\end{document}ArrEij represents the j-th element in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ArrE_{i}$$\end{document}ArrEi, row is the number of rows of the RGB matrix, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$R_{kj}$$\end{document}Rkj, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$G_{kj}$$\end{document}Gkj, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$B_{kj}$$\end{document}Bkj represent the values of three channels of the pixel point in the k-th row and j-th column in the RGB matrix respectively, $j = 1$,2,..., col, and col is the number of columns of the RGB matrix, $i = 1$,2,..., \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\|Imgset\|$$\end{document}\|Imgset\|, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\omega$$\end{document}ω is a control parameter that determines the converted value. The number of rows in ArrE represents the number of samples, and the number of columns in ArrE represents the number of features, ArrE =(\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ArrE_{1}, ArrE_{2},..., ArrE_{\|Imgset\|}$$\end{document}ArrE1,ArrE2,...,ArrE\|Imgset\|). Secondly, in order to improve the clustering effect, we use the PCA algorithm to reduce the redundant features to form a new matrix ArrEÂ´ with less but important features. Thirdly, we execute the k-means algorithm for all row vectors in ArrEÂ´ to obtain cluster \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_{l}$$\end{document}Cl whose vectors with similar features, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$$l = 1$,2,..,c$$\end{document}$l = 1$,2,..,c, where c denotes the number of clusters. Finally, we partition the Imgset into different sets S according to cluster \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_{l}$$\end{document}Cl, and remove the sets with incorrectly labeled samples. Other remaining samples in S are used to form the final training set SÂ´. Fig. 4Eliminating incorrectly labeled samples in negative training samples ## Training the filtering model We construct a relatively lightweight neural network structure shown in Fig. 1. Each image in SÂ´ is taken as the input of the constructed network. The CNN module of constructed network includes three identical structures, which each structure consists of a 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 3 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× 3 convolution kernel, ReLU activation function, and a max-pooling layer. The matrix outputted by the CNN module is flattened into a vector and fed this vector into the classification module with two fully connected layers. Behind each fully connected layer, one dropout layer [27] is added to prevent overfitting. Since detecting SVs can be regarded as a binary classification problem, we select the most commonly used activation function sigmoid to calculate the output of the fully connected neural network. We use the images in training set SÂ´ to train the model. ## Detecting SVs on long-read datasets The \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2 is used as the input of our proposed method cnnLSV. The long-read information for INS, DEL, INV, and DUP is encoded into the images. The trained model is used to determine whether the images represent true positives. If the final output value of the model is greater than 0.5, the image represents a true positive; otherwise, a false positive. Here, the true positive denotes the variant in long reads. We retain true positives and eliminate false positives of INS, DEL, INV and DUP in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2. Different from the other four variants, the variant call format (VCF) file generated by callers records TRAs at the breakpoint level. It is difficult to encode TRAs occurring between two chromosomes into images. Thus, we do not filter the TRAs in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2 by encoding alignment information and CNN, but combine the two TRAs \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{a}$$\end{document}Ta and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{b}$$\end{document}Tb in \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Cset_{2}$$\end{document}Cset2 representing the same TRA variant into one TRA. \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{a}$$\end{document}Ta and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$T_{b}$$\end{document}Tb are considered to be the same TRA by the formulas [2] and [3]:2\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} Chr_{a1}=Chr_{b1}\ and\ Chr_{a2}=Chr_{a2} \end{aligned}$$\end{document}Chra1=Chrb1andChra2=Chra23\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} \|Pos_{a1}-Pos_{b1}\|\le \gamma \ and\ \|Pos_{a2}-Pos_{b2}\|\le \gamma \end{aligned}$$\end{document}\|Posa1-Posb1\|≤γand\|Posa2-Posb2\|≤γwhere \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Chr_{i1}$$\end{document}Chri1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Chr_{i2}$$\end{document}Chri2 represent the two chromosomes where the fragment exchange occurred in the i-th TRA variant information, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Pos_{i1}$$\end{document}Posi1 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Pos_{i2}$$\end{document}Posi2 denote the breakpoint positions of the two chromosomes, i=a, b, and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\gamma$$\end{document}γ indicates the threshold of the bias of the \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Pos_{aj}$$\end{document}Posaj and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Pos_{bj}$$\end{document}Posbj and its default value is 1000, $j = 1$, 2. After combining the variant information representing the same TRA into the same cluster, we select the TRAs detected by at least \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c_{0}$$\end{document}c0 detection methods as the final output, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$c_{0}$$\end{document}c0 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_{sup}$$\end{document}Csup meet the following constraint:4\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned} \begin{aligned} C_{sup}\ge c_{0} \end{aligned} \end{aligned}$$\end{document}Csup≥c0where \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$C_{sup}$$\end{document}Csup is the number of callers that detected the TRAs. ## Algorithm Let GTset denote the ground truth set corresponding to \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Dset_{1}$$\end{document}Dset1, VCFfile represent the variant call format file with detection results, Img be the image set for training, img represent an image generated by variant, Bset be the set for storing TRA, chr, pos, svl, and svt represent the chromosome, position, length and the type of the i-th SV respectively, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Arrs_{1}$$\end{document}Arrs1 denote the set of one-dimensional arrays, \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$Arrs_{2}$$\end{document}Arrs2 represent the set of low dimensional arrays. By encoding long-read alignment information and using CNN, Algorithm 1 describes our proposed detecting four-type structural variant algorithm called cnnLSV. ## Experimental environment The experiment was carried out on the computing node X580-G30 with CPU 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Intel Xeon Gold 6230, GPU 2 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× Tesla T4, and main memory 192GB DDR4 of Sugon 7000A parallel computer cluster system at Guangxi University. The running operating system is CentOS 7.4. The proposed method was implemented by Python3.8 programming. The Pytorch was used to train and test the constructed network model. ## Experiment on simulated datasets We obtained the real variants provided by cuteSV, used the tool Visor [28] to insert these SVs to some specific positions in the reference genome, and applied the tool PBSIM [29] to generate the long-read alignment files with 30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× coverage for each type of SV. We detected the simulated datasets by executing methods Sniffles, PBSV, SVIM, and cuteSV, and used the Bcftools [30] to merge the detected results of the four methods into a set Meg. We extracted the variant information of chromosomes with no.1-16 from Meg as a training set Me\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g_{t}$$\end{document}gt for training model, and extracted the variants of the remaining chromosomes from Meg as a set Me\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g_{d}$$\end{document}gd for evaluating the performance of the detection methods. Let \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{30}$$\end{document}M30 denote the model trained on the datasets with 30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× coverage. By executing our method cnnLSV, we obtained \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{30}$$\end{document}M30 trained with Me\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g_{t}$$\end{document}gt and filtered the false positives of INS, DEL, INV and DUP in Me\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$g_{d}$$\end{document}gd to achieve the final detection results. To verify the impact of the coverage on detection performance and generality of the model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{30}$$\end{document}M30, we also generated 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× and 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× datasets \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{20}$$\end{document}D20 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{10}$$\end{document}D10, and used \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{30}$$\end{document}M30 to filter the detection results of executing methods Sniffles, PBSV, SVIM, and cuteSV on the two datasets \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{20}$$\end{document}D20 and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$D_{10}$$\end{document}D10. We used the F1-score to balance precision and recall as the final detection performance metric. Precision Pre, recall Rec, and F1-score F1 are computed by the following formulas:5\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} Pre=\frac{TP}{TP+FP} \end{aligned}$$\end{document}Pre=TPTP+FP6\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} Rec=\frac{TP}{TP+FN} \end{aligned}$$\end{document}Rec=TPTP+FN7\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\begin{aligned}{} & {} F1=\frac{2\times Pre\times Rec}{Pre+Rec} \end{aligned}$$\end{document}F1=2×Pre×RecPre+Recwhere TP, FP, and FN represent the number of true positives, false positives, and false negatives, respectively. The detection results on the simulated datasets are shown in Fig. 5.Fig. 5Detection results on simulated datasets As shown in Fig. 5, when the five methods were executed on the 30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× and 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× datasets, our method cnnLSV achieved the highest F1-scores for INS/DEL, INV, and DUP, and the second high F1-score for TRA which was a bit lower than that of cuteSV. When executing the five methods on the 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× datasets, our method cnnLSV achieved higher F1-scores than other methods for detecting DEL, INV, and DUP, and the second high F1-scores for INS and TRA which were a little lower than Sniffles and cuteSV, respectively. The results indicate that cnnLSV achieves better detection performance overall. This is because cnnLSV filters all the detection results of the other four methods Sniffles, PBSV, SVIM, and cuteSV to reduce the false positives to achieve high precision and recall. In addition, it is noteworthy that our method cnnLSV used only one model \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$M_{30}$$\end{document}M30 to detect variants on 30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, and 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× datasets and performed better on the whole. This illustrates that the detection model of cnnLSV has high adaptability because our proposed encoding method automatically calculates the depth of SVs and resizes the generated image to the uniform scale according to the depth in order to avoid the impact of the coverage as much as possible. The detailed detection results on simulated datasets for all five methods are provided in the supplementary materials (See: Table S1, S2, and S3 in Additional file 1). ## Datasets In the experiment, we trained the filtering model on six PacBio datasets HG00512, HG00513, HG00731, HG00732, NA19238, and NA19239, and loaded the model to detect SVs on the five PacBio datasets HG00514, HG00733, NA19240 [31], HG002 CLR, and HG002 CCS [32]. Among them, the HG002 datasets are also used in Sniffles [6], PBSV [7] and cuteSV [9] to evaluate the performance of the detection methods. Table 1 shows the detailed information of all datasets with coverages and the average length of reads. Table 1Information of real datasetsDatasets for training modelDatasetHG00512HG00513HG00731HG00732NA19238NA19239Coverage19 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×18 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×22 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×23 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×18 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×16 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Average length10,74611,41113,05312,29868016530Datasets for detecting SVsDatasetHG00514HG00733NA19240HG002 CLRHG002 CCSCoverage41 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×44 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×37 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×69 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×28 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Average length11,80012,2956503832313,478 For the experiment on the datasets HG00514, HG00733, and NA19240, the minimal number of supporting reads and minimal SV length of Sniffles, SVIM, and cuteSV are set to 5 and 50 respectively, and these two parameters of PBSV follow its default settings. For the experiment on the datasets HG002 CLR 69 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, 40 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, 30 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, 20 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, and HG002 CCS 28 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, the minimal number of supporting reads of Sniffles and cuteSV is set to $\frac{10}{5}$/$\frac{4}{4}$/3 and $\frac{3}{2}$ respectively, and that of SVIM is set to $\frac{5}{5}$/$\frac{5}{2}$/2 and $\frac{5}{2}$ respectively. The minimal SV length of Sniffles, SVIM, and cuteSV is 50. The minimal number of supporting reads and the minimal SV length of PBSV also follow the default settings. We used the tool Truvari [33] to evaluate our proposed method cnnLSV with existing four long-read based SV detection methods Sniffles, PBSV, SVIM, and cuteSV by Pre, Rec and F1. ## Training the model on real datasets For each dataset, we first used Bcftools [30] to merge the initial detection results of executing Sniffles, PBSV, SVIM, and cuteSV into one callset and encoded INSs, DELs, INVs, and DUPS in the callset into images. We applied the PCA and k-means algorithms to eliminate the incorrectly labeled negative samples and selected $\frac{5000}{5000}$/$\frac{10000}{10000}$ as the thresholds of INS/DEL/INV/DUP to balance the number of positive samples and the number of negative samples to improve effect of the training model. To verify the effect of executing algorithms PCA and k-means, we also balanced the positive samples and negative samples of images not filtered by PCA and k-means algorithms to train the constructed model. Figure 6 shows the AUC curves during the training model stage on unfiltered training sets and filtered training sets. It can be seen from Fig. 6 that the training result on the sets filtered by PCA and k-means algorithms has higher AUC values. This illustrates that the combing use of algorithms PCA and k-means can effectively reduce the number of incorrectly labeled samples to improve the effect of training model. Fig. 6AUC curves during the training model stage. The blue and red curves represent training sets filtered and unfiltered by PCA and k-means algorithms, respectively ## Detection results on real datasets We first conducted the experiment on the first group of datasets including HG002 CLR and HG002 CCS. We used Samtools [30] to perform down-sampling on HG002 CLR and HG002 CCS datasets with different coverages to evaluate the performance of five detection methods. The experimental results are shown in Table 2.Table 2Performance of detection methods on datasets HG002 CLR and HG002 CCSCoverageMetric (%)SnifflesPBSVSVIMcuteSVcnnLSVHG002 CLR69 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre94.36494.82594.98195.23395.976Rec89.26587.57491.89991.54793.403F191.74491.05593.41593.35494.67240 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre93.12794.78295.34395.25395.512Rec90.17786.42388.35290.96691.754F191.62890.41091.71593.06093.59530 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre91.71095.00196.27794.94195.143Rec88.68484.01682.21188.52889.410F190.17189.17288.69091.62392.18720 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre92.84595.42785.53096.19392.835Rec77.31676.92190.37477.47188.248F184.37285.18187.88685.82390.48310 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre90.32296.88891.83796.36592.161Rec57.62949.59073.68557.38073.654F170.36365.60381.76671.93081.875HG002 CCS28 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre94.05993.62293.31294.96494.858Rec93.77784.17292.77093.39393.673F193.91888.64693.04094.17294.26110 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×Pre94.09295.46892.19294.93793.748Rec88.37375.53290.34387.01490.872F191.14384.33891.25890.80392.288The values in bold represent the best results We can see from Table 2 that our method cnnLSV performed best in all coverages on both HG002 CLR and HG002 CCS datasets among five methods. For the dataset HG002 CLR, the detection performance of each method was decreased as the coverage was decreased. For the HG002 CLR dataset with higher coverages 69 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× and 40 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}×, cnnLSV achieved the highest precision, recall, and F1-score among five methods. Even for the HG002 CLR dataset with lower coverages, our method cnnLSV also achieved the highest F1-score. This is because cnnLSV can dynamically determine the depth of read stack and regions of searching long-read alignment information around each SV. It is noteworthy that all five methods performed better on the HG002 CCS 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× dataset than on the HG002 CLR 10 \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\times$$\end{document}× dataset. This indicates that the sequencing error rate has a significant impact on long-read SV detection. We also calculated F1-scores of all the methods in detecting SVs with different lengths on HG002 CLR. The statistical results are shown in Fig. 7. It can be seen intuitively from the Fig. 7 that cnnLSV got higher F1-scores than the other four methods in most cases, especially when the lengths of SVs exceed 1000. The detailed detection results on HG002 CLR and HG002 CCS datasets for all five methods are provided in the supplementary materials (See: Table S4 and S5 in Additional file 1).Fig. 7F1-scores of detection methods in detecting different lengths of SVs In the following, we conducted the experiment on the second group datasets including HG00514, HG00733, and NA19240. We ran Sniffles, PBSV, SVIM, and cuteSV on the datasets in the second group, and merged their outputs as the input of our method cnnLSV. We compare the detection results of five methods with the ground truth set in Table 3.Table 3Performance of detection methods on datasets HG00514, HG00733, and NA19240TypeMethodHG00514HG00733NA19240Pre (%)Rec (%)F1 (%)Pre (%)Rec (%)F1 (%)Pre (%)Rec (%)F1 (%)INS/DUPSniffles43.27860.93450.61034.90761.73144.59642.21960.94949.883PBSV50.58853.65252.07550.25453.96052.04151.15252.46951.802SVIM44.55961.15851.55542.57462.09750.51545.76261.18052.359cuteSV50.98066.20257.60251.61068.67458.93251.96363.71057.240cnnLSV57.53959.75858.62859.11459.10059.10760.20160.56960.384DELSniffles55.62864.30859.65454.32064.97759.17357.63765.19261.182PBSV63.87362.50763.18263.16163.06463.11265.00661.98363.459SVIM53.08165.71458.72651.09766.43957.76655.69966.37960.572cuteSV54.12267.87260.22251.35369.14958.93755.28068.01160.988cnnLSV63.46064.84364.14462.81365.41664.08865.29964.79765.047INVSniffles9.0917.0097.9166.2787.2736.7399.4206.6677.808PBSV32.6929.34614.53626.9237.27311.45232.6539.33314.517SVIM10.00010.28010.1386.0209.5457.3849.54810.66710.076cuteSV27.9076.54210.59925.0006.36410.14530.9526.66710.970cnnLSV36.1709.81315.43827.4517.27311.49931.4819.77814.921AllSniffles47.65362.14153.94140.85662.83949.51747.84462.64754.254PBSV55.81457.45456.62355.23957.76856.47556.92456.74656.835SVIM52.14366.58458.48645.42163.71353.03449.53663.35655.600cuteSV47.65362.93154.23651.47668.47758.77253.30665.41958.745cnnLSV59.88961.78160.82160.56261.61361.08362.37162.32962.350The values in bold represent the best results We can see from Table 3 that for detecting INS, DUP, DEL, and INV, our method cnnLSV achieved higher F1-scores than the other four methods on all three datasets overall. This means that cnnLSV has a better comprehensive detection ability for various types of SV. And the existing detection methods are often good at detecting specific types of structural variants. For example, cuteSV had a good performance in detecting INS/DUP, but PBSV is good at detecting DEL and INV. In addition, we can also find that cnnLSV can better balance precision and recall to obtain a higher F1-scores, especially when detecting INS/DUP and DEL. Due to the lack of TRA in the ground truth set, we did not evaluate the detection results of TRA. The detailed detection results on HG00514, HG00733, and NA19240 datasets for all five methods are provided in the supplementary materials (See: Table S6, S7, and S8 in Additional file 1). The effectiveness of cnnLSV in each stage can also be seen in the supplementary materials (See: Table S9 in Additional file 1). ## Case study The dataset HG002 is real sequence alignment data from an Ashkenazim son. The cnnLSV detected SVs in HG002 dataset and outputted detailed information for each variant. Table 4 lists some key information about several SVs, where “CHROM”, “POS”, “ID”, “ALT”, “REF”, “QUAL”, and “INFO” represent the chromosome, position, identity, base, sequence, confidence score, and detailed information of each variant respectively, and “FILTER” indicates whether the variant is filtered out. Table 4Case study of cnnLSV on the HG002 datasetChromPOSIDALTRefQualFilterInfo1934137INS.177CCGCGGAGCGGAGGGC60PASSSVTYPE=INS;GCGGAGCGGAGGGGAEND=934137;GGGCGCGGAGCGGAGSVLEN=59;GGGGAGGGCGCCGGASUPPORT=5;187819365INS.590523AAGAACGATAGAAG60PASSSVTYPE=INS;CGCTGGATGTTGAEND=7819365;GGGAGGGTGGAGCSVLEN=53;ACACTGGCAGAAGSUPPORT=2;556131176DEL.3813GGTTGACAGGAAGGCAG60PASSSVTYPE=DEL;GGAAGAGGAGACAGGAEND=56131239;AGGCAGGGAAGAGGAGSVLEN=64;ACAAGGAGGCAGGGAASUPPORT=8;95441971DEL.4918AATACGTGTGTATATAC60PASSSVTYPE=DEL;ACACGTATATACATGTEND=5442033;GTGTATATACATGTGTSVLEN=63;GTATATACACACGTASUPPORT=3; We can see from Table 4 that cnnLSV can output the chromosome, start and end points, type, sequence, and length of each SV. In addition, cnnLSV also provides the confidence score and the number of supporting reads of each SV for subsequent genomic analysis. ## Discussion According to the characteristics of the long reads and multiple types of SV, we design an encoding strategy for four kinds of structural variants and propose a deep learning-based multi-type SV detection method using long-read alignment information. In the image encoding stage, our method cnnLSV can automatically adjust the images from different variants to a uniform size according to the length of each variant and the coverage of the dataset for training the filtering model. And in the training model phase, cnnLSV converts the images in training set into one-dimensional arrays, and executes the principal component analysis and k-means clustering to eliminate the incorrectly labeled images to improve the filtering performance of the model. The experimental results on simulated and real datasets show that the overall performance of our method is better than that of other existing methods on detecting INS, DEL, INV, and DUP. At present, cnnLSV just combines the TRA from detection results of existing detection methods, and does not support filtering for TRA temporarily because the read stacking method is not suitable for TRA. Since cnnLSV encodes all variant fragments in long reads, it will take more time and memory, especially when detecting large SVs on high coverage datasets. In the future, we will further investigate the detection of TRA, optimization of specific implementation of the encoding method to reduce memory usage, and parallel processing of cnnLSV to accelerate the detection. ## Conclusion In this paper, our proposed method cnnLSV detects SV by encoding long-read alignment information and CNN, and uses PCA and k-means algorithms to eliminate mislabeled samples to improve the performance of model in the training model phase. The experimental results on simulated and real datasets show that the detection performance of cnnLSV is overall better than existing detection algorithms. We will continue to investigate the detection of TRAs and the optimize the performance of cnnLSV in terms of detection speed and memory usage in the future. ## Supplementary Information Additional file 1. Supplementary information. ## References 1. Abel HJ, Duncavage EJ. 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--- title: Identification of Putative Molecules for Adiponectin and Adiponectin Receptor and Their Roles in Learning and Memory in Lymnaea stagnalis authors: - Kanta Fujimoto - Yuki Totani - Junko Nakai - Nozomi Chikamoto - Kengo Namiki - Dai Hatakeyama - Etsuro Ito journal: Biology year: 2023 pmcid: PMC10045044 doi: 10.3390/biology12030375 license: CC BY 4.0 --- # Identification of Putative Molecules for Adiponectin and Adiponectin Receptor and Their Roles in Learning and Memory in Lymnaea stagnalis ## Abstract ### Simple Summary Insulin and insulin-like peptides are involved in improving learning and memory in both vertebrates and invertebrates. Adiponectin has blood glucose-lowering and insulin sensitivity-increasing effects in mammals. These two facts led us to hypothesize that adiponectin and its receptors play an important role in learning and memory. We evaluated this hypothesis using the pond snail Lymnaea stagnalis, which has long been used for studies of learning and memory. First, the genes coding for putative molecules of adiponectin and its receptor in Lymnaea were identified, and then their localization in the central nervous system and changes in their expression levels associated with the nutritional conditions were examined. Next, an operant conditioning protocol of escape behavior was applied to the snails, and changes in the expression levels of adiponectin and its receptor were examined. Adiponectin was upregulated by food deprivation, whereas the expression of its receptor was upregulated after operant conditioning was established. These findings suggested the involvement of the adiponectin-signaling cascade in learning and memory in Lymnaea via changes in the concentrations of glucose and the activation of insulin. ### Abstract Adiponectin enhances insulin sensitivity, which improves cognition in mammals. How adiponectin affects the mechanism’s underlying cognition, however, remains unknown. We hypothesized that experiments using the pond snail Lymnaea stagnalis, which has long been used in learning and memory studies and in which the function of insulin-like peptides affect learning and memory, could clarify the basic mechanisms by which adiponectin affects cognition. We first identified putative molecules of adiponectin and its receptor in Lymnaea. We then examined their distribution in the central nervous system and changes in their expression levels when hemolymph glucose concentrations were intentionally decreased by food deprivation. We also applied an operant conditioning protocol of escape behavior to Lymnaea and examined how the expression levels of adiponectin and its receptor changed after the conditioned behavior was established. The results demonstrate that adiponectin and adiponectin’s receptor expression levels were increased in association with a reduced concentration of hemolymph glucose and that expression levels of both adiponectin and insulin-like peptide receptors were increased after the conditioning behavior was established. Thus, the involvement of the adiponectin-signaling cascade in learning and memory in Lymnaea was suggested to occur via changes in the glucose concentrations and the activation of insulin. ## 1. Introduction The pond snail Lymnaea stagnalis is an important model system for studying the causal neuronal mechanisms of associative learning and the subsequent formation of long-term memory. Lymnaea can be both classically and operantly conditioned for a number of different behaviors, and researchers have primarily focused on feeding, withdrawal, and aerial respiratory behaviors [1,2,3,4,5]. Lymnaea possesses relatively simple nervous systems, and the neuronal circuits mediating many of the behaviors that exhibit learning and memory have been well elucidated. Many of the identified neurons in the circuits mediating the behaviors are large, and they can consistently be recorded from individuals that have been subjected to either learning or control procedures [6]. Insulin-like peptides in the brain are strongly involved in the learning and memory mechanisms of Lymnaea [7,8,9,10]. For example, in long-term memory formation following a conditioned taste aversion (CTA) protocol [9], molluscan insulin-related peptides (MIPs) are up-regulated at the mRNA level in the Lymnaea central nervous system (CNS) [11], and MIPs enhance the neural transmission efficacy at a synapse in the CTA neural circuit [12]. A common role of insulin and insulin-like peptides is to lower blood glucose levels, and MIPs lower hemolymph glucose levels in Lymnaea [13]. CTA learning performance in Lymnaea is best during mild starvation (i.e., food deprivation for 1 day), but learning and memory deteriorate during severe starvation (i.e., food deprivation for 5 days) [13]. In addition, injections of mammalian insulin into Lymnaea improve learning and memory performance in 5-day food-deprived snails [13]. The relationships between the multiple functions of MIPs, nutritional status, and learning ability have been investigated [13]. Adiponectin, a ca. 30-kDa adipokine secreted by adipocytes, has blood glucose-lowering and insulin resistance-improving effects in mammals [14,15]. Thus, adiponectin is often discussed in connection with type 2 diabetes mellitus and metabolic syndrome [16,17,18]. Adiponectin belongs to the C1q family, with a collagen domain on the N-terminal and a globular C1q domain on the C-terminal [19]. In mammals, AdipoR1, AdipoR2, and T-cadherin are reported as adiponectin receptors [20,21]. AdipoR1 and AdipoR2, in particular, whose structures are 7-transmembrane receptors, are thought to be involved in the insulin resistance-improving effects of adiponectin [16]. Unlike the widely known 7-transmembrane G protein-coupled receptor (GPCR), however, the N-terminal is intracellular and the C-terminal is extracellular [22]. Adiponectin receptor-like proteins have also been studied in molluscs, and such proteins have been identified in the Japanese oyster Crassostrea gigas and are implicated in immune responses [23]. The function between the C1q protein and adiponectin is different in mammals: they complement system and glucose-level regulation, respectively. In molluscs, several C1q-related and C1q-domain-containing proteins were isolated, and these proteins are suggested to be involved in molluscan immune systems [24,25,26]. However, in mammals, adiponectin can bind with the C1q complex and activate the classical complement pathway [27]. Additionally, adiponectin is structurally homologous to C1q and can stimulate the tyrosine kinase-dependent engulfment of apoptotic cells through a shared pathway [28]. Taken together, we speculate that the Lymnaea C1qC, which will be identified in the present study, harbors a similar function to adiponectin. Adiponectin also has a role in the brain and its function has been studied in relation to “dementia”. For example, in rat models of dementia, adiponectin improves memory and neuroplasticity [29,30]. Mice with AdipoR1 knockdown or chronic adiponectin deficiency exhibit impaired insulin signaling and impaired learning and memory [31,32]. Caloric restriction leads to increased serum insulin and adiponectin concentrations, and improved insulin sensitivity and hippocampus-dependent spatial learning ability in mice [33]. Although the reports mentioned above suggest a relationship between adiponectin function and learning and memory, the detailed mechanisms of this relationship remain unclear. Therefore, we investigated the further possible involvement of adiponectin in learning and memory using Lymnaea stagnalis, which has long been used for neuroscience studies. First, we identified the genes coding putative molecules corresponding to adiponectin (LymAdipo) and Lymnaea adiponectin receptor (LymAdipoR) in Lymnaea, and then examined their localization in the CNS and changes in their expression levels associated with the nutritional status and the formation of operant conditioning of escape behavior. Benatti et al. recently revisited the operant conditioning of escape behavior that we had previously studied [34], and it was again attracting attention [35]. We thus wanted to broaden the scope of our research into the molecular mechanisms of operant conditioning as well as classical conditioning. ## 2.1. Snails Lymnaea stagnalis with a 20 to 25 mm shell length obtained from our snail-rearing facility (original stocks from Vrije Universiteit Amsterdam) were used. They were fed turnip leaves (Brassica rapa var. perviridis, known as Komatsuna in Japanese) ad libitum and kept in dechlorinated tap water under a 12 h light:12 h dark cycle at 21.0–22.5 °C. ## 2.2. Definition of Nutritional Status The nutritional status of the snails was defined as follows [36]: [1] “Day 0” = snails food-deprived; [2] “Day −1” = snails fed normally; [3] “Day 1” = snails food-deprived for 1 day; [4] “Day 5” = snails food-deprived for 5 days; and [5] “High Glucose” (HG) = snails immersed in 20 mM sucrose instead of turnip leaves for 2 days. ## 2.3. Identification of LymAdipo and LymAdipoR Putative molecule sequences for adiponectin and its receptor in Lymnaea (LymAdipo and LymAdipoR) were identified by a BLAST search (https://blast.ncbi.nlm.nih.gov/Blast.cgi (accessed on 26 February 2023)) using the transcriptome shotgun assembly (TSA) database for Lymnaea [37] on the basis of the adiponectin receptors in the Japanese oyster Crassostrea gigas (XM_011441672.3). The domain sequences of the identified amino acid sequences were predicted with the database Pfam in InterPro (http://www.ebi.ac.uk/interpro/ (accessed on 26 February 2023)) and another database, the Simple Modular Architecture Research Tool (SMART) (http://smart.embl-heidelberg.de (accessed on 26 February 2023)). Furthermore, the transmembrane regions were predicted with the database Phobius in InterPro. Multiple alignments were analyzed using ClustalW (https://www.genome.jp/tools-bin/clustalw (accessed on 26 February 2023)). The maximum likelihood trees of adiponectin-like and adiponectin receptor-like proteins identified from different organisms were generated using MEGA 11 software (https://www.megasoftware.net/ (accessed on 26 February 2023)). The amino acid sequences used for the phylogenic tree are listed in Table 1. ## 2.4. In Situ Hybridization In situ hybridization was performed according to a previous study [38] with modifications. For hybridization, frozen sections were then prepared as follows: the CNS was isolated from each anesthetized Lymnaea in chilled saline (NaCl 50 mM, KCl 1.6 mM, MgCl2 2 mM, CaCl2 3.5 mM, HEPES 10 mM, pH 7.9), fixed in $4\%$ paraformaldehyde in phosphate-buffered saline (PBS) for 1 h at room temperature, and then washed with PBS containing $30\%$ sucrose. After embedding the fixed CNS in an OCT compound (Sakura Finetek, Tokyo, Japan), serial 10 µm-thick frozen sections were cut horizontally on a cryostat (CM3000; Leica, Nussloch, Germany) and placed on MAS-coated glass slides (Matsunami glass, Osaka, Japan). For probe synthesis, regions of probes for LymAdipo and LymAdipoR were amplified from Lymnaea cDNA with Ex Taq (Takara Bio, Shiga, Japan), and the primers are listed in Table 2. The polymerase chain reaction (PCR) products were cloned into pTAC-2 plasmids. The inserted regions were amplified with KOD FX (Toyobo, Osaka, Japan) and forward and reverse M13 primers, and then purified using NucleoSpin Gel and PCR Clean-up (Takara). The sense and antisense probes were synthesized at 37 °C for 2 h with MAXIscrip SP6/T7 Transcription Kit (Invitrogen-Thermo Fisher Scientific, Waltham, MA, USA) and RNA labeling mix (DIG-UTP; Roche, Basel, Switzerland). Details of the in situ hybridization experiments were the same as those in Hatakeyama’s study [38]. The slides with CNS sections were overlaid with Immu-Mount (Shandon, Fisher Scientific, Singapore), and cover-slipped for light microscopic examination (CKX53; Olympus, Tokyo, Japan). ## 2.5. Real-Time PCR The real-time PCR protocol was performed according to the previous study [36] with modification. The snail’s CNS was dissected and stored at −80 °C. In the experiments, to identify the ganglion expressing the target mRNAs, the samples were separated by ganglion type, and three individual ganglia were collected together as a single sample, whereas the whole CNS was collected in the other experiments. Total RNA was extracted using ISOGEN II (311-07361; Nippon Gene, Tokyo, Japan) according to the manufacturer’s instructions. cDNA was synthesized by the ReverTra Ace qPCR RT Master Mix with gDNA Remover (Toyobo). THUNDERBIRD Next SYBR qPCR Mix (Toyobo) was used to perform real-time PCR (StepOnePlus Real-Time PCR System; Applied Biosystems, Waltham, MA, USA). The relative mRNA levels were quantified using the comparative Ct method. The Ct values of the target genes were normalized by dividing by the mean of the Ct values of 18S ribosomal RNA and β-tubulin. The mean of 18S ribosomal RNA and β-tubulin exhibited stable values under the measured conditions. The primer sequences are shown in Table 2. Efficiency values for the real-time PCR primers ranged from 90 to $110\%$. The PCR conditions were as follows: 1 cycle at 95 °C for 30 s, followed by 40 cycles of denaturation at 95 °C for 5 s; and annealing at 60 °C for 10 s. Melting curve analysis was performed from 60 to 95 °C with a heating rate of 0.3 °C/s. ## 2.6. Measurement of the Hemolymph Glucose Concentration Moisture around the Lymnaea abdomen was thoroughly wiped off with a paper towel, and the tip of a pipette was used to stimulate the mantle. Upon stimulation, 150 µL of hemolymph was collected from the snail’s renal hole. The glucose concentration in the collected hemolymph was measured using a Glucose Assay Kit-WST (Dojindo, Kumamoto, Japan). The working solution for 10 experimental wells was prepared from Dye Mixture Stock Solution:Assay Buffer:Enzyme Stock Solution, which were contained in the kit, at a ratio of 50 µL:450 µL:9 µL. In each well, 50 µL of hemolymph and 50 µL of the working solution were added. After incubation at 37 °C for 30 min, absorbance at 450 nm was measured using a microplate reader (Corona Electronic, Ibaraki, Japan). For calibration, a serial dilution series of 0.5, 0.25, 0.125, 0.0625, 0.0313, 0.0157, 0.00785, and 0 mmol/L of glucose standard was used. The HG snails were compelled to swim in the rearing water (i.e., tap water) for 30 min prior to the collection of hemolymphs to exclude glucose solution on the body surface. ## 2.7. Operant Conditioning of Escape Behavior The operant conditioning protocol for the escape behavior experiments was constructed based on previous studies [34,35]. A tray was lined with paper towels soaked with distilled water (DW, neutral reinforcement) or 100 mM KCl (negative reinforcement), and 35 mm dish lids were placed on the tray. These lids were filled with DW at a depth of 3 mm, and a Lymnaea was placed in the center of each lid. The experiments comprised three steps: the first step was an 80 min pre-test. Both the negative and neutral reinforcement groups were placed on paper towels soaked with DW. The second step was a 60 min training period. The negative reinforcement group was placed on paper towels soaked with 100 mM KCl, and the neutral reinforcement group was placed on paper towels soaked with DW. The third step was a 60 min post-test. Both groups were placed on paper towels soaked with DW. In all three tests, the number of escapes from the lid was recorded every 20 min. The timing of the escape was defined as the moment when the Lymnaea leaned out of the lid and put its head on the paper towel. When the escape was confirmed, the snail was relocated to the center of the lid. Besides recording the number of escapes per 20 min, the latency to the first escape was recorded in the pre-test and post-test. For individuals that did not escape within 60 min, the first escape was considered to have a 60 min latency. The CNS was dissected from Lymnaea 60 min after the post-test and subjected to real-time PCR experiments. ## 2.8. Statistics The data are expressed as mean ± standard error of the mean (SEM). N indicates the biological replications unless otherwise noted. Significant differences ($p \leq 0.05$) between two groups were determined by Student’s t-test or Welch’s t-test. Comparisons of three or more groups were made by one-way ANOVA. Tukey‘s test was used for multiple comparisons when significant differences were observed and the homogeneity of variances could be assumed. This statistical calculation was applied to the data of real-time PCR for LymAdipoR for the ganglia and the data of mRNA expression level changes of Adipo, AdipoR, and MIPR under different nutritional conditions. Games–Howell‘s test was used when significant differences were observed but the homogeneity of variances could not be assumed. This was applied to the data of real-time PCR for LymAdipo for the ganglia and the data of mRNA expression level changes of the MIP II data under different nutritional conditions. Comparisons in changes in escape behavior were made by two-way repeated measures ANOVA. In this case, multiple comparisons were performed using the Bonferroni test. SPSS Statistics 28 was used for the statistics. ## 3.1. Identification of Putative Molecules of Adiponectin and Its Receptor in Lymnaea A putative adiponectin in Lymnaea, LymAdipo, was identified from the homolog of the freshwater snail Biomphalaria glabrata C1qC (XM_013227908.1). The Blastn (Standard Nucleotide BLAST) search resulted in hits for Lymnaea stagnalis mRNA TSA (contig: LymstCNS_TSA_2863, mRNA sequence FX182981.1). Examination of this mRNA sequence revealed that it contained the full length of its open-reading frame (ORF). The sequence was translated for the ORF and aligned with adiponectin-like proteins from other organisms (Figure 1). The identified LymAdipo sequence has a collagen-like repeat sequence consisting of Gly-X-Y repeats as in human and mouse adiponectin, and a C1q domain, confirming that it belongs to the C1q family based on the Pfam database. A putative adiponectin receptor in Lymnaea, LymAdipoR, was identified based on the Japanese oyster Crassostrea gigas adiponectin receptor (XM_011441672.3). The Blastn search resulted in hits for Lymnaea stagnalis mRNA TSA (contig: LymstCNS_TSA_1103, mRNA sequence FX181221.1). The sequence of this mRNA was examined and found to contain the full length of the ORF. Figure 2 shows the sequence translated from the ORF and the alignment with the adiponectin receptor-like proteins of other organisms. The predictions for the domain sequence and transmembrane region suggest that LymAdipoR has transmembrane regions and extracellular regions at the N- and C-terminals that are highly homologous with those of vertebrates. LymAdipoR comprises 404 amino acids and is well conserved between Lymnaea and mammals. LymAdipoR and human AdipoR1 or AdipoR2 displayed $47\%$ or $50\%$ amino acid similarity, respectively, and in particular, high amino acid similarity was observed in the transmembrane region. A molecular phylogenetic tree of adiponectin-like proteins and their receptor-like proteins deduced from various animals was generated using the maximum likelihood method [39] (Figure 3 and Figure 4). The adiponectin-like sequence identified in the present study for Lymnaea (i.e., LymAdipo) is most similar to the C1qC of the freshwater snail Biomphalaria glabrata, and it was clustered into the C1q family of Cephalopoda and Gastropoda. This cluster was designated “molluscan adiponectin group 1”. C1q family molecules of bivalves such as the Japanese oyster Crassostrea gigas and the scallop Pecten maximus formed a separate cluster, which was designated “molluscan adiponectin group 2”. In contrast to the strict clustering of C1q family members in vertebrates (for example, the C1qC cluster is different from the adiponectin cluster), various types of C1q family molecules are intermingled within a single cluster in molluscs, suggesting that the subdivision of C1q family molecules is underdeveloped in invertebrates. From these analyses, LymAdipo identified in the present study is considered to be the “primitive” adiponectin of molluscs. LymAdipoR was first clustered with the adiponectin receptor-like protein of the freshwater snail Biomphalaria glabrata (Figure 4). It then formed a large cluster with adiponectin receptors of gastropods, cephalopods, and bivalves, and finally, it was clustered into the invertebrate adiponectin receptor branch. Due to the highly conserved transmembrane and N- and C-terminal extracellular regions, the sequence identified in the present study was used as LymAdipoR in the following experiments. ## 3.2. Localization of LymAdipo and LymAdipoR in the Lymnaea CNS To identify the CNS localization of LymAdipo and LymAdipoR, we performed in situ hybridization using frozen sections of the whole CNS and real-time PCR quantitation for mRNA extracted from each ganglion (Figure 5 and Figure 6). In situ hybridization with the antisense probe showed the presence of LymAdipo signals mainly in the cerebral ganglia, parietal ganglia, and visceral ganglion (Figure 5b), whereas these signals were not observed with the sense probe (Figure 5c). The signals were recognized in the cerebral giant cells, which are regulatory neurons and play important roles in various behaviors [40]. The hybridization signals for the LymAdipoR antisense probe were observed mainly in the pedal ganglia, parietal ganglia, and visceral ganglion (Figure 5d). As with LymAdipo, no signals were observed with the sense probe for LymAdipoR (Figure 5e). The results of real-time PCR quantification for mRNA extracted from each ganglion are shown in Figure 6. The real-time PCR results show the expression of LymAdipo in all the ganglia. In particular, the expression level of LymAdipo in the cerebral ganglia was significantly higher than that in the other ganglia (F[5,24] = 19.001, $p \leq 0.001$, cerebral ganglia vs. plural ganglia: $$p \leq 0.018$$; cerebral ganglia vs. parietal ganglia, $$p \leq 0.020$$; cerebral ganglia vs. visceral ganglion: $$p \leq 0.032$$, $$n = 5$$ each). LymAdipoR was also expressed in all the ganglia. The highest expression was found in the pedal ganglia, followed by the cerebral ganglia (F[5,24] = 5.902, $$p \leq 0.004$$, pedal ganglia vs. buccal ganglia: $$p \leq 0.020$$; pedal ganglia vs. plural ganglia: $p \leq 0.001$, pedal ganglia vs. parietal ganglia: $$p \leq 0.013$$: cerebral ganglia vs. plural ganglia: $$p \leq 0.035$$, $$n = 5$$ each). ## 3.3. Glucose Concentrations in the Hemolymph and Changes in the LymAdopo and LymAdipoR mRNA Expression Levels under Different Nutritional Conditions The hemolymph glucose concentrations under the three food-deprivation conditions (Day −1, Day 1, and Day 5) and HG state were quantified with a glucose concentration kit. The glucose concentrations were (mean ± SEM, mmol/L): 0.104 ± 0.017 in HG snails, 0.043 ± 0.005 in Day −1 snails, 0.035 ± 0.006 in Day 1 snails, 0.022 ± 0.003 in Day 5 snails ($$n = 13$$ each). The glucose concentration decreased as food deprivation progressed ($$p \leq 0.0145$$ between Day −1 and Day 5). When Lymnaea were reared in a 20-mM sucrose solution for 2 days (referred to as HG), the glucose concentrations in the hemolymph of the HG cohort were significantly higher than those in the hemolymph of the Day −1 snails ($$p \leq 0.0047$$, $$n = 13$$ each), demonstrating that the HG snails were hyperglycemic individuals. Due to the fact that the glucose concentration in the hemolymph changes is associated with changes in the nutritional conditions, we expected that the mRNA expression level of LymAdipo may also change. We thus measured the expression levels of LymAdipo and LymAdipoR in the whole CNS by real-time PCR (Figure 7). The results show that the expression levels of both LymAdipo and LymAdipoR were significantly higher than those of HG as food-deprivation progressed (LymAdipo. F[3,36] = 4.308, $$p \leq 0.011$$, HG vs. Day 1: $$p \leq 0.029$$; HG vs. Day 5: $$p \leq 0.018.$$ LymAdipoR. F[3,36] = 5.163, $$p \leq 0.005$$, HG and Day 5: $$p \leq 0.021$$; Day −1 vs. Day 5: $$p \leq 0.008$$, $$n = 10$$ each). The mRNA levels of molluscan insulin-related peptide II (MIP II) were also examined (Figure 7). Contrary to expectations, the expression of MIP II mRNA was significantly lower in the hyperglycemic HG snails than in the normally fed Day −1 snails (F[3,36] = 7.721, $$p \leq 0.010$$, HG and Day −1: $$p \leq 0.023$$, $$n = 10$$ each), and it decreased with the initiation of food deprivation, becoming significantly lower in Day 5 snails than in Day −1 snails (Day −1 and Day 5: $$p \leq 0.032$$). MIP II expression was as low in Day 5 snails as in HG snails in a hyperglycemic condition. The expression levels of the only receptor for MIPs, the MIP receptor (MIPR), were examined, and no significant differences among the four nutritional states were detected (F[3,36] = 1.861, $$p \leq 0.154$$, $$n = 10$$ each). ## 3.4. Establishment of Escape Behavior by Operant Conditioning and Change in the Expression Levels of LymAdipo and LymAdipoR during Memory Formation The behavioral changes in escape behavior by operant conditioning were examined (Figure 8). The negative reinforcement cohort (i.e., KCl stimulation) had significantly fewer escape attempts than the neutral reinforcement cohort (i.e., DW stimulation) in the first 20 min of the posttest period (F[9,144] = 2.170, $$p \leq 0.027$$ for interactions, $$p \leq 0.025$$ by Bonferroni test, $$n = 9$$ each), whereas there was no significant difference in the first 20 min of the pre-test period ($$p \leq 0.148$$ by Bonferroni test, $$n = 9$$ each) (Figure 8a). The latency between the first escape attempt in the pre- and post-tests showed that the latency of the negative reinforcement cohort did not change between the pre- and post-tests (F[1,16] = 9.280, $$p \leq 0.008$$ for interactions, $$p \leq 0.742$$ by Bonferroni test, $$n = 9$$ each), whereas it decreased in the posttest of the neutral reinforcement cohort ($$p \leq 0.001$$ by Bonferroni test, $$n = 9$$ each) (Figure 8b). The latency did not differ significantly between the negative reinforcement cohort and the neutral reinforcement cohort in the pretests ($$p \leq 0.711$$ by Bonferroni test, $$n = 9$$ each), whereas there was a significant difference between them in the posttests ($$p \leq 0.015$$ by Bonferroni test, $$n = 9$$ each). Based on these results, we judged that an operant conditioning of escape behavior using an aversive stimulus was established. Changes in the mRNA expression levels of LymAdipo, LymAdipoR, MIP II, and MIP R associated with the operant conditioning of escape behavior were examined by real-time PCR (Figure 9). The results show that the expression of LymAdipoR and MIPR was significantly higher in the negatively reinforced (KCl) cohort than in the neutrally reinforced (DW) cohort (LymAdipoR: $$p \leq 0.0382$$, MIPR: $$p \leq 0.016$$, $$n = 9$$ each). The expression of LymAdipo was not different between the KCl cohort and the DW cohort. That of MIP II was also constant regardless of the negative or neutral reinforcement. These results suggest that the adiponectin-signaling cascade, especially including LymAdipoR, is involved in learning and memory in Lymnaea via the regulation of the hemolymph glucose concentration (Figure 7) and the activation of insulin signaling (Figure 9). ## 4. Discussion We identified the putative molecules of adiponectin and its receptor in Lymnaea, LymAdipo, and LymAdipoR. LymAdipo, comprising 264 amino acids, has a Gly-X-Y collagen-like repeat at the N-terminal and a globular C1q domain at the C-terminal. These domains are common sequences in the C1q family of proteins, which includes adiponectin [19]. The results suggest that the C1q family proteins in molluscs are not as segmented as those in vertebrates and that they contain “primitive” adiponectin-like proteins. As with mammalian adiponectin receptors, seven transmembrane regions were predicted in LymAdipoR, and the N-terminal intracellular region and the C-terminal extracellular region were revealed. The results suggest that LymAdipoR is a member of the progestin and adipoQ receptor (PAQR) family, to which the mammalian adiponectin receptor belongs, and that it possesses the sequences necessary for adiponectin receptor function. Vertebrates have two types of receptors, AdipoR1 and AdipoR2, whereas molluscs have only one type of receptor, with the exception of the octopus Octopus sinensis. That is, octopuses have two types of receptors. This also suggests that the subdivision of the adiponectin pathway occurred during post-vertebrate evolution and that molluscan adiponectin-like proteins are “primitive” [23]. The real-time PCR and in situ hybridization experiments revealed that LymAdipo is highly expressed in the cerebral ganglia, and LymAdipoR is highly expressed in the pedal ganglia and the cerebral ganglia. In the arcuate nucleus of the mouse hypothalamus, adiponectin and adiponectin receptors are co-expressed with leptin [41]. Leptin modifies feeding behavior by activating pro-opiomelanocortin (POMC)-expressing neurons [42], suggesting that adiponectin receptors in the mouse are involved in appetite suppression. In Drosophila larvae, adiponectin receptor-like proteins have been identified in neurons in the brain lobes, and are known to suppress the juvenile hormone (JH) response and promote insulin signaling [43]. In the present study, the main localization site of LymAdipo and LymAdipoR in the Lymnaea CNS was the cerebral ganglia. In particular, the cerebral giant cells in the cerebral ganglia are necessary for the control of feeding behavior including CTA in Lymnaea [40], and these contain LymAdipo and LymAdipoR. Taken together, we can speculate that the adiponectin-signaling cascade in Lymnaea may also function to regulate feeding behavior. LymAdipoR expression was significantly increased by severe food deprivation (i.e., Day 5 snails). In the mouse brain, adiponectin receptors are upregulated during food deprivation [41], which is consistent with the findings in Lymnaea. However, because learning and memory performance in Lymnaea CTA is enhanced by mild food deprivation (i.e., Day 1 snails) [13,36], a positive effect of the adiponectin-signaling cascade on the learning and memory performance suggests that other factors (e.g., 5-HT concentration) that reduce the learning and memory performance are at work in Day 5 snails [36]. Alternatively, there may be an optimal LymAdipoR concentration range for adequate functioning of learning and memory capacity. The pedal ganglia, which most abundantly expressed LymAdipoR, regulates locomotion. The escape behavior is a kind of locomotion. AdipoR is also expressed in the cerebral ganglia, which is strongly involved in memory consolidation. On the other hand, HG individuals reared on a 20 mM sucrose for 2 days had significantly lower LymAdipo expression compared with severely food-deprived snails (Day 5). MIP II expression was significantly lower than that in Day –1 snails (i.e., the normal nutritional condition) and decreased to the same level as in Day 5 snails. These results suggest that the HG snails were in a “diabetic” state. The expression of adiponectin is reduced in obese rhesus macaques, which frequently develop type 2 diabetes mellitus [44]. On the other hand, we examined the voluntary movement to find whether a 20 mM sucrose solution affected Lymnaea behavior. The “active” or “quiescent” state, which was defined by Stephenson and Lewis [45], was studied using five snails each in the control and HG groups for 1 h. As a result, both groups were always in an “active” state and no difference was observed. The possibility that the HG sucrose solution causes osmotic stress to Lymnaea is unlikely because there is no change in their behavior. Furthermore, in our experiment, HG was only used to measure the expression level of genes, so whether it was osmotic stress or not was not an issue in this experiment. In the future, we believe that further caution will be necessary when performing behavioral experiments in the state of hyperglycemia. MIP II expression decreased with progressive food deprivation. This is thought to be due to a decrease in hemolymph glucose levels, and thus a decrease in the need for insulin. It is also possible that when MIP II expression levels are low but sensitivity is high, the insulin spike becomes high, thereby establishing learning [13]. In the Lymnaea CNS, the expression of LymAdipoR increased and that of MIP II decreased with food deprivation, suggesting that the decreased expression of MIP II is compensated for by increasing the expression of LymAdipoR to increase insulin sensitivity. The learning experiment used in the present study was operant conditioning of escape behavior, and thus the pedal ganglia and cerebral ganglia are important because escape can be learned by associating escape behavior with aversive stimuli. The high expression of LymAdipo and LymAdipoR in these characteristic ganglia leads us to imagine that the adiponectin-signaling cascade is somehow involved in escape behavior. Regarding the fact that the latency did not decrease after training with KCl but decreased with DW, it is thought that this is because Lymnaea became accustomed to the dish lid environment through the experiment. In the KCl cohort, the habituation to the dish lid environment was hindered by the aversive stimulus to KCl, and as a result, there was no difference in latency between pre- and post-tests, and it is believed that the latency decreased only in the DW cohort. We view the mechanism of operant conditioning of escape behavior as follows: Previous reports have already shown that the negative reinforcement did not weaken the Lymnaea mobility but made Lymnaea strictly understand to keep its position at a safe place [34]. The interaction between the neural pathways for withdrawal response and those for locomotion (foot-muscle extension) [46,47,48] is probably important. Therefore, in the future, we would like to deepen our understanding of the operant conditioning of escape behavior by performing experiments such as insulin injection in the same manner as for conditioned taste aversion [13]. ## 5. Conclusions The present study furthers our understanding that adiponectin-like proteins are possibly involved in the regulation of learning and memory. The research use of invertebrates, such as Lymnaea, is helpful to show the relationship among the actions of adiponectin, the function of insulin, and the regulation of learning and memory. ## References 1. Álvarez B., Koene J.M., Hollis K.L., Loy I.. Learning to anticipate mate presence shapes individual sex roles in the hermaphroditic pond snail,. Anim. Cogn. (2022.0) 25 1417-1425. DOI: 10.1007/s10071-022-01623-7 2. Hatakeyama D., Chikamoto N., Fujimoto K., Kitahashi T., Ito E.. 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--- title: Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus authors: - Mohadeseh Zarei Ghobadi - Elaheh Afsaneh - Rahman Emamzadeh - Mona Soroush journal: BMC Medical Genomics year: 2023 pmcid: PMC10045051 doi: 10.1186/s12920-023-01492-0 license: CC BY 4.0 --- # Potential miRNA-gene interactions determining progression of various ATLL cancer subtypes after infection by HTLV-1 oncovirus ## Abstract ### Background Adult T-cell Leukemia/Lymphoma (ATLL) is a rapidly progressing type of T-cell non-Hodgkin lymphoma that is developed after the infection by human T-cell leukemia virus type 1 (HTLV-1). It could be categorized into four major subtypes, acute, lymphoma, chronic, and smoldering. These different subtypes have some shared clinical manifestations, and there are no trustworthy biomarkers for diagnosis of them. ### Methods We applied weighted-gene co-expression network analysis to find the potential gene and miRNA biomarkers for various ATLL subtypes. Afterward, we found reliable miRNA-gene interactions by identifying the experimentally validated-target genes of miRNAs. ### Results The outcomes disclosed the interactions of miR-29b-2-5p and miR-342-3p with LSAMP in ATLL_acute, miR-575 with UBN2, miR-342-3p with ZNF280B, and miR-342-5p with FOXRED2 in ATLL_chronic, miR-940 and miR-423-3p with C6orf141, miR-940 and miR-1225-3p with CDCP1, and miR-324-3p with COL14A1 in ATLL_smoldering. These miRNA-gene interactions determine the molecular factors involved in the pathogenesis of each ATLL subtype and the unique ones could be considered biomarkers. ### Conclusion The above-mentioned miRNAs-genes interactions are suggested as diagnostic biomarkers for different ATLL subtypes. ### Supplementary Information The online version contains supplementary material available at 10.1186/s12920-023-01492-0. ## Background Adult T-cell leukemia/lymphoma (ATLL) is virus-caused cancer that is developed after infection by Human T-cell leukemia virus type-1 (HTLV-1) [1]. ATLL is diagnosed by the aggressive T-cell and malignant lymphoproliferations which are increased in the infected individuals after likely a long latency period [2]. The prevalence of ATLL is approximately $5\%$ among HTLV-1 infected cases. Based on Shimoyama classification, ATLL is categorized into four major subtypes: acute, lymphoma, chronic, and smoldering. The first two are aggressive with a poor prognosis and the last two include an indolent clinical period with disparate clinicopathologic characteristics. The acute type is more common and usually is associated with high amounts of serum lactose dehydrogenase and leukemia. The lymphoma cells are present in the blood and affect the bones, skin, lymph nodes, spleen, and liver. In addition, lymphomatous ATLL is infrequent and grows quickly. Also, it can impress the brain and spinal cord with an increase in the lymph nodes. Chronic ATLL develops leisurely similar to the smoldering type and elevates T cells and lymphocytes in the blood. It can influence the lungs, skin, spleen, liver, and lymph nodes. Smoldering ATLL can also affect the lungs and skin which leads to unusual T-cell counts [3–5]. MicroRNAs (miRNAs) are a category of non-coding RNAs with a length of almost 19–25 nucleotides that regulate the expression of different genes. They have effects on various biological functions such as proliferation, cell cycle, apoptosis, differentiation, and immune response. The conceivable roles of miRNAs in the progression of ATLL and tumorigenesis have been specified [6–8]. Different ATLL subtypes have a poor prognosis because of the intrinsic chemoresistance and the severe immunosuppression in addition to their heterogeneous advent. The combination of chemotherapy drugs and miRNAs can be a suitable remedy for ATLL [9]. Several papers have introduced the genes and miRNAs implicated in the progression of ATLL without considering different subtypes [10–12]. Therefore, the exploration of miRNA-gene interactions in various ATLL subtypes to propose potential therapeutic targets using computational algorithms could be advantageous. Weighted gene co-expression network analysis (WGCNA) is a potent algorithm that could cluster the genes through the calculation of correlations between them. The identified clusters named modules contain the co-expressed gene groups which likely participate in the same biological pathways. Moreover, assessing the preservation of the identified modules in the external data could lead to identifying the specific modules involved in disease [10]. We recently used machine learning to classify different ATLL subtypes based on the mRNA and miRNA datasets [9]. However, we could only find one common miRNA and a few genes for each subtype. In this study, we employed the weighted gene co-expression method for finding specific coding and non-coding RNA interactions for three subtypes of ATLL. It sheds light on the pathogenesis mechanisms from asymptomatic carriers (ACs) toward the progression of each ATLL subtype. ## Gene expression datasets and preprocessing The microarray gene expression datasets GSE33615 [13], GSE55851 [14], GSE29312 [15], and GSE29332 [15] were downloaded from the database Gene Expression Omnibus (GEO). The two first datasets include the gene expression levels in the Peripheral Blood Mononuclear Cells (PBMCs) or the whole blood of patients with one of the ATLL subtypes including acute, chronic, and smoldering. The last two datasets contain the gene expression levels in the PBMCs of AC carrier samples. Totally, 29, 23, and 10 subjects including ATLL with acute, chronic, and smoldering subtypes, respectively, as well as 37 AC subjects were used for further analysis. In addition, GSE31629 [13] and GSE46345 [16] datasets containing the miRNAs expression levels of 40 ATLL and 12 ACs subjects were employed to analyze the non-coding RNA data. The dataset details are explained in Table 1. The possible batch effect among datasets was removed using the function of removeBatchEffect in the Limma package version 3.54 in the R 4.2.2 environment [10, 17–21]. The data was also quantile normalized. Table 1Details of the datasets involved in the analysisDatasetNumber of SamplesLink to dataset Gene datasets GSE33615Acute: 26Chronic: 20Smouldering: 4 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE33615 GSE55851Acute: 3Chronic: 3Smouldering: 6 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE55851 GSE29312ACs: 20 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29312 GSE29332ACs: 17 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE29332 miRNA datasets GSE31629ATLL: 40 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE31629 GSE46345ACs: 12 https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=46345 ## Weighted gene co-expression network The weighted gene co-expression network was constructed employing the R package “WGCNA” version 1.71 [22]. WGCNA was used to find clusters of co-expressed genes that likely are involved in similar biological pathways. To identify these clusters, known as modules, an adjacency matrix was initially calculated using Pearson correlation between pairs of genes/miRNAs, with the optimized soft power. The “pickSoftThreshold” function was used to identify scale-free topology fitting indices against different soft thresholding powers β. Afterward, the Topological Overlap Matrix (TOM) was determined by transforming the adjacency matrix. Highly co-expressed genes were then grouped using hierarchical clustering. Next, the dynamic tree cut algorithm was applied to cut dendrogram branches and to identify gene modules. The close modules were merged utilizing the mergeCloseModules function. ## Identification of specific modules for each subtype In this step, the module’s preservation for each individual ATLL subtype in the ACs expression dataset was determined. To this end, the “modulePreservation” function in the WGCNA package (version 1.71) was utilized. The module preservation statistics introduced a measure indicating the preservation or somewhat non-preservation of a module between a reference network and a test network [23]. In this study, the co-expression networks of ATLL subtypes were considered as the reference and ACs as the test network. The same analysis was performed for the miRNA dataset. The parameters of Zsummary (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{ {Z}_{\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}}+ {Z}_{\text{c}\text{o}\text{n}\text{n}\text{e}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}}}{2}$$\end{document}) and medianRank (\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\frac{ {\text{m}\text{e}\text{d}\text{i}\text{a}\text{n}\text{R}\text{a}\text{n}\text{k}}_{\text{d}\text{e}\text{n}\text{s}\text{i}\text{t}\text{y}}+ {\text{m}\text{e}\text{d}\text{i}\text{a}\text{n}\text{R}\text{a}\text{n}\text{k}}_{\text{c}\text{o}\text{n}\text{n}\text{e}\text{c}\text{t}\text{i}\text{v}\text{i}\text{t}\text{y}}}{2}$$\end{document}) were measured to determine the preservation of modules. Zsummary and medianRank combine various preservation statistics into individual measures of preservation. These two measures are both important for deciding the preservation of a network module. In this study, Zsummary determines whether modules identified in the ATLL datasets remain highly connected in the ACs dataset (density) and whether the connections between the genes in each module are the same between the ATLL and ACS datasets (connectivity) [24]. The medianRank is beneficial to compare the preservation among several modules so that a module with a higher medianRank shows weaker preservation statistics than a module with a lower median rank. It is highly independent of module size [23]. Modules with Zsummary<2 and medianRank≥8 were regarded as non-preserved gene co-expression modules in the ACs group and so are specific for each ATLL subtype [25–27]. Moreover, Zsummary<2 was considered to determine specific miRNA co-expression modules for ATLL. ## Deteremining differentially expressed genes and miRNAs To determine the differentially expressed genes (DEGs) and differentially expressed miRNAs (DEMs) between ATLL and ACs groups, the Bioconductor package Limma (version 3.54) was employed. The statistically meaningful DEGs and DEMs were identified by applying Benjamini-Hochberg adjusted p-value [28] cutoff of less than 0.05. ## Identification of target genes for miRNAs The unique DEGs in the preserved modules in each ATLL subtype were determined (U_DEGs). Moreover, the unparalleled DEMs in the preserved modules in ATLL were also found (U_DEMs). Next, the miRTarBase database containing the experimentally validated miRNA-target gene interactions was searched to determine the target genes of the U_DEMs [8]. Afterward, the common genes between these target genes and U_DEGs were determined (C_DEGs). Finally, the interactions of miRNA-genes was depicted in Cytoscape 3.6.1. ## Stepwise method to perform analysis The steps of the performed analyses in this study are shown in a flowchart (Fig. 1). Briefly, we first prepared data for further analysis by merging different datasets and pre-precessing. Then, we constructed the weighted gene/miRNAs co-expression networks. Afterward, we determined the specific gene modules for each ATLL subtype/miRNA module for ATLL through performing module preservation analysis and finding unique genes in each gene module (U_modules). In the next step, we identified DEGs and DEMs between ATLL and ACs and then found unique DEGs for each subtype. We further identified shared genes between unique DEGs and genes in U_modules (U_genes) as well as common miRNAs between DEMs and miRNAs in U_modules (U_miRNAs). Following the determination of the target genes of U_miRNAs, we explored the shared genes between the target genes of U_miRNAs and U_genes (C_genes). Finally, we constructed miRNA-gene interactions between miRNAs and C-genes for each subtype. Fig. 1Flowchart of the step-wise analyses in this study ## Construction of WGCNs A total of 14,837 common genes were used to construct three weighted co-expression networks for three ATLL subtypes. At first, the soft-thresholding power (β) of 7, 17, and 2 were determined as the optimum quantities to obtain a scale-free topology for acute, chronic, and smoldering, respectively. After calculating adjacency matrix power β, TOM dissimilarity, hierarchical clustering, cutting the clusters, and finally merging the close clusters, nine modules were identified for ATLL_acute, seven modules for ATLL_chronic, and nine modules for ATLL_smoldering (Grey module contains the genes that are not assigned to any of the modules). Figure 2a-c indicates the dendrogram and the identified modules specified by a unique color for each subtype. Moreover, a weighted gene co-expression network was constructed for miRNA ATLL samples. No dataset comprising the miRNA expression for each ATLL subtype is available, so we presumed the miRNA expression for ATLL regardless of its subtype. The β of 10 was determined as the optimum value to reach a scale-free topology. Figure 3 demonstrates the dendrogram and the four obtained modules. Fig. 2Dendrogram of clustered genes constructed by WGCNA based on (1-TOM) for (a) ATLL acute subtype (ATLL_acute), (b) ATLL chronic subtype (ATLL_chronic), and (c) ATLL smoldering subtype (ATLL_smoldering) with the specified module colors. Each color denotes a module (group of genes) determined by the dynamic tree cut algorithm before and after merging modules Fig. 3Dendrogram of clustered genes constructed by WGCNA based on (1-TOM) for miRNA dataset of ATLL with the specified module colors. Each color denotes a module (group of genes) determined by the dynamic tree cut algorithm before and after merging modules ## Identification of non-preserved modules To identify specific modules for each of the three ATLL subtypes, their preservations in the ACs dataset were investigated. The modules with medianRank ≥ 8 and Zsummary < 2 were considered as specific non-preserved gene modules and Zsummary < 2 for miRNA modules. Figure 4a-c demonstrates the plots of Zsummary scores and Fig. 4d-f indicates the plots of medianRank scores versus module size for ATLL_acute, ATLL_chronic, and ATLL_smoldering, respectively (Supplementary data file 1). Therefore, blue4 and coral4 modules in ATLL_acute, darkorange and navajowhite2 modules in ATLL_chronic, and darkseagreen2 module in ATLL_smoldering were found as specific and subtype-related modules. Figure 5a,b also represents the plots of Zsummary and medianRank scores for ATLL_miRNA and shows the preservation of turquoise and yellow modules in ATLL (Supplementary data file 1). Next, we determined the unique genes in each specific module among all ATLL subtypes. Since they are not present in any other modules, we referred to them as unique modules (U_modules, Supplementary data file 2). The miRNAs in the preserved modules in ATLL (turquoise and yellow) were also considered U_modules. In the further step, we determined DEGs between each ATLL subtype and ACs samples as well as DEMs between ATLL and ACs samples considering adj. p. value < 0.05. Then, the unique DEGs for each subtype were identified (Supplementary data file 3). Afterward, the common ones between genes/miRNAs in each U_module and DEGs/DEMs called U_genes/U_miRNAs were found (Supplementary data file 4). Fig. 4Preservation Zsummary (a-c) and medianRank (d-e) versus module size for ATLL acute subtype (ATLL_acute), ATLL chronic subtype (ATLL_chronic), and ATLL smoldering subtype (ATLL_smoldering), respectively. The modules below the dashed line (Zsummary<2 and medianRank ≥ 8) are the specific modules for each ATLL subtype Fig. 5Preservation (a) Zsummary and (b) medianRank versus module size after constructing a weighted miRNA co-expression network. The modules below the dashed line (Zsummary<2 and medianRank ≥ 8) are the specific modules for ATLL. ## Constructing miRNA‑gene interactions To find the experimentally validated target genes of U_miRNAs, the miRTarBase database was explored (Supplementary data file 5). Next, the shared genes between the target genes and U_genes (C_genes) for each subtype were explored. As a result, the interactions of miR-29b-2-5p and miR-342-3p with LSAMP in ATLL_acute, miR-342-5p with FOXRED2, miR-342-3p with ZNF280B, and miR-575 with UBN2 in ATLL_chronic, miR-1225-3p and miR-940 with CDCP1, miR-423-3p and miR-940 with C6orf141, miR-324-3p with COL14A1 in ATLL_smoldering were found (Fig. 6). The identified miRNA-gene interactions may be involved in the pathogenesis mechanism and development of each subtype. Moreover, the unique ones in these interactions could be considered potential biomarkers. Fig. 6The unique miRNA-gene interactions for (a) ATLL_acute, (b) ATLL_chronic, (c) ATLL_smoldering. ## Discussion The identification of the potential role of genes and miRNAs in the development of each ATLL subtype is crucial for understanding the pathogenesis mechanism and identifying therapeutic targets. In this study, we utilized the weighted gene co-expression analysis procedure to identify the particular co-expressed genes in three subtypes of ATLL. In the following, we discuss the determined genes and miRNAs that probably have the main roles in the progression of each ATLL subtype cancer. In the acute subtype, LSAMP gene and its interaction with miR-29b-2-5p and miR-342-3p were identified. LSAMP encodes a neuronal surface glycoprotein present in the subcortical and cortical regions of the limbic system. LSAMP can be involved in tumor suppression and neuropsychiatric disorders [29, 30]. Furthermore, miR-29b-2-5p and miR-342-3p barricade cell proliferation and promote apoptosis. Their functions have been determined in several cancers, such as pancreatic ductal adenocarcinoma, cervical cancer, and non-small cell lung cancer [31–33]. The lower expression of LAMP may be related to the higher expressions of miR-29b-2-5p and miR-342-3p that ultimately result in tumor suppression [30]. In the chronic subtype of ATLL, FOXRED2 and ZNF280B were found to have interconnections with miR-342-5p and miR-342-3p, respectively, and UBN2 was also identified to have an interaction with miR-575. FOXRED2 is an unstable protein that is probably implicated in the ubiquitin-dependent ERAD pathway and is essential for the modulation of the proteasome [34]. The inhibitors of proteasome induce apoptosis, which can have an antitumor effect [35]. The function of FOXRED2 in cancer is not yet fully understood, and further studies are required to investigate its role in chronic ATLL. ZNF280B is known as an oncogene that encodes a transcription factor protein inducing the overexpression of MDM2. MDM2 boosts tumor constitution and cancer cell growth by targeting some tumor repressor proteins like p53 [36, 37]. MiR-342-5p is a downstream molecule of Notch signaling implicated in the regulation of Endothelial cells (ECs) during angiogenesis. Its higher expression weakens angiogenesis and promulgated EndMT. MiR-342-5p likely acts as a tumor suppressor and may also suppress migration and cell proliferation [38, 39]. Similarly, miR-342-3p represses cell growth and proliferation and also inhibits migration and invasion [32, 40]. The overexpression of these two miRNAs by targeting ZNF280B and FOXRED2 could suppress tumorigenesis and cell proliferation in chronic ATLL. On the other hand, UBN2 is a nuclear protein with the capability of interacting with several transcription factors. It acts as an oncogene that can be involved in the proliferation and tumorigenicity of cancer cells [41]. UBN2 can contribute to the transcription of the KRAS gene as a sector of histone chaperone. The cell cycle can be regulated by KRAS signaling through phosphorylation and interdicting p21 and p27 to mitigate cyclinD1 [42]. UBN2 is targeted by miR-575 as an oncomir that can boost cell proliferation and migration in some cancer cells and possibly chronic ATLL [43–45]. In the smoldering subtype of ATLL, CDCP1, C6orf141, and COL14A1 were found. CDCP1 is a known protein implicated in malignancies of multiple cancers. It associates with important tumorigenic signaling cascades, comprising the PI3K/AKT, SRC/PKCδ, RAS/ERK, WNT axes, and oxidative pentose phosphate pathway [46]. Therefore, CDCP1 is a considerable therapeutic and diagnostic target [47]. C6orf141 has been found as a tumor repressor protein in oral cancer. Its promoter CpG islands are methylated in some cancer which communicates with high-density lipoprotein alterations [48]. COL14A1 is another gene whose role has not been fully understood in cancers. It is methylated in renal cell carcinoma that may act as a tumor suppressor. It associates with a poorer prognosis independent of tumor grade, size, and stage [46]. Also, it has been identified that COL14A1 has an important role in keeping the stem cell-like and self-renewal features of Liver cancer stem cells through the activation of ERK signaling [47]. MiR-940 interdicts proliferation and migration of cancer cells and miR-1225-3p implicates malignancy. These two miRNAs interact with CDCP1 [48, 49]. Moreover, miR-423-3p is an oncomir that boosts cancer cell proliferation through the promotion of the G1/S transition phase of the cell cycle [50, 51]. It is in association with miR-940 target C6orf141 in smoldering ATLL. On the other hand, miR-324-3p which targets COL14A1, suppresses the invasion and growth of some cancer cells by elevating the apoptosis [52]. Also, it was proposed that the miR-324-3p/Smad4/Wnt signaling axis could be a therapeutic target to barricade cancer progression [53]. However, more studies must be performed for finding its convenient role in tumorigenesis. On the whole, the miRNA-gene interaction networks that may contribute to the pathogenesis of each ATLL subtype were proposed. However, these networks represent only a small fraction of the complex network involved in ATLL development, and additional data are required to unveil the complete network. Therefore, future studies with larger cohorts are necessary to determine the comprehensive interaction of genes and miRNAs in each ATLL subtype. ## Conclusion In summary, we found the genes and miRNAs that could be significantly involved in the pathogenesis of three ATLL subtypes. The step-wise analysis revealed unique genes/miRNA in the identified interactions, including LSAMP and miR-29b-2-5p in acute, FOXRED2, UBN2, miR-342-5p, and miR-575 in chronic, and CDCP, C6orf141, COL14A1, miR-1225-3p, miR-940, miR-423-3p, miR-324-3p in smoldering subtypes. These genes and miRNAs could serve as potential biomarkers. 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--- title: Anti-Inflammatory Effect of Caffeine on Muscle under Lipopolysaccharide-Induced Inflammation authors: - Tuany Eichwald - Alexandre Francisco Solano - Jennyffer Souza - Taís Browne de Miranda - Liebert Bernardes Carvalho - Paula Lemes dos Santos Sanna - Rodrigo A. Foganholi da Silva - Alexandra Latini journal: Antioxidants year: 2023 pmcid: PMC10045054 doi: 10.3390/antiox12030554 license: CC BY 4.0 --- # Anti-Inflammatory Effect of Caffeine on Muscle under Lipopolysaccharide-Induced Inflammation ## Abstract Evidence has shown that caffeine administration reduces pro-inflammatory biomarkers, delaying fatigue and improving endurance performance. This study examined the effects of caffeine administration on the expression of inflammatory-, adenosine receptor- (the targets of caffeine), epigenetic-, and oxidative metabolism-linked genes in the vastus lateralis muscle of mice submitted to lipopolysaccharide (LPS)-induced inflammation. We showed that caffeine pre-treatment before LPS administration reduced the expression of Il1b, Il6, and Tnfa, and increased Il10 and Il13. The negative modulation of the inflammatory response induced by caffeine involved the reduction of inflammasome components, Asc and Casp1, promoting an anti-inflammatory scenario. Caffeine treatment per se promoted the upregulation of adenosinergic receptors, Adora1 and Adora2A, an effect that was counterbalanced by LPS. Moreover, there was observed a marked Adora2A promoter hypermethylation, which could represent a compensatory response towards the increased Adora2A expression. Though caffeine administration did not alter DNA methylation patterns, the expression of DNA demethylating enzymes, Tet1 and Tet2, was increased in mice receiving Caffeine+LPS, when compared with the basal condition. Finally, caffeine administration attenuated the LPS-induced catabolic state, by rescuing basal levels of Ampk expression. Altogether, the anti-inflammatory effects of caffeine in the muscle can be mediated by modifications on the epigenetic landscape. ## 1. Introduction Regular, moderate-intensity exercise has been proven to promote an anti-inflammatory state that helps prevent the development of chronic diseases (for a review see [1]). Strenuous exercise can lead to increased levels of blood proinflammatory cytokines, which are linked to fatigue, and therefore, to reduced performance [2]. This scenario has led to caffeine administration being used to increase alertness [3], to accelerate metabolism [4], and to delay fatigue development in aerobic and anaerobic exercises, including muscular strength [5], running [6], cycling [7], and team sports [8], among others. While it is unclear what molecular mechanisms are behind caffeine consumption and its ergogenic responses, evidence is mounting that caffeine may induce anti-inflammatory effects in both humans, and animals. For example, it has been demonstrated that caffeine supplementation reduced inflammatory markers in the blood of athletes [9,10,11]. In the case of animal models, reduced pro-inflammatory and increased anti-inflammatory markers were not only seen in the blood of trained rats, but also in key tissues linked to exercise performance, such as the brain, the lung, the heart and the skeleton, of rodents exposed to caffeine [12,13,14,15,16]. Furthermore, an elegant study involving 114 participants showed that caffeine intake is associated with lower inflammation and activation of the inflammasome, which resulted in less production of the pro-inflammatory cytokine interleukin-1 beta (IL-1b) [17]. In addition, caffeine supplementation has been shown to cause changes in gene expression that could be linked to improved exercise performance [18,19,20]. These modifications have been related to altered epigenetics, a term conceived to describe the possible causal processes acting on genes that regulate phenotype [21]. Some of the reported effects of caffeine are associated with DNA methylation [22], a major epigenetic factor influencing gene activities. Considering that epigenetics can change the activity of a DNA segment without changing the sequence, it is plausible that caffeine can modulate inflammatory processes by changing the epigenetic landscape. When DNA methylation is increased in a gene promoter, it will typically act to repress gene transcription, including the expression of inflammatory mediators. Altogether, we aimed to understand whether caffeine can modulate epigenetics to induce an anti-inflammatory scenario in the mouse skeletal muscle. ## 2.1. Animals Adult Swiss male mice (3–5 months of age; body mass 45–50 g) from the central animal house of the Centre for Biological Sciences, Universidade Federal de Santa Catarina (Brazil) were kept in a controlled environment (22 ± 1 °C, 12 h light/dark cycle) with water and food ad libitum, for ten days (acclimatation period). The experimental protocols were approved by the Ethics Committee for Animal Research (PP00760/CEUA) of the Federal University of Santa Catarina (Brazil). All efforts were made to minimize the number of animals used and their suffering. Five mice were included per experimental group, unless otherwise stated. ## 2.2. LPS-Induced Inflammation Acclimatized mice were randomly divided into the following 4 groups (5 animals per group): Vehicle: Animals that received an intraperitoneal (i.p.) injection of 0.9 % sodium chloride (injection volume of 0.1 mL for every 10 g of body weight); Caffeine: Animals that received an i.p. injection of caffeine (6 mg/kg of body weight); LPS: Animals that received an i.p. injection of LPS (0.33 mg/kg of body mass; E. coli LPS, serotype 0127:B8), and Caffeine+LPS: Animals that received an i.p. injection of caffeine and 15 min later received the injection of LPS. Twenty-four hours after the treatment mice were euthanized by cervical dislocation and the vastus lateralis muscle was immediately collected and processed in Trizol as previously published by our group [23]. The dosage of LPS used was based on previously published data [24,25]. ## 2.3. RNA Extraction and cDNA Synthesis For total RNA extraction, the vastus lateralis muscle was collected 24 h after the administration of the different treatments (vehicle, caffeine, LPS and Caffeine+LPS), and immediately homogenized with Ambion TRIzol Reagent (Life Sciences, Fisher Scientific Inc., Waltham, MA, USA). After adding 200 μL of chloroform, and followed by centrifugation at 17,000× g for 15 min at 4 °C, the upper aqueous layer containing the RNA was collected and transferred to a new tube. Then, 800 μL of chilled isopropanol were added and after light agitation, the RNA was precipitated by centrifugation at 17,000× g for 15 min at 4 °C. The supernatant was removed by inversion and the precipitated RNA was washed with 1 mL of 70 % alcohol and again centrifuged at 17,000× g for 5 min at 4 °C. Supernatants were discarded and 50 μL of nuclease-free H2O was added to the tube. The quantity and purity of the extracted RNA was estimated by using the NanoDrop spectrophotometer, at 260 and 280 nm. The synthesis of the cDNA was performed after treating the total RNA with DNase I (Invitrogen, Carlsbad, CA, USA), and with high-capacity cDNA Reverse Transcription Kit (Applied Biosystems, Foster City, CA, USA), according to the manufacturer’s instructions. ## 2.4. Real-Time Reverse Transcription and Quantitative PCR (RT-qPCR) Real-time reverse transcription and quantitative PCR (RT-qPCR) reactions were performed using SYBR Green Master Mix (PowerUp™ SYBR™ Green Master Mix-Applied Biosystems, Foster City, CA, USA) with specific primers shown in Table 1. All the reactions were carried out in a total of 10 μL, containing 5 μL of specific primers (0.4 μM of each primer), 50 ng of cDNA and nuclease-free H2O in a QuantStudio® 3 Real-Time PCR (Thermo Fisher Scientific, Waltham, MA, USA). ## 2.5. DNA Extraction Genomic DNA (gDNA) was extracted from the mouse muscle 24 h after the treatments. Tissues were homogenized in extraction buffer (10 mM Tris pH 3.0; $0.5\%$ SDS, 5 mM EDTA) and then digested with proteinase K solution at 65 °C for 16 h. Additionally, 500 μL of equilibrium phenol was transferred to the tube and thus the mixture was spun down at 17,000× g for 15 min. The upper aqueous layer containing the target DNA was preserved and mixed with 200 μL of chloroform. The mixture was centrifuged at 17,000× g for 15 min and the supernatant was collected and transferred to a new tube. Then, 800 μL of isopropanol and 150 μL sodium acetate 3 M was added to the mixture. Next, the mixture was centrifuged at 17,000× g for 15 min. The supernatant was removed, and the pellet was washed with 500 μL of 70 % alcohol, centrifuged at 17,000× g for 5 min. The supernatant was then completely discarded and 50 μL of nuclease free H2O was added to the tube. The quantity and purity of extracted gDNA was estimated by using the spectrophotometer apparatus NanoDrop, at 260 and 280 nm. ## 2.6. Enzymatic gDNA Treatment After confirming the quantity and purity by spectrophotometry (OD $\frac{260}{280}$ ≥ 1.8 and OD $\frac{260}{230}$ ≥ 1.0), the gDNA was treated with T4-β-glucosyltransferase (T4-BGT) and subsequently with MspI and HpaII (New England BioLabs, Beverly, MA, USA). For this, three tubes (A, B and C) containing 400 ng gDNA of each sample were treated with 40 mM UDP glucose and T4-BGT (1 unit) for 1 h at 37 °C, followed by enzyme inactivation for 10 min at 65 °C. Next, the samples were digested with H2O (tube A), MspI (tube B) and HpaII (tube C) for 2 h at 37 °C according to the manufacturer’s instructions. ## 2.7. Methylation-Specific qPCR (MS-qPCR) MS-qPCR methylation data were derived from 5 independent animals and a technical duplicate. The pattern of methylation (5-meC) and hydroxymethylation (5-hmeC) of the promoter regions of Adora1 (island 1 (F: 5′ AAG GAG CTC ACC ATC CTG 3′); (R: 5′ GTG GGT GGG CAC AGG GTA G 3′) and island 2 (F: 5′ CGA GAC TCC ACT CTG GC 3′); (R: 5′ CAC CTC GGT ACT GTC CCT GT 3′)) and Adora2A (F: 5′ AGG GTG CGC CCA TGA GCG GC 3′); (R: 5′ CAA CCC GAG AGT CTG ACC CGC CT 3′) were determined in qPCR reactions containing 2x SYBR Green I Master (5 µL), 0.4 µM specific primers (1 µL), 25 ng of treated gDNA (1.5 µL-3 conditions: H2O, MspI and HpaII) and q.s.p of nuclease-free H2O (2.5 µL). Primer sequences were designed in regulatory regions with CpG islands within regions of hypersensitivity to DnaseI, regulated by histone modification markers and with transcription factor binding sites using the Primer3 Input program (version 0.4.0) [26]. All primers sequences were blasted to confirm chromosomal location by the in-silico PCR tool (https://genome.ucsc.edu/, accessed on 15 June 2022) and the secondary structures and annealing temperatures analyzed using the Beacon Designer program (http://www.premierbiosoft.com/, accessed on 15 June 2022). ## 2.8. Statistical Analysis Data are presented as mean ± standard error of mean (SEM). Data were analyzed by two-way ANOVA followed by the post hoc test of Tukey when F was significant. When comparing two independent groups, Student’s t-test for independent samples was used. The accepted level of significance for the tests was $p \leq 0.05.$ Statistics and all graphs were performed by using GraphPad Prism 9®. ## 3.1. Caffeine Administration Reduced LPS-Mediated Inflammation in the Mouse Muscle Figure 1 shows the effect of caffeine and/or LPS administration (i.p.) after twenty-four h on pro-inflammatory cytokines gene expression in the mouse vastus lateralis muscle (Figure 1A). LPS exposure significantly increased the expression of the pro-inflammatory cytokine Il1b (F[1,16] = 6.46, $p \leq 0.01$) (Figure 1B). Moreover, the expression of the anti-inflammatory cytokines Il10 (F[1,16] = 6.73, $p \leq 0.05$) (Figure 1E) and Il13 (F[1,16] = 5.36, $p \leq 0.01$) (Figure 1F) were also upregulated, possibly as a physiological compensatory response elicited by LPS-induced inflammation. Figure 1B shows that the expressions of Il1b and Il6 (Figure 1C) were downregulated, and Il10 (Figure 1E) upregulated when caffeine was administered in association with LPS. Furthermore, caffeine per se positively modulated the expression of Tnfa (F[1,16] = 19.32, $p \leq 0.001$) (Figure 1D) and decreased the Il6 levels (F[1,16] = 2.20, $p \leq 0.05$) (Figure 1C). However, no differences were observed in the levels of Il1b (Figure 1B), Il10 (Figure 1E), and Il13 (Figure 1F). ## 3.2. The Anti-Inflammatory Effect of Caffeine Was Mediated by Downregulating Nrlp3 Inflammasome Components Figure 2 shows the effect of the administration of caffeine and/or LPS (i.p.) on NLRP3 inflammasome components gene expression in the mouse vastus lateralis muscle (Figure 2A). Figure 2 shows that LPS administration elicited the upregulation of the inflammasome assembly components Asc (F[1,15] = 28.90, $p \leq 0.001$) (Figure 2C) and Casp1 (F[1,15] = 58.57, $p \leq 0.001$) (Figure 2D) in the mouse muscle, which was prevented by the administration of caffeine (Caffeine+LPS experimental group). Although, LPS treatment per se did not alter the levels of Nlrp3 expression 24 h after the administration, the combination with caffeine provoked its upregulation (F[1,16] = 34.78, $p \leq 0.001$) (Figure 2B) in the mouse muscle. No differences were induced by caffeine administration alone. Similar results were found in the absolute gene expression (Figure S1). ## 3.3. Caffeine Administration Enhanced the Expression of Adenosinergic Receptors in the Vastus Lateralis Muscle Mice Figure 3 shows the effect of caffeine administration on adenosinergic receptors gene expression and gene methylation. Caffeine administration per se increased the expression of Adora1 (F[1,16] = 10.03, $p \leq 0.05$) (Figure 3A) and Adora2A (F[1,16] = 16.26, $p \leq 0.001$) (Figure 3B) in the mouse muscle, which was prevented by the administration of LPS alone and combined with caffeine. LPS per se increased the levels of Adora1 (F[1,16] = 10.03, $p \leq 0.01$) (Figure 3B). Considering that gene expression can be controlled by epigenetics, the levels of DNA methylation and demethylation of Adora1 and Adora2A were analyzed. While global DNA methylation was not modified by caffeine and/or LPS administration (Figure 3C,D), 5-meC/5-hmeC ratio, used as an index of DNA methylation, was increased under caffeine and Caffeine+LPS administration (F[1,16] = 0.16, $p \leq 0.05$) (Figure 3E). The higher methylation of the Adora2A promoter might represent a homeostatic mechanism to control the upregulation of the gene. Figure S2 shows that the absolute gene expression of the two ARs, Adora1 and Adora2A, were similar in basal conditions. ## 3.4. Caffeine+LPS Exposure Enhanced the De Novo DNA Methylation in the Vastus Lateralis Muscle Mice Figure 4 shows the effects of caffeine and/or LPS administration on the epigenetic profile in the mouse vastus lateralis muscle. Caffeine+LPS significantly upregulated the expression of the maintenance methylation gene Dnmt1 (F[1,16] = 101.05, $p \leq 0.001$) (Figure 4A), and de novo methylation gene Dnmt3A (F[1,15] = 1.26, $p \leq 0.001$) (Figure 4B), while the expression of de novo methylation gene Dnmt3B was significantly downregulated (F[1,16] = 0.06, $p \leq 0.001$) (Figure 4C) in the mouse muscle, when compared with the Vehicle and LPS groups. Caffeine+LPS treatment also inhibited the expression of the gene encoding the DNA demethylation enzyme Tet3 (F[1,16] = 133.5, $p \leq 0.001$) (Figure 4F), while no effect was observed on Tet1 and Tet2 when compared with the basal condition (vehicle), or under inflammation (LPS group). In addition, LPS modulated the expression of Tet genes; while LPS positively modulated the expression of Tet2 (F[1,16] = 11.84, $p \leq 0.05$) (Figure 4E), LPS administration negatively modulated the expression of Tet3 (F[1,16] = 133.50, $p \leq 0.001$), when compared with the vehicle condition. LPS treatment also compromised the expression of Dnmt3B, which was partially attenuated by caffeine co-administration (F[1,16] = 0.06, $p \leq 0.01$) (Figure 4C). Caffeine treatment per se increased the levels of Tet2 (Figure 4E), while it inhibited Tet3 expression (Figure 4F). ## 3.5. Caffeine Administration Attenuated the Catabolic State Induced by LPS Administration in the Mouse VASTUS lateralis Figure 5 shows the effects of caffeine and/or LPS catabolism in the mouse muscle. LPS treatment significantly elicited the upregulation of the energy status sensor gene Ampk (F[1,16] = 5.35, $p \leq 0.01$) (Figure 5). The coadministration of caffeine significantly reverted the effect induced by LPS. ## 4. Discussion Caffeine is a stimulant drug widely known and used due to its psychoactive and ergogenic effects [27]. The effects of caffeine on physical exercise, endurance performance, and fatigue stalling are well documented [28,29,30]. However, the molecular mechanisms behind these modulations are still under study. To the best of our knowledge, this is the first study to show that epigenetics is involved in the anti-inflammatory effects of caffeine on the vastus lateralis muscle of resting mice. Here, we showed that treatment with caffeine prevented an increase of the gene expression of LPS-induced pro-inflammatory cytokines Il1b and Il6 and promoted the upregulation of the anti-inflammatory genes Il10 and Il13 in the mouse muscle. The anti-inflammatory state observed in the caffeine experimental group occurred with decreased gene expression of the NLRP3 inflammasome components, Asc and Casp1. Indeed, the activation of caspase 1 mediates the cleavage of pro-IL-1β to generate and release its biologically pro-inflammatory active form, IL-1β [31]. Moreover, caffeine administration promoted the upregulation of the adenosinergic receptors Adora1 and Adora2A, the signaling of which is known to induce vasodilatation, healing and anti-inflammation, promotion of tissue blood flow and cellular homeostasis in different cell types [32]. Thus, in order to maintain homeostasis, the upregulation of Adora2A might have been responsible of triggering the methylation of its promoter. While DNA methylation patterns were not altered by caffeine treatment, the DNA methylating status was increased after Caffeine+LPS administration, suggesting that the observed adaptation to inflammation induced by caffeine was due to epigenetics. Caffeine is the most commonly consumed social drug to increase alertness, arousal and energy [33]. Its consumption has been related to improvement in cognitive performance and mood in healthy population [34,35], and is the main ergogenic resource used by athletes to enhance exercise performance, extend time to exhaustion, and to delay fatigue [36]. Moreover, it has been shown that caffeine upregulated dopamine metabolism and signaling, and increased the synthesis and turnover of noradrenaline, being closely associated with an improvement in both peripheral and central fatigue [37]. Furthermore, the ergogenic effects of caffeine consumption have also been shown to be more evident in fatigued than in well-rested subjects [38,39]. These effects are proposed to be mediated by the non-selective antagonism of Adora1 and Adora2A [33]. Adora1 are widely expressed in the cortex, hippocampus, cerebellum, and thalamus [33], and in the adipose tissue, stomach, kidney, and heart [40]. Due to the capacity for lowering cAMP intracellular levels, the activation of Adora1 promotes bradycardia, inhibition of lipolysis, antinociception, reduction of sympathetic and parasympathetic activity, neuronal hyperpolarization, among others [41]. In contrast, Adora2A has a more restricted distribution, being more expressed in the striatum, nucleus accumbens, and olfactory tubercle [33]. Skeletal muscle, bladder, and the immune system are the tissues with the highest density of these receptors in the periphery [42]. The activation of Adora2A triggers neurotransmitter release, anti-inflammatory immune responses, and vascular smooth muscle cell relaxation, due to the activation of signaling pathways mediated by increased cAMP intracellular levels [36,41]. Therefore, the typical effects of adenosine, the natural agonist of Adoras and the final catabolite of ATP, that are associated with tiredness and drowsiness are counterbalanced by caffeine. The effective dose of caffeine to antagonize Adoras and to lead to increased exercise time to fatigue ranges from 3 to 9 mg/kg in humans [28,43,44] and rodents [45,46]. These doses have been shown to increase performance in endurance, intermittent and resistance exercises in humans [5,6,7,8,47,48,49,50]. These effects have been associated with enhanced peripheral energy metabolism, activation of ryanodine channels for quicker release of calcium, and oxidant system in the muscle, improving muscle speed and strength (for a review see [16]). However, a large body of evidence suggests that caffeine can also mediate its ergogenic effects by inducing an anti-inflammatory status, preventing excessive endogenous catabolism and oxidative stress [1,16,51]. The anti-inflammatory effect of caffeine is also supported by the fact that individuals who suffer from cancer, obesity or liver, metabolic or neurodegenerative disorders and for whom persistent inflammation has been reported, developed fatigue when the symptoms appeared [52,53,54,55,56,57,58]. Moreover, high circulating levels of caffeine have been associated with delayed onset or reduced risk of dementia in individuals with mild cognitive impairment [59]. Furthermore, healthy individuals receiving an acute dose of caffeine showed reduced levels of pro-inflammatory markers and delayed development of fatigue [54,60]. Indeed, one of the first and most common symptoms associated with system immune activation is fatigue [61]. In addition, regular coffee consumption has also been associated with a reduced risk of low-grade inflammation in clinical conditions such as type 2 diabetes mellitus [62], and metabolic syndrome [63]. Considering that fatigue is characterized by temporary reductions in voluntary muscular force production, and cognitive and motivational changes that induces poor physical performance [64], we aimed to study whether caffeine could induce anti-inflammatory effects in the inflamed mouse muscle, and whether these effects are associate with epigenetic modifications. It has been suggested that the development of fatigue may activate pathways that promote the activation of nuclear factor kappa b (NF-κB), which is considered a prototypical pro-inflammatory signaling pathway (for a review see [65]). NF-κB is known to be activated by a wide array of mediators, including LPS, inflammatory cytokines such as IL-1b and TNF-a, and reactive oxygen species, which in turn activates several signal transduction cascades and induces changes in transcription factors that promote a pro-inflammatory status. NF-κB’s activation induces the synthesis of pro-IL-1β and the activation of caspase 1 that induces the proteolytic maturation of pro-IL-1β [31]. Caspase 1 and ASC are key components of NLRP3 inflammasome, a multiproteic complex that induces inflammatory responses and cell death in response to various danger signals [66], including pro-inflammatory cytokines, reactive species, oxidized compounds that are known to accumulate in the muscle and blood during exhaustive physical exercise [67,68,69]. Therefore, the effect of caffeine on the inflammatory response induced by LPS that we observed in the mouse muscle suggests that part of its ergogenic effects might be mediated by the inhibition of the inflammasome assembly. The anti-inflammatory effect might also be related to the positive effects we observed on Adoras’ increased expression in the inflamed muscle. Thus, increasing the antagonism of Adoras will potentiate the anti-inflammatory effect on different immune cell populations, that are cells known for their expression of high levels of Adora2A. Accordingly, it has been proposed that caffeine is an immunosuppressor since it has shown to inhibit proliferation, activation, and cytokine secretion by lymphocytes [70]. For example, it has been shown that caffeine reduced TNF-a secretion and enhanced the expression of Adora2A in LPS-activated human macrophages [70]. In addition, reduced ATP/AMP ratio, which occurs during the inflammatory response [71], is a key modulator of enhanced AMPK signaling, which denotes energy deficit. Although, we did not measure the levels of phosphorylated AMPK, the restoration of basal Ampk gene expression suggests that caffeine also protects the inflamed muscle by improving energy metabolism as previously proposed [72]. DNA methylation is also known to repress gene expression by blocking the promoter sites at which activating transcription factors are bound [73]. The reduced global DNA demethylation could be responsible for the upregulation of Ampk under LPS treatment, which was rescued when caffeine was also administered. This is also in agreement with the increased expression of pro-inflammatory cytokines, which was negatively modulated after the coadministration of caffeine in our study. This effect has also been reported in other tissues and cells. For example, LPS-challenged human peripheral blood mononuclear cells exposed to caffeine at different concentrations (10–100 μM) for 24 h, negatively modulated the production of TNF-α [74]. Similarly, mouse splenocyte cultures stimulated with concanavalin A (a pro-inflammatory agent) showed reduced production of TNF-α, IL-2, and IFN-γ when co-treated with 3.75 and 10 mM of caffeine for 24 h [75]. The DNA methylation machinery requires DNMT3a and DNMT3b for the de novo [76], and DNMT1 for the maintenance [77] of DNA methylation. In general, when methylation occurs in the promoter region of a particular gene, the gene expression is expected to be repressed. DNA can also be demethylated by the action of ten-eleven translocation (TET) enzymes TET1, TET2, and TET3 [78], which may result in enhanced gene expression. Therefore, the balance of these processes may regulate the expression of different genes, including the ones involved in inflammation and adenosine signaling as shown here. Indeed, genome-wide meta-analyses identified several genes positively associating caffeine consumption and DNA methylation [22,79,80]. 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--- title: Associations between Serum Folate Concentrations and Functional Disability in Older Adults authors: - Lujun Ji - Tianhao Zhang - Liming Zhang - Dongfeng Zhang journal: Antioxidants year: 2023 pmcid: PMC10045063 doi: 10.3390/antiox12030619 license: CC BY 4.0 --- # Associations between Serum Folate Concentrations and Functional Disability in Older Adults ## Abstract Folate may have beneficial effects on physical function through its antioxidant effect. Thus, we investigated the associations between serum folate and functional disability in older adults. Data from the National Health and Nutrition Examination Survey 2011–2018 were used. Serum folate included 5-methyltetrahydrofolate and total folate. Five domains of functional disability, including lower extremity mobility (LEM), instrumental activities of daily living (IADL), activities of daily living (ADL), leisure and social activities (LSA), and general physical activities (GPA), were self-reported. Multivariable-adjusted logistic regression models and restricted cubic splines were employed. 5-Methyltetrahydrofolate was inversely associated with IADL and GPA disability, and the multivariate-adjusted ORs ($95\%$ CIs) in the highest versus lowest quartiles were 0.65 (0.46–0.91) and 0.70 (0.50–0.96), respectively. The total folate was also inversely associated with IADL (OR quartile 4vs1 = 0.65, $95\%$ CI: 0.46–0.90) and GPA (OR quartile 3vs1 = 0.66, $95\%$ CI: 0.44–0.99) disability. The dose–response relationships showed a gradual decrease in the risk of IADL and GPA disability as serum folate increased. In the sex, age, BMI, and alcohol consumption subgroup analyses, we saw that the associations were primarily found in females, under 80 years old, normal weight, and non-drinkers. Sensitivity analyses further confirmed the robustness of our results. Our results indicated that serum folate concentrations were negatively associated with IADL and GPA disability, especially in females. In other subgroup analyses, we discovered that these negative associations were primarily prevalent in participants under 80 years old, normal weight, and non-drinkers. ## 1. Introduction Functional disability, defined as difficulty in performing basic activities of daily life [1], can induce a range of adverse health consequences, such as decreased quality of life [2], increased hospitalization rates [3], and increased mortality [4]. The number and proportion of older adults are increasing across many populations worldwide [5], and they tend to have a higher prevalence of functional disability [6], which imposes a heavy burden on society and families [7]. Consequently, exploring potentially modifiable factors that may prevent or delay the progression of functional disability is critical to reducing the burden on healthcare systems and achieving healthy aging. Studies have shown that diet may be an effective way to prevent functional disability [8]. Recently, there has been considerable interest in the relationships between functional disability and dietary or nutritional factors, such as dietary protein [9], coffee [10], vitamin K [11], selenium [12], etc. Folate is a water-soluble vitamin that plays a role in the antioxidant process [13]. Folate can attenuate oxidative stress by improving biomarkers in the antioxidant defense system [13], and oxidative stress may increase the risk of disability [14]. Folate also acts as a coenzyme of one-carbon metabolism involved in homocysteine methylation, converting homocysteine to methionine [15,16]. Studies have shown that decreased homocysteine levels may be associated with improved physical function [17,18]. Some studies have investigated the relationship between dietary folate [19,20] or folate supplements [21] and physical function in older adults. Serum folate has also attracted the attention of a few researchers, because it can better reflect the recent folate intake level [22]. Still, there are few studies on the relationship between serum folate and physical function in older adults, and the results are inconsistent. To our knowledge, a study conducted in Singapore among 796 older participants discovered that a higher serum folate concentration was related to better physical performance on balance [23]. Another study of older adults in Spanish showed that people with a satisfactory folate status scored higher on the instrumental activities of daily living (IADL) test [24]. Nevertheless, a study of 698 older Italians found no significant relationship between serum folate and subsequent physical function (defined by the Short Physical Performance Battery (SPPB) test) [25]. 5-Methyltetrahydrofolate (5-MTHF) is the main biologically active form of folate. Until recently, no studies have looked into the relationship between 5-MTHF and the risk of functional disability. Furthermore, disability is multidimensional, but previous studies have often been limited to one or two domains of functional disability. In addition, there was no clear dose–response relationship between serum folate and functional disability. As a result, we extracted data from the National Health and Nutrition Examination Survey (NHANES) 2011–2018 cycles and performed this study to assess the associations between serum folate (including 5-MTHF and total folate) and five domains of functional disability in older Americans. ## 2.1. Study Population The NHANES is a continuous survey utilizing a stratified multistage probability sample approach to assess the health and nutrition states of the United States (US)’ civilian people. The survey results are released publicly once every two years. The NHANES protocols were approved by the National Center for Health Statistics Ethics Review Committee, and each survey participant gave informed consent [26]. In this study, we combined four survey cycles of NHANES (2011–2012, 2013–1014, 2015–2016, and 2017–2018), totaling 39,156 individuals. Participants under the age of 60 were eliminated ($$n = 31$$,473). In addition, 1750 participants were ruled out because serum folate data were missing. Furthermore, participants with extreme total energy intakes (<500 or >5000 kcal/day for females, <500 or >8000 kcal/day for males) ($$n = 25$$) were removed [27]. After excluding 58 participants with incomplete functional disability information, leaving 5850 participants (2946 females and 2904 males) in the current study. Figure 1 depicts the specific screening procedure. ## 2.2. Serum Folate Assessment Isotope dilution high-performance liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) was used to measure the concentrations of five bioactive forms of serum folate, including 5-MTHF, folic acid, 5-formyl-tetrahydrofolate, tetrahydrofolate, and 5,10-methenyl-tetrahydrofolate [28]. Five bioactive forms of serum folate were added together to estimate the total folate [29]. The main bioactive form among these five is 5-MTHF, which contributes about $90\%$ of the total folate [30,31]. As a result, we included 5-MTHF and total folate in our investigations [32]. ## 2.3. Self-Reported Functional Disability Assessment The NHANES provided self-reported data on physical function [33]. Based on previous investigations [34,35,36,37], 19 well-validated questions of physical function were categorized into five domains of functional disability: lower extremity mobility (LEM), IADL, activities of daily living (ADL), general physical activities (GPA), and leisure and social activities (LSA). Detailed information is shown in Supplementary Table S1. Each question examined an individual’s ability to perform a task without using any special equipment. The options available for participants to answer were “no difficulty”, “some difficulty”, “much difficulty”, “unable to do”, or “do not do this activity”. Functional disability was defined as having any difficulty in performing one or more tasks within a given domain. ## 2.4. Other Covariates Based on the prior literature [10,17], we took into account the influences of the following factors. The demographic factors included sex, age, race/ethnicity, educational level, marital status, and poverty–income ratio (PIR). We also adjusted the personal lifestyle factors such as total energy intake, physical activity, alcohol consumption, and smoking status. Furthermore, the health conditions factors included body mass index (BMI) and some chronic diseases, including hypertension, diabetes, arthritis, stroke, gout, cancer, congestive heart failure (CHF), coronary heart disease (CHD), angina, asthma, chronic bronchitis, and emphysema. Supplementary Table S2 presents the classifications of the covariates. ## 2.5. Statistical Analyses We calculated the new sample weights in light of the NHANES weighting guidelines when merging the four 2-year cycles data to ensure the national representation of the sample. The Kolmogorov–Smirnov test was employed to identify the normality of the quantitative variables. We used the chi-square test to compare the differences between participants with and without functional disability for qualitative variables, the ANOVA test for normally distributed quantitative variables, and the Mann–Whitney U test for non-normally distributed quantitative variables. In our analyses, considering the obvious differences in the serum folate levels between males and females and based on previous literature [32,38,39], we divided the serum folate data into four groups based on sex-specific quartiles, with the lowest quartile group serving as the reference group. Age-adjusted and multivariate-adjusted binary logistic regression analyses were conducted to evaluate the relationships between serum folate and all domains of functional disability. The multivariate-adjusted model adjusted for age, race/ethnicity, educational level, marital status, PIR, physical activity, alcohol consumption, smoking status, BMI, hypertension, diabetes, arthritis, stroke, gout, cancer, CHF, CHD, angina, asthma, chronic bronchitis, emphysema, and total energy intake. Sex, age, BMI, and alcohol consumption stratification analyses were also conducted in our study. To investigate the dose–response relationships between serum folate and functional disability, we employed restricted cubic splines with 3 knots (the 5th, 50th, and 95th percentiles of the serum folate distribution) in the multivariate-adjusted model. To test the robustness of our results, we conducted the following sensitivity analyses. Firstly, considering that ignoring the presence of Mefox (an oxidation product of 5-MTHF) may lead to underestimation of the total folate [30], we further examined the relationships between the combined total folate (total folate plus Mefox) and all domains of functional disability. Secondly, we conducted secondary analyses by excluding participants using antibiotics, estrogens, and anticonvulsants. Thirdly, we minimized the confounding by excluding participants suffering from malnutrition. In addition, we additionally examined the relationships between the dietary folate intake and folate supplementation use (yes/no) and functional disability. All statistical analyses were performed using Stata 15.0 and R software, version 4.2.1. Statistical significance was considered as two-sided p-values ≤ 0.05. ## 3.1. Characteristics of the Participants The characteristics of the participants are shown in Table 1 and Supplementary Table S3. For all domains of functional disability, people with a functional disability tended to be older; single; smokers; were more likely to have less total energy intake; lower PIR; higher BMI; a lower educational level; lower physical activity; and higher prevalence of stroke, hypertension, arthritis, diabetes, CHF, CHD, angina, asthma, chronic bronchitis, and emphysema. In addition to that, participants with functional disability were more likely to be females, except for ADL disability. Participants with disability in IADL, LEM, LSA, and ADL tended to be drinkers and minority races and were more likely to have lower serum folate concentrations. Supplementary Table S4 shows the comparison results of the serum folate concentrations between males and females. We found that females had higher serum folate concentrations than males. ## 3.2. Relationships between Serum Folate and Functional Disability Table 2 and Supplementary Table S5 display the weighted odds ratios (ORs) with $95\%$ confidence intervals (CIs) for all domains of functional disability according to the quartiles of 5-MTHF and total folate. In the age-adjusted models, the concentrations of 5-MTHF and total folate were inversely related to all domains of functional disability. In the multivariate-adjusted models, we found negative relationships between 5-MTHF and IADL and GPA disability, the ORs ($95\%$ CIs) in the highest versus lowest quartiles were 0.65 (0.46–0.91) and 0.70 (0.50–0.96), respectively. We also found that elevated level of total folate was associated with decreased odds of disability in IADL (OR quartile 4vs1 = 0.65, $95\%$ CI: 0.46–0.90) and GPA (OR quartile 3vs1 = 0.66, $95\%$ CI: 0.44–0.99). We further performed stratified analyses by sex. For females, after adjusted age, we observed that 5-MTHF and total folate were negatively associated with the odds of all domains of functional disability. Compared with quartile 1 (Q1), the fully adjusted ORs ($95\%$ CIs) for IADL disability for the highest quartile of 5-MTHF and total folate were 0.52 (0.35–0.79) and 0.53 (0.35–0.78), respectively. In the multivariate-adjusted models, the risk of GPA disability decreased in Q2–Q4 for 5-MTHF and total folate (adjusted ORs ranged from 0.46 to 0.56). The results are shown in Table 3 and Supplementary Table S6. However, no statistical significance was found in males in the multivariate-adjusted models (Supplementary Tables S7 and S8). Figure 2 presents the associations between 5-MTHF and the risk of IADL and GPA disability in stratified analyses by age, BMI, and alcohol consumption. Among people aged 70–79 years old, normal weight, and non-drinkers, 5-MTHF was negatively associated with IADL disability. In addition, we found that 5-MTHF was negatively associated with GPA disability in the population of 60–69 years old, normal weight, and non-drinkers. The stratified analyses of total folate and the risk of IADL and GPA disability yielded similar findings (Supplementary Figure S1). In Figure 3, the outcomes of the restricted cubic spline analyses between 5-MTHF and the risk of IADL and GPA disability are displayed. Among overall participants and females, we found that as 5-MTHF levels increased, the risk of IADL and GPA disability decreased gradually. We found similar dose–response relationships between total folate and the risk of IADL and GPA disability (Supplementary Figure S2). ## 3.3. Sensitivity Analyses The results of the sensitivity analyses of the associations between combined total folate and all domains of functional disability were consistent with our primary results (Supplementary Table S9). After excluding 798 participants using antibiotics, estrogens, and anticonvulsants, 5-MTHF and total folate were negatively associated with three domains of functional disability (IADL, LSA, and GPA) (Supplementary Table S10). After eliminating 192 participants suffering from malnutrition, the results did not change substantially (Supplementary Table S11). Supplementary Table S12 presents the relationships between dietary folate intake and the risk of functional disability. Compared with Q1, the multivariate-adjusted ORs ($95\%$ CIs) for IADL disability of dietary folate intake in the Q3 and Q4 were 0.68 (0.50–0.93) and 0.62 (0.45–0.86), respectively. The associations between folate supplementation use and functional disability risk are shown in Supplementary Table S13. Compared with non-supplement users ($$n = 2168$$), folate supplement users had a lower prevalence of ADL disability, with an OR ($95\%$ CI) of 0.78 (0.64–0.96). ## 4. Discussion In this cross-sectional study of 5850 participants, after adjusting all covariates, higher concentrations of serum folate were related to lower odds of disability in IADL and GPA. In sex-stratified analyses, we found that serum folate concentrations were adversely associated with IADL and GPA disability in females, but no such associations were found in males. In age, BMI, and alcohol consumption subgroup analyses, negative associations between serum folate with the risk of IADL disability were observed in the population of 70–79 years old, normal weight, and non-drinkers, and with the risk of GPA disability were mainly observed in the population of 60–69 years old, normal weight, and non-drinkers. The dose–response relationships showed a gradual decrease in the risk of IADL and GPA disability as serum folate increased. Sensitivity analyses further confirmed the robustness of our results. An observational study involving 796 Singapore older adults aged 55 and above indicated that relatively higher serum folate concentration was related to better performance in the balance test [23]. Another survey of Spanish older adults aged 65 to 89 found that participants who performed better on the IADL test had higher serum folate concentrations than those who performed worse [24]. In addition, a review summarized the existing evidence that some vitamin deficiencies had a negative impact on the functional recovery of older adults, including folate [40]. These studies provide indirect support for our findings. It is worth mentioning that several studies have found that dietary folate and folate supplement intakes may be partially associated with improved physical function in older adults [20,21], which further supported our findings. Nevertheless, a 3-year cohort study of 698 older adults showed that serum folate was not associated with subsequent SPPB test scores [25]. The inconsistent findings may be due to the differences in the specific items covered by the test for assessing physical function. The mechanisms underlying the link between serum folate and functional disability remained unclear, and there may be the following aspects. Firstly, folate may have beneficial effects on physical function through its antioxidant effect [41,42]. Several studies have shown that folate or folic acid supplementation may attenuate oxidative stress by improving biomarkers in the antioxidative defense system, such as increased serum total antioxidant capacity and glutathione (GSH) concentration [13,43,44,45,46]. Studies have found that oxidative stress is an independent predictor of functional disability [14]. Secondly, the anti-inflammatory effect of folate [47] may also be a mechanism for decreasing the risk of functional disability [48,49]. In addition, folate as a coenzyme of one-carbon metabolism involved homocysteine methylation and promotes homocysteine to methionine conversion [50,51,52]. Several studies have suggested that elevated homocysteine levels may be a risk factor for functional disability [17] or physical function decline [18,53,54]. Specifically, homocysteine may impair physical function through mechanisms such as increasing the concentration of reactive oxygen species and reducing the bioavailability of nitric oxide [55]. In the results of sex stratification, we only found negative associations between serum folate and the risk of functional disability in females. The first possible reason may be that the improvement of serum folate on biomarkers in the antioxidant defense system varies by gender. A meta-analysis found that short-term folate supplementation significantly increased serum GSH concentration in females, but this effect was not observed in males [13]. Second, Larry A. Tucker analyzed data from NHANES and suggested that women with low folate levels were more prone to have telomere shortening and cell aging, but no such association was found in men [56]. An animal experiment showed that transplanting aging cells into young mice could cause continuous physical dysfunction [57]. Another possible reason for the significant gender differences may be as stated above. Third, many studies have shown that homocysteine levels tend to be higher in males than in females [58,59,60]. In addition to folate, homocysteine levels are influenced by other factors, such as sex hormones [61,62]. Finally, sex differences in folate metabolism may also be a possible cause [63]. Additionally, other subgroup analyses found that the associations of higher serum folate with decreased risk of disability in IADL and GPA were mainly in the population under 80 years old, normal weight, and non-drinkers. To our knowledge, several studies have suggested that age and obesity were possible risk factors for functional disability [64,65,66]. Therefore, we speculate that the detrimental effects of age and obesity might counteract the beneficial effects of folate on functional disability. In addition, studies have shown that alcohol consumption may interfere with the absorption and action of folate [67,68], which may partly attenuate the beneficial effects of folate. Further studies are necessary to understand the possible biological mechanisms underlying subgroup differences in this association. It is worth noting that studies have shown that poor diet and poor appetite are the main causes of folate deficiency [69,70], Poor absorption owing to gastrointestinal dysfunction/disease can also result in folate deficiency [71,72]. The physiological process of aging makes older adults more susceptible to these nutritional problems, such as poor taste, loss of appetite, and gastrointestinal malabsorption [73]. Older adults tend to have a higher prevalence of folate deficiency [74]. In addition, poor diet and malabsorption may cause or contribute to the deterioration of physical, intellectual, or mental function and then generate a series of functional impairments and even disabilities [75]. Therefore, we recommend appropriately increasing the intake of folate-rich foods, especially in the older population with poor diet and gastrointestinal dysfunction/disease. The present study has several advantages. First, we conducted the study using NHANES data, which employs a complex sampling design and stringent quality control to obtain representative samples of American residents, lending credibility to our findings. Second, the relationship between 5-MTHF (the primary biological activity form of serum folate) and functional disability was examined in our study for the first time. Third, we evaluated multiple domains of functional disability. In addition, we further explored the dose–response relationships between serum folate concentrations and functional disability. However, we must acknowledge that our research has the following flaws. First, this study was a cross-sectional study, and causality cannot be inferred. Second, although we referred to previous studies [10,35,37] and used 19 well-validated questions of physical function to define functional disability, but functional disability was defined by self-reported questionnaires rather than objective measurements of physical function, which may lead to recall bias and influence the accuracy of participants’ physical function status. While some studies have shown a high correlation between self-reported physical function and objective physical function measures [76,77]. Third, although we evaluated as many different domains of functional disability as possible, there are still some types of disability that are not considered, such as vision loss and hearing loss. Fourth, although we carefully adjusted the potential confounding factors, residual confounding may still exist. In addition, considering that the use of a single indicator, such as serum creatinine, is insufficient to diagnose kidney disease [78], our study did not exclude participants with kidney dysfunction/disease. Since kidney function strongly affected homocysteine levels [79], participants with kidney dysfunction/disease may have influenced the results. Finally, our study focused on older adults aged 60 and over in the US. 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--- title: Lipidomic Analysis of Liver Lipid Droplets after Chronic Alcohol Consumption with and without Betaine Supplementation authors: - Madan Kumar Arumugam - Sathish Kumar Perumal - Karuna Rasineni - Terrence M. Donohue - Natalia A. Osna - Kusum K. Kharbanda journal: Biology year: 2023 pmcid: PMC10045066 doi: 10.3390/biology12030462 license: CC BY 4.0 --- # Lipidomic Analysis of Liver Lipid Droplets after Chronic Alcohol Consumption with and without Betaine Supplementation ## Abstract ### Simple Summary Alcohol-associated liver disease is a major healthcare problem worldwide and is the third leading cause of preventable deaths in the US. Hepatic steatosis is the earliest manifestation of chronic alcohol misuse, characterized by accumulation of specialized fat storing organelles called lipid droplets (LDs). Our previous studies reported that the alcohol-induced increase in the number and size of LDs is attenuated by simultaneous treatment with the methyl group donor, betaine. In this study, we examined alcohol ± betaine-induced changes in the LD lipidome with respect to their size. Untargeted lipidomic analyses of the three different-sized hepatic LD fractions revealed higher phospholipids, cholesteryl esters, diacylglycerols, ceramides, and hexosylceramides in each fraction isolated from livers of ethanol-fed rats compared with the corresponding fractions of pair-fed controls. Betaine supplementation significantly attenuated the ethanol-induced LD lipidomic changes. We conclude that ethanol-induced changes in the hepatic LD lipidome may stabilize larger-sized LDs during steatosis development. Furthermore, betaine supplementation could effectively reduce the size and dynamics of LDs to attenuate alcohol-associated hepatic steatosis. ### Abstract The earliest manifestation of alcohol-associated liver disease is hepatic steatosis, which is characterized by fat accumulation in specialized organelles called lipid droplets (LDs). Our previous studies reported that alcohol consumption elevates the numbers and sizes of LDs in hepatocytes, which is attenuated by simultaneous treatment with the methyl group donor, betaine. Here, we examined changes in the hepatic lipidome with respect to LD size and dynamics in male Wistar rats fed for 6 weeks with control or ethanol-containing liquid diets that were supplemented with or without 10 mg betaine/mL. At the time of sacrifice, three hepatic LD fractions, LD1 (large droplets), LD2 (medium-sized droplets), and LD3 (small droplets) were isolated from each rat. Untargeted lipidomic analyses revealed that each LD fraction of ethanol-fed rats had higher phospholipids, cholesteryl esters, diacylglycerols, ceramides, and hexosylceramides compared with the corresponding fractions of pair-fed controls. Interestingly, the ratio of phosphatidylcholine to phosphatidylethanolamine (the two most abundant phospholipids on the LD surface) was lower in LD1 fraction compared with LD3 fraction, irrespective of treatment; however, this ratio was significantly lower in ethanol LD fractions compared with their respective control fractions. Betaine supplementation significantly attenuated the ethanol-induced lipidomic changes. These were mainly associated with the regulation of LD surface phospholipids, ceramides, and glycerolipid metabolism in different-sized LD fractions. In conclusion, our results show that ethanol-induced changes in the hepatic LD lipidome likely stabilizes larger-sized LDs during steatosis development. Furthermore, betaine supplementation could effectively reduce the size and dynamics of LDs to attenuate alcohol-associated hepatic steatosis. ## 1. Introduction Alcohol-associated liver disease (ALD) is a major healthcare problem and is the third leading cause of preventable deaths in the US [1,2,3,4]. Hepatic steatosis (fatty liver) is the earliest indicator of chronic alcohol misuse. It is characterized by lipid accumulation within specialized cytoplasmic organelles called lipid droplets (LDs) within hepatic parenchymal cells (hepatocytes). The pathogenic mechanism(s) for ethanol-induced hepatic lipid accumulation include enhanced uptake by hepatocytes of circulating fatty acids, accelerated intracellular fatty acid synthesis, and decelerated fatty acid oxidation and export [5,6,7]. Steatosis, the reversible stage of ALD, was once considered a benign condition. It is now regarded as the “first hit” that leaves the liver vulnerable to multiple subsequent hits, that enhance ALD progression to severe, irreversible stages [8]. Due to the close relationship between hepatic steatosis and progressive liver injury, steatosis is a prime target for therapeutic intervention. Therefore, lipidomic was employed here to examine whether it provides an early diagnosis of ALD for the eventual development of treatment options [9,10]. Recent analyses have revealed that chronic ethanol consumption causes a significant decline in hepatic phosphatidylcholine (PC) concomitant with the increase in fatty acids, diacylglycerols (DAG), and lysophosphatidylcholine in conjunction with an increase in unsaturated fatty acyl chains [11,12]. While these previously mentioned studies were all related to total hepatic lipidome, here, our objective was to specifically examine the LD lipidome and to quantify changes in these organelles during ethanol-induced steatosis development [13,14]. A key histopathological feature of steatosis is an increased LD number and size [15]. Based on these considerations, we hypothesized that, after ethanol exposure, the hepatic LD lipidome would vary with the size and dynamics of these lipid storing organelles. Here, we conducted an untargeted lipidomic analysis of LDs using electrospray ionization-tandem mass spectrometry of different-sized LD fractions isolated from livers of rats fed the control or ethanol liquid diet. Our previous studies demonstrated that co-administration of betaine with ethanol prevented the development of alcohol-associated steatosis, as judged by a reduction in LD size and number compared with rats fed ethanol alone [15,16]. Therefore, in this study, our lipidomic analyses also included LD fractions isolated from livers of rats fed the betaine-supplemented ethanol diet. ## 2.1. Animal Experiments Lieber-DeCarli control and ethanol liquid diets were purchased from Dyets Inc. (Bethlehem, PA, USA). Male Wistar rats, weighing from 180 to 200 g, were acquired from Charles River Laboratories, (Wilmington, MA, USA), weight-matched, adapted to liquid diet feeding, then pair-fed the Lieber-DeCarli control, ethanol, or the betaine-supplemented ethanol liquid diet [17] for 6 weeks, as described previously [16]. Briefly, the rats were divided into three groups of eight animals each. Group 1 was fed control diet, group 2 was fed ethanol diet, and group 3 was fed ethanol diet containing $1\%$ (w/v) betaine. Each feeding day, rats in groups 1 and 2 were fed the same volume of diet consumed the previous day by rats in group 3. At the time of sacrifice, liver was excised and immediately processed for histology or for isolation of different-sized LD fractions. The remaining liver tissue was stored at −70 °C for subsequent analysis of total liver triacylglycerol (TAG), cholesterol, S-adenosylmethionine, and S-adenosylhomocysteine levels [15,16]. All animal care, use, and experimental procedures utilized for this study complied with the NIH guidelines and were approved by the Institutional Animal Care and Use Committee of the Omaha Veterans Affairs Medical Center. ## 2.2. Liver Histology Formalin-fixed liver tissue was processed for hematoxylin-eosin staining and evaluated for lipid content [15]. ## 2.3. Separation and Isolation of Different-Sized LDs LDs of varying sizes were isolated from the liver tissues according to the protocol previously described [18,19]. Briefly, the excised liver was rinsed with ice-cold phosphate-buffered saline (PBS) and a 6 g portion was homogenized in 20 mM Tricine, (pH 7.8), containing 250 mM sucrose and 0.5 mM PMSF. Each homogenate was centrifuged at 500× g for 5 min at 4 °C to pellet tissue debris and blood cells. Each supernatant (8.5 mL) was transferred into an ultraclear SW 40 Ti ultracentrifuge tubes (Beckman #344059) and was overlaid with 3 mL of buffer B (20 mM HEPES, (pH 7.4), 100 mM KCl, and 2 mM MgCl2). Following centrifugation at 500× g for 20 min at 4 °C, the top white layer was collected into a 1.5-mL microcentrifuge tube and marked as “large-sized LDs” (LD1). Then, an equal volume of buffer B was added back (to replace the white buffy layer (just removed) and the gradient was recentrifuged at 2000× g for 20 min at 4 °C. The top white band was again collected into a 1.5-mL microcentrifuge tube, which was marked as “medium-sized LDs” (LD2). Then, another equal volume of buffer B was loaded onto the top and the gradient was recentrifuged at 8000× g for 20 min at 4 °C. The top white layer was again collected into a 1.5 mL-microcentrifuge tube, which was marked as smaller-sized LDs (LD3). Finally, each LD fraction collected was further purified by centrifuging it for 10 min at 4 °C at the g force that was originally used to acquire it. The underlying solution and pellet were removed, and the LDs were gently resuspended in buffer B. This final washing step was repeated until no pellet was visible after centrifugation. ## 2.4. Lipid Extraction Total lipids were quantitatively extracted from pre-weighed liver sections and from each LD fraction, using a modified Folch lipid extraction method [20]. TAG and cholesterol levels were biochemically determined in the lipid extract of each liver and each LD fraction, as described [15,16]. ## 2.5. BODIPY Staining of LDs To assess LD size and purity, the different-sized LD fractions isolated from livers of rats fed control, ethanol, or betaine-supplemented ethanol diet were stained with 1,3,5,7-Tetramethyl-8-phenyl-4,4-difluoroboradiazaindacene (BODIPY $\frac{493}{503}$; Invitrogen, Carlsbad, CA, USA), as previously described [21,22]. Briefly, 20 µL of each isolated LD fraction was placed on a slide and stained with 1 µg BODIPY $\frac{493}{503}$ (Invitrogen, Carlsbad, CA, USA). All LD fractions were used undiluted, except for LD1 and LD2 fractions obtained from livers of ethanol-fed rats, which were diluted 1:20 and 1:4, respectively. After staining for 5 min, LDs were visualized under a Keyence BZ-X810 florescence microscope and images were captured. LD size was quantified using Keyence BZ-X810 Analyzer software [22]. ## 2.6. Mass Spectrometry Analysis An automated electrospray ionization mass spectrometry (ESI-MS/MS) approach was used for lipidomic analysis. Data acquisition and analyses were carried out at the Kansas Lipidomics Research Center (Manhattan, KS, USA) as described previously [23,24]. ## 2.7. Statistical Analysis Data were analyzed by one-way ANOVA, followed by Tukey’s post-hoc unpaired test for comparisons among groups. Comparisons with p-values ≤ 0.05 were considered statistically significant. ## 3.1. Body Weights, Liver Weights, and Liver Histology The final body weights and relative liver weights of control and experimental rats are shown (Figure 1A,B, respectively). There were no differences in the final body weights among the three animal groups (Figure 1A). Compared with pair-fed controls, absolute liver weight (in grams) and relative liver weight (in grams/100 g BW) were significantly higher in both ethanol-fed rats and in betaine-supplemented ethanol-fed rats (Figure 1B). The observed hepatic TAG levels (Figure 1C), SAM:SAH ratio (Figure 1D), and liver histopathological evaluations (Figure 1E) were consistent with our previously published results [15,16]. H&E-stained liver sections of ethanol-fed rats exhibited cytoplasmic vacuolization, with micro- and macrovesicular steatosis (Figure 1E). However, some evidence of microvesicular steatosis was evident in livers of rats fed the betaine-supplemented ethanol diet at higher magnification (Figure 1E). ## 3.2. Effect of Ethanol and Betaine Co-Administration on LD Sizes BODIPY $\frac{493}{503}$ is a green lipophilic fluorescent dye that allows microscopic detection of LDs [25]. Quantification of BODIPY-stained LD isolated from livers of rats fed control, ethanol, or betaine-supplemented ethanol diet still revealed some heterogeneity in LD sizes in each LD fraction. We confirmed the sizes of isolated LDs by BODIPY $\frac{493}{503}$ staining (Figure 2). Generally, the LD1 fraction comprised of large-sized LDs (LD1) followed by medium-sized LDs (LD2) and smaller-sized LDs (LD3). LD fractions (LD1-LD3) isolated from rats fed the control or the betaine-supplemented ethanol diet exhibited generally reduced sizes compared with the corresponding fractions of ethanol treated rats as shown in Figure 2. ## 3.3. Effect of Ethanol and Betaine Co-Administration on TAG and Total Cholesterol Levels in LD Fractions LDs store fatty acids in the form of neutral lipids, including TAG and cholesteryl esters [26]. The lipid extracts from each LD fraction were quantified, which revealed significantly higher TAGs and cholesterol in LD fractions of ethanol-fed rats compared with the corresponding fractions of pair-fed controls (Figure 3A,B). Inclusion of betaine in the ethanol diet effectively attenuated the TAG and cholesterol levels in each LD fraction compared with those levels in the fractionated LDs of rats fed ethanol alone (Figure 3A,B). This attenuation of the lipid contents in LDs is consistent with our previously published findings of total TAG in livers of rats fed the betaine-supplemented ethanol diet [15,16]. ## 3.4. Lipidomic Analyses of Different Liver LD Fractions We performed lipidomic analyses on the individual lipid classes of distinct phenotypes in different-sized LDs from our three groups of animals. A total of 21 lipid classes, constituting 1914 different lipid species were identified and analyzed from LD extracts of large, medium, and smaller-sized LDs isolated from livers of rats fed control, ethanol, and betaine-supplemented ethanol diets. This lipidomic approach permitted the quantitative analysis of phospholipids (PL), ceramides (Cer), hexosylceramides (HexCer), cholesteryl esters (CE), diacylglycerol (DAG), TAG, and glycerophospholipids, including PC, phosphatidylethanolamine (PE), phosphatidylserine (PS), and phosphatidylinositol (PI) species, which were characterized by their carbon chain lengths and the numbers of double bonds in their constituent acyl residues. ## 3.4.1. Effect of Ethanol and Betaine Co-Administration on LD1 The level of PI, PS, HexCer, CE, PL, DAG, and TAG lipid species in the larger LDs (LD1) from livers of control, ethanol, and betaine-supplemented ethanol-fed rats are shown in Figure 4. All these lipid species were significantly elevated in LD1 from ethanol-fed rats compared with pair-fed control rats. Betaine co-treatment significantly decreased the aforementioned lipid species (Figure 4A–C). We analyzed the total PI (Figure 4A) and 20 distinct PI variants (Figure 4G) in ethanol LD1 that were higher than those in control LD1. Pl is a major source of arachidonic acid, a precursor of the proinflammatory eicosanoid and an early indicator of steatosis [27,28]. In this study, we found 4.9-fold ($p \leq 0.05$) higher PI (36:3) 1-linoleoyl-2-oleoyl-sn-glycero-3-phosphoinositol and 4.8-fold ($p \leq 0.05$) higher PI (36:2) 1-linoleoyl-2-stearoyl-sn-glycero-3-phosphoinositol in ethanol LD1 compared with control LD1 (Table S1). Alterations in the phospholipid compositions of liver cell membranes occur during ALD pathogenesis [29]. Interestingly, we observed 10.3-fold higher phosphatidylserine (PS) levels in ethanol LD1 ($p \leq 0.001$) compared with control LD1, which was decreased by 2.7-fold with ($p \leq 0.05$) by betaine co-treatment (Figure 4A). Additionally, the heat map generated revealed significantly higher levels of 36 different PS species in LD1 fraction of ethanol-fed rats when compared with control LD1 (Figure 4D; $p \leq 0.05$). Specifically, as shown in Table S2, PS (32:0) 1,2-dihexadecanoyl-sn-glycero-3-phosphoserine was 11.7-fold, PS (34:2) 1-dodecanoyl-2-(13Z,16Z-docosadienoyl)-glycero-3-phosphoserine was 8.8-fold, and PS (38:4) 1-(9Z-octadecenoyl)-2-(8Z,11Z,14Z eicosatrienoyl)-glycero-3-phosphoserine level was 13.2-fold higher in ethanol compared with those from controls ($p \leq 0.05$). This ethanol-induced increase was effectively reduced after betaine co-treatment. Interestingly, compared with the corresponding fractions from livers of ethanol-fed rats, PS (34:4), PS (36:4), PS (40:3), PS (42:8), and PS (44:7) were unaltered in larger-sized LDs by betaine treatment. Similarly, other lipid subpopulations, such as HexCer (18:$\frac{1}{20}$:0) N-(eicosanoyl)-sphing-4-enine and HexCer (18:$\frac{1}{22}$:0) N-(docosanoyl)-sphing-4-enine were elevated by 18.8-fold ($p \leq 0.05$) and 15-fold ($p \leq 0.05$), respectively, in LD1 fractions of ethanol-fed rats compared with those of pair-fed controls. The levels of these HexCer were significantly lowered by betaine co-treatment (Figure 4B,E; Table S3). Cholesteryl esters (CE) are long-chain fatty acids linked to the hydroxyl group of cholesterol. They are significantly less polar molecules than free cholesterol and appear to be the preferred molecular form for transport in plasma and storage in liver [30]. We found 10 distinct CEs that were significantly elevated in ethanol LD1, particularly CE (18:2) cholesteryl linoleate and CE (20:3) cholesteryl eicosadienoic acid by 13.4- and 14.8-fold, respectively, over control LD1. Compared with ethanol LD1, the levels of these CEs were significantly lower in LD1 of betaine co-treated animals (Figure 4F; Table S3). Similarly, 31 different DAGs were significantly elevated in the ethanol LD1 fraction compared with the control LD1 (Figure 4H). Specifically, DAG (18:$\frac{2}{18}$:2) glyceryl 1,2-dilinoleate was 19.8-fold ($p \leq 0.05$) and DAG (18:$\frac{2}{18}$:1) 1-linoleoyl-2-vaccenoyl-sn-glycerol was 19.7-fold ($p \leq 0.01$) higher in ethanol LD1 compared with control LD1. These DAG species were significantly decreased in the LD1 fraction of rats fed the betaine-supplemented ethanol diet (Figure 4H; Table S3). In addition, 12 different TAGs were significantly elevated in the ethanol LD1 fraction compared with the control LD1 (Figure 4I; Table S4). Specifically, TAG (50:2), TAG (50:1), TAG (50:0), TAG (52:6), TAG (52:5), TAG (52:2), TAG (52:1), and TAG (52:0) were higher in ethanol LD1 compared with control LD1. As seen with other lipid classes, betaine co-treatment with ethanol significantly reduced the levels of these TAG species in LD1 fractions compared with the corresponding fraction from ethanol-fed rat livers. ## 3.4.2. Effect of Ethanol and Betaine Co-Administration on LD2 We further investigated the quantitative changes in medium-sized LDs (LD2). Similar to LD1, we found that the lipid species CE, PL, PI, as well as more common phospholipids (RP) were again, significantly higher in LD2 fractions from livers of ethanol-fed rats compared with their pair-fed control rats (Figure 5A,B). However, the majority of other abundant lipid species, including PC, PE, DAG, and TAG were essentially equal in the control and ethanol LD2. The heat map shows the subclass of a total of nine different CE species (Figure 5C), of which five exhibited no changes with ethanol alone or with betaine co-treatment. However, other CEs, such as CE (18:2) cholesteryl linoleic acid, CE figure (18:1) cholesteryl oleate, CE (18:3) 1-g-linolenoyl-cholesterol, CE (16:0) cholesterol palmitate, and CE (18:3) 1-a-linolenoyl-cholesterol were significantly higher in ethanol LD2 fraction compared with control LD2, which were all significantly reduced in the corresponding fraction of rats fed the betaine-supplemented ethanol diet (Table S5). Similarly, other 14 different glycerophospholipids of PI and PS were analyzed as shown in Figure 5D,E, respectively. The fold changes, as shown in Table S6, demonstrate that PI (34:3), PI (34:1), PI (36:5), PI (36:3), PI (36:2), PI (36:1), PI (38:4), PI (38:3), PI (38:2), and PI (40:5) were significantly increased in ethanol LD2 fraction compared with control LD2. Other PI species, such as PI (34:2), PI (40:8), PI (40:4), and PI (40:3) are unaltered between the LD2 of control and experimental groups. Similarly, several PS species were significantly increased in ethanol LD2 compared with control LD2, as shown (Table S7), which were reduced by betaine co-treatment. Other species of PS, such as PS (34:4), PS (38:0), PS (44:5), PS (36:3), and PS (40:4) were unaltered between the LD2 fractions obtained from the different experimental groups. ## 3.4.3. Effect of Ethanol and Betaine Co-Administration on LD3 As performed previously for the other LD fractions, we examined the lipidomes of smaller-sized LDs (LD3) from livers of rats fed control, ethanol, or betaine-supplemented ethanol diet. The majority of the abundant lipid species, such as phosphatidic acid (PA), Cer, HexCer, CE, PL, DAG, TAG, PC, PE, PI, and RP were significantly higher in ethanol LD3 compared with control LD3 (Figure 6A–C). Additionally, the heat map showed the level of glycerophospholipid class, of which a total of 10 different PA species (Figure 6D), such as PA (32:0) 1-arachidoyl-2-lauroyl-sn-phosphatidic acid, PA (34:1) 1-palmitoyl-2-vaccenoyl-sn-glycero-3-phosphate, PA (36:5) 1-stearidonoyl-2-oleoyl-sn-phosphatidic acid, PA (38:2) 1-nervonoyl-2-myristoleoyl-sn-phosphatidic acid, and PA (40:7) 1-meadoyl-2-arachidonoyl-sn-phosphatidic acid were significantly higher in ethanol LD3 compared with control LD3 (Table S8). Other PA species, such as PA (34:4), PA (36:4), PA (36:2), and PA (38:6) were unaltered in these LD3. Ceramides are biologically active sphingolipids that act as critical factors in the pathogenesis of ALD [31] Here, we observed higher levels of three distinct Cer, i.e., Cer (18:$\frac{1}{16}$:0), Cer (18:$\frac{1}{24}$:1), and Cer (18:$\frac{1}{24}$:0) in ethanol LD3 compared with control LD3. After betaine co-treatment, these Cer levels were significantly lower than in LD3s from ethanol-fed rats (Figure 6E; Table S8). One HexCer 18:1 (22:0) (Figure 6E), 14 different CE (Figure 6F; Table S8), 30 different PC (Figure 6G; Table S9), 23 different PE (Figure 6H; Table S10), and 17 different PI (Figure 6I; Table S11) were also increased in ethanol LD3 fractions compared with control LD3 fractions. Rats fed the betaine-supplemented ethanol diet exhibited significantly lower levels of these species in the corresponding fraction from ethanol-fed rats. As shown, the total DAG and TAG were 3.7-fold ($p \leq 0.001$) and 3.8-fold higher ($p \leq 0.05$), respectively, in ethanol LD3 compared with the corresponding fraction of pair-fed controls (Figure 6B). However, while these total DAG and TAG levels were unaltered by the betaine co-treatment, there was a marked decrease in mostly mono- and poly-unsaturated fatty acid species in LD3 fractions. ## 3.5. Analysis of PC:PE Ratio Interestingly, the larger-sized fraction, LD1, had significantly lower PC:PE ratio than the smaller-sized LD3 fraction, irrespective of their treatments (Figure 7). When we compared the ratios between treatments, we observed that the PC:PE ratio was significantly lower (by 1.2-fold) in ethanol LD1 (and LD3 by 2.1-fold) compared with their respective controls, LD1 and LD3 ($p \leq 0.05$). The PC:PE ratio in both the largest LDs (LD1) and smallest LDs (LD3) reverted to near normal levels after the betaine treatment. ## 4. Discussion Hepatic steatosis, the first pathological sign of liver disease in the ALD spectrum, is characterized by enhanced production and slower catabolism of LDs in hepatocytes [32], as well as the expansion of LD size [15,33,34]. Here, we sought to ascertain how chronic ethanol administration alters the liver LD lipidome in Wistar rats and whether these lipidomic changes were prevented or reversed by betaine that was simultaneously co-administered with ethanol. Previous studies reported that there are significant lipidomic changes in livers of ethanol-fed animals [11,35,36]. We recently reported that chronic alcohol consumption increases the number and size of liver LDs [15,32]. Our further studies indicated that the accumulation of large-sized LDs in livers of ethanol-fed rats likely occurs via increased generation of smaller-sized LDs and their subsequent fusion and that the generated larger-sized LDs accumulate due to decreased lipolysis of their lipid stores [22]. The current study was conducted to explore whether changes in the lipid LD profile after chronic alcohol administration is related to the size of the LDs, which, in turn, controls their dynamics, as shown previously [22]. Our other objective was to determine whether betaine treatment prevented these LD lipidome alterations. Here, we extracted three different-sized LDs from livers of control, ethanol, and betaine-supplemented ethanol-fed rats and performed lipidomic analyses. Our untargeted lipidomic analysis revealed that ethanol administration significantly altered several hepatic lipids, including phospholipids and glycerophospholipids in different-sized LDs. Cer and HexCer belong to the group of cerebrosides within the sphingolipids. They are key precursors for the biosynthesis of dihexosylceramides, which are key structural components of intracellular membranes and lipid rafts [37,38]. These ceramides act as a bioactive lipid that can impair insulin signaling, induce oxidant stress, impair fatty acid oxidation, and enhance lipoprotein aggregation, all of which are linked to ALD pathogenesis [39]. Our results are consistent with the results reported earlier, documenting that chronic alcohol consumption significantly elevates specific Cer in the liver, while it reduces Cer plasma levels [35]. We observed higher levels of sphingolipids, such as 2.7-fold increase in Cer C18:1 (24:0), 18.8-fold increase in HexCer -C18:1 (20:0), and 15-fold increase in HexCer C18:1 (22:0) in each fraction of LDs from livers of ethanol-fed rats. Since these sphingolipids are the major source of membranes [40] and larger sized LDs are seen in livers of ethanol-fed rats [15,32], whether this increase may be regulating the LD size or vice versa is not known. There is, however, a definite link between the LD size and this lipid class, since the ethanol-induced increase in sphingolipids was significantly attenuated after the betaine co-treatment, which coincided with a reduction in LD size. Furthermore, the mechanistic link between hepatoprotective effect of betaine and sphingolipid level is not completely clear. Since sphingolipids have cell signaling properties that trigger inflammation, apoptosis, and insulin resistance [41,42], the reduction in their levels by betaine treatment could be related to the attenuation in various insults, (including alcohol)-induced increase in inflammation, apoptosis, and insulin resistance [43,44,45,46]. Studies have reported that chronic alcohol consumption promotes accumulation of neutral lipids in LDs within hepatocytes [11,47,48]. These neutral lipids, such as CE and TAG are uncharged, have no signaling properties, and are often enclosed within LDs [13,49,50]. Increased hepatic accumulation of these neutral lipids is a characteristic feature of alcohol-associated steatosis as shown by other lipidomic studies [35,50,51]. Here, we observed similar increases, with notable elevations in 16- and 18-carbon fatty acids, such as C (16:1), C (18:0), C (18:3) along with eicosatrienoic acid C (20:3) and docosahexaenoic acid C (22:6) in LD fractions of ethanol-fed rats compared with control counterparts. Biochemical analyses corroborated the increase in cholesterol and TAG in each LD fraction from alcohol-fed rats compared with rats fed the control or the betaine-supplemented ethanol diet. Alcohol consumptions drive hepatic TAG synthesis via enhanced de novo fatty acid synthesis and increased uptake of adipose lipolysis-derived circulating fatty acids [52]. These alcohol-induced changes, i.e., increased de novo lipogenesis in hepatocytes and the increase in adipose lipolysis are a consequence of reduced SAM:SAH ratio [21,22]. Another factor for alcohol-induced increased TAG accumulation in hepatocytes is a reduction in very-low density lipoprotein (VLDL) secretion [53], which is a major pathway for exporting fat and preventing hepatic steatosis development. VLDL biogenesis is regulated by the availability of TAG stored in LDs, which must be hydrolyzed to provide a substrate for VLDL assembly and subsequent secretion [54,55,56,57]. Up to $70\%$ of the TAGs packaged and secreted by hepatocytes in VLDLs are derived via lipolysis of LD TAG stores [57]. Indeed, we previously reported that hepatocytes isolated from ethanol-fed rats display a decrease in the rate of lipolysis compared with controls [22,32]. Subsequent studies from our laboratory showed that the reduced lipolysis is due to the lower SAM:SAH ratio, which reduces the activation of important lipases that target LD TAG stores [22]. In this study, several distinct DAG and TAG species were detected in each LD fraction, with significantly increased levels observed in those isolated from livers of alcohol-fed rats compared with controls. We believe that these increased levels result from the reduced hydrolysis of LD TAG stores [32], favoring DAG/TAG accumulation over their catabolism to fatty acids, which must be shuttled to mitochondria (for β-oxidation) or to the endoplasmic reticulum for re-esterification to form the VLDL core. The reduction in TAG/DAG levels after betaine treatment indicates that betaine, by normalizing the SAM:SAH ratio, must promote lipolysis of LD TAG stores, as the alcohol-induced impairment of VLDL synthesis and secretion was restored to normal after this treatment [53]. Phosphatidylcholine (PC) is a critical component of cell membranes and a central player in lipid metabolism [58,59]. PC in the liver is synthesized from choline via the CDP-choline pathway or by methylation of PE via phosphatidylethanolamine methyltransferase (PEMT)-mediated catalysis [60]. Importantly, studies have shown that a decline in the PC:PE ratio correlates with a decreased hepatocyte membrane potential [61]. The latter change in membrane integrity leads to hepatocyte damage or lysis and inflammation, promoting the progression of steatosis to steatohepatitis [61,62]. Limited studies have investigated the phospholipid composition and reported a reduction in PC levels in livers of ethanol-fed rats [11]. Even fewer studies have examined the changes in LDs, despite PC being the most abundant phospholipid (followed by PE) in the LD monolayer, which shields the neutral lipid core from the surrounding cytosol [24,63]. Interestingly, previous studies implicated that the PC:PE ratio regulates the size of LDs, which, in turn, influences the access of lipases to the LD TAG stores [64,65]. Others have also confirmed that the relative abundance of PC and PE regulate the sizes and dynamics of LDs [66]. Indeed, we observed a lower PC:PE ratio in total LDs isolated from livers of ethanol-fed rats compared with controls [32]. We further showed that the reduction in PC:PE ratio can lead to the over-abundance of large LDs through selective recruitment of class II anti-lipolytic LD proteins [32,67]. In line with these observations, here, we observed that larger-sized LDs (LD1) had a much lower PC:PE ratio than the smaller-sized LD3, irrespective of the treatment. Furthermore, this ratio was significantly lower in LD fractions from ethanol-fed rats compared with their pair-fed controls. This altered ratio returned to near normal after betaine treatment. Restoration of the PC:PE ratio in LD sub-fractions by betaine co-treatment coincided with the significant attenuation of ethanol-induced hepatic steatosis. It is possible that these PC:PE ratio changes in LDs are related to changes in the PEMT-catalyzed reaction to generate PC. We have shown that the PEMT-mediated catalysis is modulated by hepatocellular SAM:SAH ratio, and therefore is impaired in livers of ethanol-fed rats and normalized by betaine-supplementation [16,34]. We are currently conducting proteomic studies to determine whether the levels/activity of enzymes of the CDP-choline/PEMT pathway are altered in the different-sized fractions. ## 5. Conclusions In conclusion, our findings indicate that ethanol-induced changes in the LD lipidome, especially changes in the PC:PE ratio, likely stabilizes larger-sized LD fractions during fatty liver development. The increased size of LDs may also be responsible for the reduced lipolysis of the TAG stores, causing their accumulation in the hepatocytes. 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--- title: Comparison of gut viral communities in children under 5 years old and newborns authors: - Hong Li - Hao Wang - Huimin Ju - Jinquan Lv - Shixing Yang - Wen Zhang - Hongyan Lu journal: Virology Journal year: 2023 pmcid: PMC10045071 doi: 10.1186/s12985-023-02013-2 license: CC BY 4.0 --- # Comparison of gut viral communities in children under 5 years old and newborns ## Abstract ### Objectives The gut virome of humans is mainly composed of bacteriophages and their role in shaping the gut microbiome and influencing human health is increasingly recognized. However, little is known about the dynamic changes of the gut virome in children and its role in growth and development. In this study, we collected fecal samples from newborns and children under 5 years old from the same area during the same time period to investigate the gut viral community using viral metagenomic technique. ### Methods We used viral metagenomics to compare the gut bacteriophage composition between newborns and children under 5 years of age. We collected fecal samples from 45 newborns who were born at the Affiliated Hospital of Jiangsu University and 45 healthy children who were examined at the same hospital. The two groups were classified as the newborn group and the children group. ### Results Our sequencing analysis showed that the number of seqeunce reads of the children group were more than that of the newborn group. The results of alpha diversity and beta diversity both indicated that the diversity of the children group was significantly higher than that of the newborn group and the children group is different from the newborn group. The abundance of gut virome in the children group was also higher than that in the newborn group. The analysis of the genetic characteristics of the viruses showed that the phage genome was scattered and clustered with specificity. ### Conclusion Our findings indicate that the gut bacteriophage communities undergo changes over time, presenting diversity and dynamic characteristics. We characterized the composition of gut virome in children and newborns in this region. However, further research is needed to investigate the function of bacteriophages in the ecology of the gastrointestinal tract. ## Introduction According to the estimation of causes of death in children younger than 5 years old and newborn by WHO, pneumonia, diarrhoea, malaria, neonatal pneumonia or sepsis account for more than half of all child deaths, which are caused by infection of bacteria, fungi and viruses[1–5]. Factors such as genetics, diet, environment, and immunity can affect children’s health. Recent research has shown that the intestinal microbiome plays an important role in the growth, development, and health of children. Gut microbiota is associated with many diseases, including childhood growth and development, diabetes, inflammatory bowel disease and obesity [6–9]. Phages can affect the diversity and abundance of intestinal bacteria [10, 11], so they may be involved in host metabolism and immune regulation. Currently, little is known about the role that phages play in child health and the distribution of phages at different ages. The gut of healthy human newborn is usually virus-free, but it can be infected by virus quickly [12]. During the early years of children, the viral microbiome of their guts is less known, including bacteriophages and eukaryotic RNA and DNA viruses [13]. It has been reported that bacteriophages can drive evolutionary changes in bacterial communities by creating gene-flow networks that promote ecological adaptation [14]. The gut bacteriophages exhibit a high degree of diversity and dynamic during the first few months of life and gradually decrease over time [15]. One study showed that the gut microbiomes of twins were more similar than those of unrelated individuals [16]. Therefore, the gut microbiome is also associated with a variety of factors. Ultimately, the distribution of intestinal phages varies according to the individual’s health [17]. Mammalian gut bacteriophages mainly include double-stranded DNA (dsDNA) belonging to Caudovirales, represented by the Siphoviridae, Podoviridae and Myoviridae families and single-stranded DNA (ssDNA) belonging to Microviridae family [18, 19]. In the current study, we analysed the gut bacteriophage communities in children under 5 years old and newborn by using viral metagenomics to understand the diversity and dynamics of bacteriophages, and map the distribution of intestinal phages in a part of healthy children. ## Sample collection and preparation To investigate the gut bacteriophage composition between newborn and children under 5 years of age, 90 fecal specimens were respectively collected from 45 newborn who were born in the Affiliated Hospital of Jiangsu University (Zhenjiang City, Jiangsu Province, China)and 45 healthy children who were examined at the same hospital from Sep. to Dec. 2018. All samples were collected with 1.5ml disposable sterile tubes and transported to the laboratory via dry ice, and then stored in the refrigerator at -80℃. The specimens were collected with the written consent of the guardian. Each fecal specimen was added ten volumes of Dulbecco’s Phosphate Buffered Saline (DPBS). Each specimen was vigorously vortexed for 5 min and it was repeated for three times. The supernatants were collected after centrifugation (10 min, 15,000 × g), then the specimens were stored in the refrigerator at -80℃ until the viral metagenomic analysis was performed. Sample collection and all experiments in the present study were performed with Ethical Approval given by Ethics Committee of Jiangsu University and the reference number is No. UJS2018030. ## Viral metagenomic analysis Prepared supernatants from 45 newborn and 45 children were respectively pooled into sample pools according to the age group, 90 sample supernatants were pooled into 10 pools with 9-sample each. Supernatant pools were filtered through a 0.45 μm filter (Millipore) to remove eukaryotic- and bacterial cell-sized particles, and 200 µL of supernatant from each pool was then subjected to a mixture of nuclease enzymes to reduce the concentration of free (non-viral encapsidated) nucleic acids. The filtrates enriched in viral particles were treated with DNase and RNase to digest unprotected nucleic acid at 37℃ for 90 min [20, 21]. Total viral nucleic acids were extracted according to the procedures of QIAamp Viral MinElute Virus Spin Kit (Qiagen). Then viral nucleic acid was reversed into double-stranded DNA using SuperScript III Reverse transcriptase kit (Invitgen) and Klenow Enzyme (NEB) according to the manufacture’s protocols. 10 libraries were then constructed using Nextera XT DNA Sample Preparation Kit (Illumina) and sequenced using the MiSeq Illumina platform with 250 bp paired ends with dual barcoding for each pool [22]. ## Bioinformatics analysis For bioinformatics analysis, paired-end reads of 250 bp generated by MiSeq were debarcoded using vendor software from Illumina. An in-house analysis pipeline running on a 32-nodes Linux cluster was used to process the data. Clonal reads were removed and low sequencing quality tails were trimmed using Phred quality score 20 as the threshold. Adaptors were trimmed using the default parameters of VecScreen which is NCBI BLASTn with specialized parameters designed for adapter removal. The cleaned reads were de novo assembled by SOAPdenovo2 version r240 using Kmer size 63 with default settings. The assembled contigs along with singlets were matched to an in-house viral proteome database using BLASTx with an E-value cutoff of < 10− 5 [23, 24]. These BLASTx results generated by DIAMOND (DAA format) were used to generate rma6 format files by MEGAN6 software, which can be further used for subsequent analysis including species accumulation curve, and Co-occurrence plot [25]. ## Phylogenetic analysis Phylogenetic analysis was performed based on predicted viral amino acid sequences and their closest viral relatives on the best BLASTp hits in GenBank and representative members of related viral species or genera. CLUSTAL X (version 2.1) was used to perform the sequence alignment with default settings and phylogenetic trees were generated by Bayesian Inference (BI) in MrBayes 3.2 [26]. ## Statistical analysis To compare differences in viral diversity between groups, statistical analyses were performed using MEGAN6 and R version 4.0.3 Alpha diversity and beta diversity were performed using the vegan package and Wilcoxon tests was used for two-group comparisons, respectively. There was considered statistically significant when $p \leq 0.05.$ The Comparison of virus alpha diversity was demonstrated by Shannon index analysis. The Beta diversity analysis based on Bray-Curtis included Analysis of similarities, Principal coordinate analysis and the Unweighted Pair-group Method with Arithmetic averages [27]. ## Overview of sequencing data In the study, to facilitate the comparison of gut bacteriophage communities in children under 5 years of age and newborn, the study cohort were divided into children and newborn groups. A total of 3,462,741 raw reads were obtained from these 10 libraries including 5 children pools and 5 newborn pools based on next generation sequencing. 55,741 reads were related to bacteriophages according to the de novo assembled and compared with the GenBank non-redundant protein database through BLASTx. The number of sequence reads of the children group were more than that of the newborn group. Detailed information of sequence read numbers were displayed in the Table 1. Sequence de novo assembly generated 26 complete genomic of the family Microviridae and with sequence length ranges from 4458 to 7718. The other incomplete sequences with complete open reading frame (ORF) of the major capsid proteins were also obtained. Table 1Raw reads and bacteriophages-related reads of each libraryLibrary No. Raw readsBacteriophages related readsLibrary No. Raw readsBacteriophages related readsChildren01536,3579617Newborn01124,266232Children02291,56513,991Newborn02138,6713438Children031,051,18513,822Newborn03199,3782230Children04517,7934145Newborn04237,7044637Children05300,5052598Newborn0565,3171031 ## Comparison of gut bacteriophage communities In the family level, alpha diversity was used to show the difference of gut bacteriophage community composition between the children and the newborn group where the Shannon index indicated that the diversity of the children group was significantly higher than that of the newborn group ($$p \leq 0.045$$, Wilcoxon test) (Fig. 1). Based on beta diversity, unweighted UniFrac analysis suggested that principal coordinate analysis (PCoA) (Fig. 2a) and hierarchical clustering can distinguish the children group from the newborn group (Fig. 2b). Fig. 1Alpha diversity analysis. Comparison of virus alpha diversity (Shannon index) between the children group and the newborn group Fig. 2Beta diversity analysis. ( a) Principal coordinate analysis (PCoA) scatter plot. Circles represent the $95\%$ normal probability ellipse for each group. The pink one was on behalf of the children group and the blue one was on behalf of the newborn group. ( b) The hierarchical clustering tree based on Bray–Curtis and built with the Unweighted Pair-group Method with Arithmetic averages (UPGMA). A great difference of the gut bacteriophage communities between the children group and newborn group were observed. Coetzeevirus was detected in the newborn group, but not in the children group. In the children group, the number of viral reads such as family Microviridae, Siphoviridae and Myoviridae were more than those in the newborn group, where family Microviridae was detected in all children libraries and Siphoviridae was abundant in all children groups except the children02 (Fig. 3). Fig. 3Comparison of gut bacteriophage communities between the children group and the newborn group. Clustering heatmap of representative viruses from the 10 libraries. The bottom of the figure represents the library number. The red bar at the top of the figure represents the newborn group and the blue bar represents the children group. The right of the figure is the name of representative viruses. The number of reads is logarithmically converted with log10 as the base which is shown in the upper right corner ## Phylogenetic analysis of main gut bacteriophages detected in the two groups To further assess the diversity of the gut bacteriophage communities of the children and newborn groups, phylogenetic analyses were performed based on the signature amino acid sequences of the main gut bacteriophages acquired in this study and reference sequences from GenBank, including the major capsid protein for Microviridae and phage terminase large subunit (TerL) for Siphoviridae and Myoviridae, respectively. In this study, amino acid sequences of the major capsid protein of the family Microviridae in the children group were used to construct the phylogenetic tree and assess the microviral diversity. The result indicated that most of these sequences were clustered divergently and formed several branches (Fig. 4). Fig. 4Phylogenetic analysis was performed on the amino acid sequences of the major capsid protein of the family Microviridae in the children group. The identified sequences in this study were labeled in red The family Siphoviridae and Myoviridae belongs to the dsDNA bacteriophage with a conserved region known as terminase large subunit (TerL). A phylogenetic tree was constructed based on the TerL sequences of the family Siphoviridae and Myoviridae identified in both children and newborn groups. The topological structure of the tree suggested that most of these sequences were too divergent to be classified into known family of Siphoviridae and Myoviridae and several new clades were formed (Fig. 5), suggesting putative novel phage families present in the gut of children and newborns. The number of hallmark gene sequences assembled from children group is much more than that assembled from the newborn group, which is consisted with the above results that gut phage diversity in the children is higher than that in the newborn. Our data also indicated that sequences of the family Siphoviridae from the newborn group were clustered with sequences from the children group (Fig. 5), implying phage communities from gut of children and newborns share the common species of siphovirus. Fig. 5Phylogenetic analysis was performed on the amino acid sequences of the family Siphoviridae and Myoviridae in the children group and the newborn group. The viruses found in the children group was labeled in red and the newborn group was labeled in pink. Reference sequences and corresponding viruses are labeled with consistent colors ## Discussion Viral metagenomics is a widely used tool to discover new viruses, map viral genomes, and investigate viral diversity. In the past decade, with the rapid development of sequencing technology and bioinformatics, metagenomics has enabled detailed exploration of the infant gut microbiome, including its diversity in various pathological conditions such as childhood developmental delays and food allergies [6, 28]. In this study, we utilized viral metagenomics to analyze the gut bacteriophage communities in children under 5 years old and newborns, aiming to understand the diversity and dynamics of bacteriophages. Our sequencing analysis revealed that the children group had more reads than the newborn group. Furthermore, both α-diversity and β-diversity analyses showed that the intestinal phage diversity in children was significantly higher than that in newborns. This may be due to the fact that newborns only consume milk or breast milk, and are in a protected environment that does not yet establish a stable intestinal phage community. In our hierarchical clustering analysis, we detected Coetzeevirus in the library of the newborn group but not in the library of the children group, which may be related to early neonatal exposure. Coetzeevirus belongs to the Lactobacillus phage group [29], and our survey indicated that newborns in China are frequently given Bifidobacterium, which could explain the high abundance of Coetzeevirus in the newborn group. In contrast, Yvonnevirus, Lentarirus, Brussowvirus, Capybara microvirus, and Leviviridae were detected in the children05 library, which could be associated with DNA amplification bias or different best matches in BLASTx (E-value cutoff of < 10− 5). Comparing the two groups, the children group had more viral reads from families such as Microviridae, Siphoviridae, and Myoviridae than the newborn group. A previous study on intestinal phages in healthy adults found mainly Microviridae, Myoviridae, Siphoviridae, and Unassigned at the family level [30]. In our complete genome analysis, we obtained 26 complete genomes of Microviridae, suggesting that Microviridae is abundant in children. However, we lack comprehensive longitudinal observation for the two groups of subjects, so there is no specific timeline for the children group to form the current distribution state of gut virome. Myoviridae and Siphoviridae are members of the Caudovirales family, sharing a central tape measure protein surrounded by a tail tube and ending with a terminator protein. [ 31, 32]. The phylogenetic analyses performed on the major capsid protein of the family Microviridae and phage terminase large subunit (TerL) of the family Siphoviridae and Myoviridae indicated that most of these sequences detected in this study were clustered divergently and formed several branches. Studies have shown that the composition of adult gut virome appears to be highly specific and stable. Most phages appear unique to everyone [16, 33, 34]. The variation could have led to this result and could also explain the scattered clustering of the sequences detected in our experiment. Clearly, there are some limitations to our experiment. First, the sample size of this study is insufficient, resulting in incomplete results. However, it also provides a certain value for the study of gut virome in newborn and children. Second, the geographical area of sample collection is too narrow. China is a large country with abundant resources, and there are great differences in climate and dietary habits among different regions, which also leads to great differences in the distribution of environmental virus species in different regions. These factors may lead to some different gut virome in children, but this needs to be confirmed by follow-up experiments. Third, compared with the study of intestinal bacterial spectrum, there are relatively few studies on gut virome, which also leads to the lack of data in relevant databases at present, so that our data analysis is limited to a certain extent. ## Conclusions It is evident that our experiment has some limitations. Firstly, the sample size of this study is insufficient, resulting in incomplete findings. However, it does offer some value for exploring the gut virome in newborns and children. Secondly, the geographic area where we collected samples is too limited. China is a vast country with diverse resources, and there are significant variations in climate and dietary habits across different regions, resulting in different distributions of environmental virus species. These factors could lead to distinct gut viromes in children, but further research is needed to confirm this. 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--- title: Strontium Ranelate Inhibits Osteoclastogenesis through NF-κB-Pathway-Dependent Autophagy authors: - Dongle Wu - Xuan Sun - Yiwei Zhao - Yuanbo Liu - Ziqi Gan - Zhen Zhang - Xin Chen - Yang Cao journal: Bioengineering year: 2023 pmcid: PMC10045081 doi: 10.3390/bioengineering10030365 license: CC BY 4.0 --- # Strontium Ranelate Inhibits Osteoclastogenesis through NF-κB-Pathway-Dependent Autophagy ## Abstract Strontium ranelate (SR) is a pharmaceutical agent used for the prevention and treatment of osteoporosis and fragility fracture. However, little attention has been paid to the effect of SR on alveolar bone remodeling during orthodontic tooth movement and its underlying mechanism. Here, we investigated the influence of SR on orthodontic tooth movement and tooth resorption in Sprague–Dawley rats and the relationship between the nuclear factor–kappa B (NF-κB) pathway, autophagy, and osteoclastogenesis after the administration of SR in vitro and in vivo. In this study, it was found that SR reduced the expression of autophagy-related proteins at the pressure side of the first molars during orthodontic tooth movement. Similarly, the expression of these autophagy-related proteins and the size and number of autophagosomes were downregulated by SR in vitro. The results also showed that SR reduced the number of osteoclasts and suppressed orthodontic tooth movement and root resorption in rats, which could be partially restored using rapamycin, an autophagy inducer. Autophagy was attenuated after pre-osteoclasts were treated with Bay 11-7082, an NF-κB pathway inhibitor, while SR reduced the expression of the proteins central to the NF-κB pathway. Collectively, this study revealed that SR might suppress osteoclastogenesis through NF-κB-pathway-dependent autophagy, resulting in the inhibition of orthodontic tooth movement and root resorption in rats, which might offer a new insight into the treatment of malocclusion and bone metabolic diseases. ## 1. Introduction Nowadays, an increasing number of people, especially female adults in high-income countries, such as Germany, are turning to orthodontic treatment for oral health and aesthetic appearance [1,2]. However, it has been found clinically that the orthodontic tooth movement of patients who have taken anti-osteoporosis drugs, such as bisphosphonate and strontium, seems to slow down compared to others. Strontium ranelate (SR) is a potent pharmaceutical agent used for the prevention and treatment of osteoporosis and fragility fracture. It has a unique dual mechanism in bone tissue that promotes bone formation and inhibits bone resorption simultaneously [3]. Specifically, it can promote osteoblastogenesis and the mineralization of the bone matrix, while inhibiting the migration, accumulation, differentiation, and resorption ability of osteoclasts, so as to reduce the risk of fracture and increase bone strength [4,5]. In the field of stomatology, SR has been proven to improve dental implant osseointegration, attenuate root resorption during orthodontic tooth movement, and reduce alveolar bone resorption in rats with periodontitis [6,7,8]. Nevertheless, little attention has been paid to the effect of SR on orthodontic tooth movement, and its specific mechanism of action has not been fully elucidated, which needs to be further investigated. Previous studies have shown that SR can inhibit the proliferation and differentiation of osteoclasts via the nuclear factor–kappa B (NF-κB) pathway [9,10]. This transcription factor family functions as a critical regulatory factor in inflammation, immune response, and bone metabolism [11,12,13]. In the classic NF-κB pathway, p65 and p50 are released from their trimer after the p-inhibitor of kappa B kinase β (p-IKKβ) phosphorylates the inhibitor of kappa B α (IκBα). Next, p65 and p50 transfer into the nucleus and activate the transcription of downstream genes, resulting in the enhancement of osteoblastogenesis and the inhibition of osteoclastogenesis [11]. Accumulating evidence indicates that SR suppresses tumor-necrosis-factor-α-induced NF-κB activation in pre-osteoblasts and nuclear translocation of NF-κB regulated by the receptor activator of nuclear factor-kappa B ligand (RANKL) in pre-osteoclasts, which promotes osteoblast differentiation and attenuates osteoclast differentiation eventually [10,14]. Meanwhile, the NF-κB pathway has been proven to play an important role in autophagy, and the strong link between autophagy and osteoclastogenesis is being increasingly appreciated [15]. Autophagy, a highly conserved catabolic process in eukaryotes, is responsible for phagocytosing and degrading damaged organelles or aged proteins via the lysosomal pathway. After cytoplasmic substances, such as macromolecules, organelles, and exogenous pathogens, are phagocytosed into autophagic bodies, they fuse with lysosomes to form autolysosomes that degrade and recycle the contents of autophagic bodies, providing energy and nutrition for the repair, survival, and maintenance of cells [16]. A number of studies have revealed that autophagy has different effects on the proliferation, differentiation, and activity of bone cells, including osteocytes, osteoblasts, and osteoclasts [17,18,19]. The dysfunction of autophagy may cause abnormality of bone homeostasis, thus resulting in various diseases associated with bone metabolism [20]. Furthermore, it has been demonstrated that the NF-κB pathway can directly enhance autophagy by inducing the expression of autophagy-related proteins, such as autophagy-related 5 homolog (ATG5), microtubule-associated protein light chain 3 (LC3), and Beclin1. These proteins are reported to be highly involved in osteoclast activity, including the formation of a ruffled border, the transport of lysosomes, and the release of protease in vitro and in vivo [21,22,23,24]. Nevertheless, the role of the NF-κB pathway and autophagy in the mechanism of osteoclastogenesis inhibited by SR has not been studied yet. This study aimed to determine the effect of SR on the efficiency of orthodontic tooth movement so as to provide a prediction of the therapy time of orthodontic patients who have taken SR. In addition, the pharmaceutical mechanism of SR was further elucidated in this study, which might provide evidence for the treatment of bone metabolic diseases. ## 2.1. Ethical Approval This study was conducted with approval from the Institutional Animal Care and Use Committee (IACUC), Sun Yat-sen University (no. SYSU-IACUC-2021-000015). ## 2.2. Osteoclast Culture After bone marrow cells were harvested from the femur and tibia of 4-week-old Sprague–Dawley rats, they were lysed with red blood cell lysis (Cwbio, Taizhou, China) on ice, centrifuged, resuspended in high-glucose Dulbecco’s modified Eagle medium (Gibco, Grand Island, NE, USA) containing $10\%$ fetal bovine serum (Gibco), and cultured in a $5\%$ CO2 incubator at 37 °C for 2 days. The rat bone-marrow-derived macrophages in the culture medium were collected, centrifuged, and resuspended in complete medium containing 30 ng/mL of macrophage-colony-stimulating factor (MCSF; PeproTech, Cranbury, NJ, USA) and then cultured in an incubator at 37 °C for 3 days to induce pre-osteoclasts. The pre-osteoclasts were divided into three groups and resuspended in a medium containing 30 ng/mL of MCSF and 50 ng/mL of RANKL (R & D Systems, Minneapolis, MN, USA). At the same time, the SR group also received 2 mM SR (Maclin, Shanghai, China), while the SR + rapamycin (RAPA; TargetMol, Boston, MA, USA) group received 2 mM SR and 0.1 nM RAPA, an autophagic inducer. After culture in an incubator at 37 °C for 5 days, osteoclasts were collected for tartrate-resistant acid phosphatase (TRAP) staining, monodansylcadaverine staining, Western blot analysis, and transmission electron microscope observation. ## 2.3. In Vitro TRAP Staining TRAP is a specific enzyme produced in osteoclasts, which can be used to identify these multinucleated giant cells. After the culture medium was removed, the cells were fixed with $4\%$ paraformaldehyde (Servicebio, Wuhan, China) for 30 min, cultured with TRAP dye solution (Sigma-Aldrich, Darmstadt, Germany) at 37 °C for 30 min in the dark, and counted under a light microscope (Axio; Zeiss, Oberkochen, Germany). ## 2.4. Western Blot After the cells were lysed with radio immunoprecipitation assay (RIPA) lysis buffer (CST, Boston, MA, USA) on ice for 30 min, the concentration of protein was calculated with the bicinchoninic acid protein assay kit (Beyotime, Shanghai, China). RIPA lysis buffer and sodium dodecyl sulfate–polyacrylamide (Beyotime) were used to dilute the proteins to obtain an equal concentration. Next, they were denatured in boiling water for 10 min; loaded in $12\%$ MOPS electrophoretic gel (GenScript, Piscataway, NJ, USA) in the same amount; transferred onto a polyvinylidene fluoride membrane (Millipore, Burlington, MA, USA); blocked with $5\%$ bovine serum albumin (Servicebio); incubated with the first antibody at 4 °C overnight, including tumor-necrosis-factor-receptor-associated factor 6 (TRAF6; 1:500; Servicebio), c-Fos (1:1000; Servicebio), matrix metalloproteinases-9 (MMP-9; 1:1000; CST), matrix metalloproteinase-14 (MMP-14; 1:500; Zen, Shanghai, China), cathepsin K (CTSK; 1:500; Servicebio), Beclin1 (1:500; Boster, Wuhan, China), LC3 (1:1000; CST), lysosomal-associated membrane protein 2 (LAMP2; 1:500; Zen), ATG5 (1:500; Zen), p62 (1:500; Boster), p-IKKα/β (1:500; Affinity, Shanghai, China), IκBα (1:500; Zen), and p65 (1:500; Zen); incubated with horseradish peroxidase (HRP)-labeled goat anti-rabbit IgG (1:500; Servicebio) at 25 °C for an hour; and finally visualized with a chemiluminescent substrate (Millipore) using a chemiluminescence imaging system (ChemiDoc; Bio-Rad, Hercules, CA, USA). ## 2.5. Monodansylcadaverine Staining Monodansylcadaverine is a fluorescent stain usually used to detect the formation of autophagosomes. After the induction of osteoclastogenesis, these cells were cultured with $10\%$ monodansylcadaverine (Leagene, Beijing, China) at 37 °C for 50 min in the dark and then observed under a laser confocal microscope (LSM980; Zeiss). ## 2.6. Transmission Electron Microscopy After the culture medium was removed, the osteoclasts were separated with trypsin (Gibco) for 10 min, centrifuged for 2 min, and immersed in $2.5\%$ glutaraldehyde (Servicebio) for 30 min. Next, they were embedded with $1\%$ agarose solution (Servicebio); fixed with $1\%$ osmic acid (TPI, Reading, PA, USA) for 2 h in the dark; dehydrated with $30\%$, $50\%$, $70\%$, $80\%$, $95\%$, and $100\%$ ethanol (Servicebio) and $100\%$ acetone (Servicebio); embedded with acetone and 812 (SPI, Shanghai, China) at 37 °C overnight; polymerized at 60 °C for 48 h; sectioned into 60-nm-thick slides using an ultra-microtome (UC7, Wetzlar, Germany, Leica); stained with $2\%$ uranium acetate (Servicebio) and $2.6\%$ lead citrate (Servicebio) for 8 min each; and observed under a transmission electron microscope (HT7800; Hitachi, Tokyo, Japan). ## 2.7. Animal Maintenance Forty-five 10-week-old male Sprague–Dawley rats (Sun Yat-sen University, Guangzhou, China) were used in this experiment. These rats weighed between 200 g and 220 g and were provided with a semi-liquid diet all day under specific pathogen-free conditions. They were randomly divided into 3 groups of 15 rats each: control group, SR group, and SR + RAPA group. ## 2.8. Orthodontic Tooth Movement Model After the induction of general anesthesia with $3\%$ pentobarbital sodium (30 mg/kg; CVRI, Shanghai, China), surgeries were performed on the maxillary left bones of the rats in all 3 groups. An orthodontic ligature wire made of stainless steel (Xinya, Hangzhou, China) was inserted through the undercut between the first and the second maxillary left molar. The wire was ligated with an orthodontic nitinol spring (Xinya), and another ligature wire was fastened on the other side of the spring. After the spring was extended with a dynamometer (Tiantian, Hangzhou, China) until the force reached 0.3 N, the wire on the other side was ligated with the incisors. Chemically cured resin was used to further fasten the ligature of the wire and the labial surfaces of the incisors after the surfaces were acid-etched (Heraeus Kulzer GmbH, Germany), washed, and dried and a primer applied to them (3M Unitek, Saint Paul, MN, USA) [25]. ## 2.9. Drug Treatment in Rats Drug administration began 3 weeks before the establishment of the orthodontic tooth movement models and lasted until the rats were sacrificed in all groups. The rats in the SR group were administered SR dissolved in 1.5 mL of physiological saline at a dosage of 900 mg/kg through gastric tubes every day. The rats in the SR + RAPA group received 900 mg/kg of SR and 300 μg/kg of RAPA, and the rats in the control group received only an equal amount of physiological saline using the same method. ## 2.10. Sample Collection and Treatment Five rats from each group were euthanized using general anesthesia on days 3, 7, and 14. After the maxillary left alveolar bones of the rats were removed, they were scanned with micro-CT (voltage: 85 kV, current: 200 μA, resolution ratio: 10 μm; SkyScan 1276; Bruker, Karlsruhe, Germany) for analysis of orthodontic tooth movement, tooth resorption, and trabecular structure. Next, the bones were fixed with $4\%$ paraformaldehyde (Servicebio) for 24 h; decalcified with ethylene diamine tetraacetic acid (EDTA; Servicebio) for several weeks; dehydrated with $75\%$, $85\%$, $90\%$, $95\%$, and $100\%$ ethanol (Servicebio), alcohol benzene (Servicebio), and xylene (Servicebio) in an automatic tissue processor (Donatello; DIAPATH, Shanghai, China); and embedded with molten paraffin (Servicebio) in an embedding machine (JB-P5; Junjie, Wuhan, China). After cooling and trimmed, these wax blocks were sectioned into 4-μm-thick slides along the sagittal direction with a pathology slicer (RM2016; Leica), which were then used for hematoxylin-eosin (HE) staining, TRAP staining, immunohistochemistry, and immunofluorescence. ## 2.11. Analysis of Data Obtained with Micro-CT Micro-CT data were analyzed using CTAn 1.20 (Bruker), a high-resolution microscopic CT quantitative analysis software [26,27]. Orthodontic tooth movement was calculated by measuring the distance between the protruding point of the first molar distal wall and the second molar mesial wall. The volume of root resorption at the pressure side of the first molars was calculated with CTAn 1.20, and 3D images of resorption were reconstructed with CTVox 3.3 (Bruker), a 3D CT reconstruction software. The basic micro-architectural parameters of the bone trabecula at the pressure side of the first molars were used to analyze the changes in the trabecular structure, including the bone volume/total volume, trabecular number, trabecular thickness, and trabecular separation. ## 2.12. Hematoxylin-Eosin Staining Dewaxed sections were incubated with hematoxylin (Servicebio) and eosin (Servicebio); dehydrated with xylene, anhydrous alcohol, and $75\%$ ethanol; and sealed with neutral gum (Servicebio). The cytoplasm and nucleus of all the cells turned to red and blue, respectively. The slices were scanned (Aperio AT2, Leica), and osteoclasts and tooth resorption lacuna were observed at the pressure side of the upper-left first molars. ## 2.13. In Vivo Tartrate-Resistant Acid Phosphatase Staining After the dewaxed sections were incubated with distilled water at 37 °C for 2 h and TRAP incubation solution at 37 °C for 20 min, they were stained with hematoxylin; dehydrated with xylene, anhydrous alcohol, and $75\%$ ethanol; and finally sealed with neutral gum. The cytoplasm and nucleus of TRAP-positive cells turned wine red and light blue, respectively. The slices were scanned (Aperio AT2, Leica), and TRAP-positive multinucleated cells were observed and counted at the pressure side of the upper-left first molars. ## 2.14. Immunohistochemistry The dewaxed sections were immersed in EDTA antigen retrieval buffer (Servicebio), heated in a microwave (P70D20TL-P4, Glanze, Hangzhou, China) at sub-boiling temperature for 15 min for antigen retrieval, incubated with $3\%$ hydrogen peroxide (Servicebio) at 25 °C for 25 min to block endogenous peroxidase, and mixed with $3\%$ bovine serum albumin (Servicebio) at 25 °C for 30 min in the dark to be blocked by serum. After that, the slices were incubated with a primary antibody at 4 °C overnight, including RANK (1:100; Zen), osteoprotegerin (OPG; 1:200; Servicebio), TRAF6 (1:100; Servicebio), nuclear factor of activated T cells 2 (NFATc2; 1:500; Servicebio), c-Fos (1:400; Servicebio), MMP-14 (1:100; Zen), CTSK (1:1000; Servicebio), p-IKK α/β (1:100; Affinity), IκBα (1:100; Zen), p65 (1:200; Zen), Beclin1 (1:200; Boster), LC3 (1:500; CST), LAMP2 (1:100; Zen), ATG5 (1:50; Zen), and p62 (1:200; Boster). Next, they were incubated with HRP-labeled goat anti-rabbit IgG (1:200; Servicebio) at 25 °C for 50 min; mixed with diaminobenzidine (DAB; Servicebio) solution for coloration; counterstained with hematoxylin; dehydrated with xylene, anhydrous alcohol, and $75\%$ ethanol; and finally sealed with neutral gum. They were scanned (Aperio AT2; Leica), and the positive expression appeared brownish yellow. The mean optical density (MOD) was measured using Image-Pro Plus 6.0 (Media Cybernetics, Rockville, MD, USA) to represent the signal intensity of the positive expression. ## 2.15. Immunofluorescence All procedures were conducted in the dark according to a previous study [24]. For single labeling of immunofluorescence, after antigen retrieval, endogenous peroxidase blocking, and serum blocking, as described before, the dewaxed sections were incubated with a primary antibody at 4 °C overnight, including Beclin1 (1:100; Boster), LC3 (1:100; CST), LAMP2 (1:100; Zen), ATG5 (1:50; Zen), and p62 (1:300; Boster), and then incubated with Cy3-labeled goat anti-rabbit IgG (1:300; Servicebio) at 25 °C for 50 min. For triple labeling of immunofluorescence, after antigen retrieval and serum blocking, as described before, the dewaxed sections were incubated with p-IKKα/β (1:1000; Affinity) at 4 °C overnight, HRP-labeled goat anti-rabbit IgG (1:300; Servicebio) at 25 °C for 50 min, and Cy3-TSA solution (1:300; Servicebio) at 25 °C for 10 min; immersed in EDTA antigen retrieval buffer (Servicebio); and heated in a microwave (P70D20TL-P4; Glanze) at sub-boiling temperature for 15 min. Next, the slices were incubated with IκBα (1:3000; Zen) at 4 °C overnight, HRP-labeled goat anti-rabbit IgG at 25 °C for 50 min, and 488-TSA solution (1:500; Servicebio) at 25 °C for 50 min and heated in the microwave, as described before. After that, they were incubated with p65 (1:200; Zen) at 4 °C overnight and Cy5-labeled goat anti-mouse IgG (1:400; Servicebio) at 25 °C for 50 min. Finally, the slices for single labeling and triple labeling were counterstained with DAPI solution (Servicebio) at 25 °C for 10 min, mixed with spontaneous fluorescence quenching reagent (Servicebio) at 25 °C for 5 min, sealed with anti-fade mounting medium (Servicebio), and scanned (3Dhistech, Pannoramic, Budapest, Hungary) to obtain high-resolution images. The mean fluorescence intensity (MFI) of the positive expression was measured using Image-Pro Plus 6.0 (Media Cybernetics). ## 2.16. Statistics All experiments were repeated three times, and quantitative results were presented as the mean ± standard deviation (SD). All data were obtained by two examiners who were blinded to the groups, and analyzed with one-way analysis of variance (ANOVA) followed by the Bonferroni test in SPSS 23.0 (IBM, Chicago, IL, USA). $p \leq 0.05$ was considered statistically significant. ## 3.1. Strontium Ranelate Inhibited Osteoclastogenesis through Autophagy After pre-osteoclasts were treated with SR for 5 days, TRAP staining showed that the nucleus number and the number and size of multinucleated giant cells decreased in comparison with the control group (Figure 1a and Figure S1 in Supplementary Materials). As shown in Figure 1d, the expression levels of TRAF6, c-Fos, MMP-9, MMP-14, and CTSK, the osteoclast markers, which were detected with Western blot analysis, reduced in the SR group. Monodansylcadaverine staining (Figure 1b and Figure S2 in Supplementary Materials) showed that the MFI of the autophagosome, including the autophagic body with a two-layer membrane and the autolysosome with a one-layer membrane, decreased after the addition of SR, and transmission electron microscope observation (Figure 1c and Figure S3 in Supplementary Materials) exhibited that the size and number of autophagosomes reduced significantly in comparison with the control group. It was found that the expression levels of Beclin1, ATG5, LAMP2, and LC3-II/LC3-I in the SR group were lower than those in the control group, while the expression level of p62, a regulator and substrate of autophagy, increased in the SR group compared with the control group, according to the analysis of Western blot (Figure 1e). Furthermore, monodansylcadaverine staining (Figure 1b and Figure S2 in Supplementary Materials) and transmission electron microscope observation (Figure 1c and Figure S3 in Supplementary Materials) showed that RAPA, a macrolide antibiotic that activates the process of autophagy, restored in part the MFI and the size and number of autophagosomes attenuated by SR. The expression levels of osteoclast markers (Figure 1d), including TRAF6, c-Fos, MMP-9, MMP-14, and CTSK, and autophagy-related proteins (Figure 1e), such as Beclin1, ATG5, LAMP2, LC3-II/LC3-I, and p62, were partially restored after the pre-osteoclasts were treated with RAPA in comparison with the SR group. These results suggested that RAPA might enhance the proliferation, differentiation, and function of osteoclasts suppressed by SR, indicating that SR might inhibit osteoclastogenesis through autophagy. ## 3.2. Strontium Ranelate Inhibited Autophagy through the NF-κB Pathway There is much evidence demonstrating that the NF-κB pathway plays an essential role in osteoclastogenesis suppressed by SR and has relevance to the process of autophagy, so we next investigated whether SR inhibits autophagy via this pathway. TRAP staining showed that Bay 11-7082, a suppressor of the NF-κB pathway, attenuated the nucleus number and the number and size of osteoclasts (Figure 2a and Figure S4 in Supplementary Materials). The size and number of autophagosomes decreased due to the inhibitor, according to the analysis of monodansylcadaverine staining (Figure 2b and Figure S5 in Supplementary Materials) and transmission electron microscope observation (Figure 2c and Figure S6 in Supplementary Materials). This inhibitor also reduced the expression levels of the osteoclast marker CTSK and the proteins central to the NF-κB pathway, including p-IKKα/β, IκBα, and p65 (Figure 2d). As shown in Figure 2e, Bay 11-7082 downregulated the expression levels of autophagy-related proteins, including Beclin1, ATG5, LAMP2, and LC3-II/LC3-I, and enhanced the expression level of p62. Furthermore, after osteoclast precursors were treated with SR for 5 days, Western blot analysis showed that the expression levels of p-IKKα/β, IκBα, and p65 significantly reduced compared to the control group (Figure 2f). These results revealed that SR might attenuate autophagy through the NF-κB pathway, which further decreases the proliferation and differentiation of osteoclasts. ## 3.3. Strontium Ranelate Reduced Orthodontic Tooth Movement and Root Resorption in Rats through Autophagy Five rats from each group were sacrificed on days 3, 7, and 14, and their maxillary left alveolar bones were removed and then scanned with micro-CT. As shown in Figure 3a, the first molars moved faster from day 0 to day 7 than from day 7 to day 14 in all groups. The administration of SR significantly slowed down the velocity of tooth movement compared to the control group (Figure 3a,d). It was found that the thickness and bone volume/total volume of trabecular bone at the pressure side of the first molars increased, while the structure model index and trabecular space reduced after the administration of SR, according to trabecular structural analysis, indicating that SR enhances the micro-architecture of trabecular bone (Figure 3f). HE staining (Figure 3c) and micro-CT (Figure 3b,e) showed that the size and volume of root resorption at the pressure side of the first molars were significantly attenuated in the SR group. In addition, RAPA partially restored the effect of SR, including the velocity of tooth movement (Figure 3a,d), the micro-architecture of trabecular bone (Figure 3f), and the extent of root resorption (Figure 3b,c,e). ## 3.4. Strontium Ranelate Might Inhibit Osteoclastogenesis through NF-κB-Mediated Autophagy in Sprague–Dawley Rats TRAP-staining showed that the number of osteoclasts decreased after the administration of SR at the pressure side of the first molars. Immunohistochemistry analysis showed that osteoclast markers, including RANK, TRAF6, NFATc2, c-Fos, MMP-14, and CTSK, had lower positive expression levels, while OPG, a negative regulator of osteoclast differentiation, had a higher positive expression level after the rats were treated with SR in comparison with the control group (Figure 4a,b). SR also downregulated the MOD and MFI of autophagy-related proteins, including Beclin1, ATG5, LAMP2, and LC3, while it enhanced the MOD and MFI of p62 at the pressure side of the first molars, according to the analysis of immunohistochemistry (Figure 5a,b) and immunofluorescence (Figure 5c and Figure S7 in Supplementary Materials). In addition, RAPA partially restored the number of osteoclasts (Figure 4a,b); the MOD of the osteoclast markers (Figure 4a,b), including RANK, TRAF6, NFATc2, c-Fos, MMP-14, and CTSK; and the MOD (Figure 5a,b) and MFI (Figure 5c and Figure S7 in Supplementary Materials) of the autophagy-related proteins, such as Beclin1, ATG5, LAMP2, LC3, and p62, at the pressure side of the first molars. The MOD (Figure 6a,b) and MFI (Figure 6c,d) of the proteins central to the NF-κB pathway, including p-IKKα/β, IκBα, and p65, reduced after the administration of SR, according to the analysis of immunohistochemistry and immunofluorescence. Immunofluorescence assay also showed that the transport of p65 from the cytoplasm to the cell nucleus was downregulated in the SR group (Figure 6c). These findings indicated that SR might suppress osteoclast differentiation at the pressure side of the first molars in rats through the activation of NF-κB-pathway-dependent autophagy, leading to the inhibition of orthodontic tooth movement and root resorption (Figure 7). ## 4. Discussion Under many physiological and pathological conditions, autophagy is initiated to protect cells for repair, survival, and maintenance [28]. In the process of orthodontic tooth movement, the periodontal ligament at the pressure side and the blood vessels inside it are compressed. These changes result in narrowed periodontal space and reduced blood flow, which puts the pre-osteoclasts at the pressure side under an anoxic state [29]. Under hypoxia stress, osteoclast precursors may initiate autophagy to protect them for survival and prevent them from undergoing apoptosis [30]. Autophagy is initiated through a compound containing ATG13 and then nucleated by a Beclin1 compound to form an autophagosome [31]. During the process of elongation, LC3-I in the cytoplasm is converted to LC3-II by ATG5 and other autophagic proteins [32]. Previous studies have shown that the expression levels of ATG5, Beclin1, and LC3 increase, while the expression level of p62, the substrate of autophagy, reduces at the pressure side of the first molars in adult male mice during orthodontic tooth movement. These results indicate that autophagy is indeed activated during the process [30,33]. In this study, an orthodontic tooth movement model was established in Sprague–Dawley rats. It was found that the administration of SR downregulated the expression levels of ATG5, Beclin1, LAMP2 (a specific marker in the lysosome membrane), and LC3-II/LC3-I and upregulated the expression level of p62 in vitro and in vivo. SR also attenuated the number and size of autolysosomes in osteoclasts in vitro, according to the analysis of monodansylcadaverine staining and transmission electron microscope observation, which revealed that the autophagy of osteoclasts at the pressure side of orthodontic tooth movement is inhibited under the influence of SR [34]. Furthermore, the differentiation and function of osteoclasts have been reported to be positively correlated with their autophagic activity. A number of studies have demonstrated that the deficiency of these autophagy-related proteins could inhibit osteoclastogenesis [19,22,35]. It was also found that sphingosine-1-phosphate (S1P), a lipid signaling molecular that promotes cell growth and inhibits apoptosis, regulates not only the mammalian target of RAPA (mTOR) pathway but also the migration of osteoclasts. These studies have suggested that S1P is a bond between autophagy and osteoclast accumulation [36]. There is evidence revealing that ATG5, LC3, and other autophagic proteins have a significant influence on the resorption ability of osteoclasts, including the formation of a ruffled border, the fusion of lysosomes and the plasma membrane, and the secretion of proteases, such as members of the MMP family and CTSK. These results indicate that autophagy could enhance the bone resorption ability of osteoclasts [21,22,37]. In this study, the administration of SR inhibited the differentiation and function of osteoclasts at the pressure side of the first molars. It was found that the nucleus number and the number and size of the red multinucleated giant cells significantly decreased in vitro and in vivo, according to the analysis of TRAP staining, a specific marker enzyme of osteoclasts. Various osteoclast markers reduced in the SR group, including RANK, TRAF6, NFATc2, c-Fos, and some proteases related to bone resorption, such as MMP-9, MMP-14, and CTSK. These proteases could degrade extracellular matrix protein, including collagen and elastin, break intercellular connections, and promote cell development and migration [15,35,38,39,40] In addition, orthodontic tooth movement at the pressure side of the first molars was inhibited in the SR group, according to the analysis of 3D reconstruction with micro-CT. The inhibition of tooth movement might result from the fact that SR significantly enhances the micro-architecture of trabecular bone by attenuating osteoclast differentiation. Although SR slowed down the process of orthodontic treatment, it decreased the extent of root resorption during the tooth movement, which suggests that topical SR might be a potent pharmaceutical agent for the prevention and treatment of root resorption. However, these effects of SR could be partially reversed by RAPA, an autophagy activator that inhibits mTOR complex 1. These results indicated that SR might suppress proliferation, differentiation and function of osteoclast through autophagy, leading to the inhibition of orthodontic tooth movement and root resorption. However, several lines of evidence suggest that autophagy plays a negative role in osteoclastogenesis [41,42]. Likewise, it has also been found that autophagy has a dual effect on the differentiation and activity of osteocytes and osteoblasts during bone remodeling [18,43,44,45] As osteocytes and osteoblasts can influence the development, function, and survival of osteoclasts through the RANK/RANKL/OPG pathway, it becomes difficult when assessing the role of autophagy in osteoclastogenesis, resulting in the contradictory conclusions in previous studies [46,47,48]. It was found that autophagy regulates osteoclastogenesis in a dose-dependent manner. The result showed that the proliferation, differentiation, and function of osteoclasts are enhanced at a low autophagic level, while they are attenuated with an increasing autophagic level [49]. Therefore, we suspect that different levels of autophagy might have contradictory effects on osteocytes, osteoblasts, and osteoclasts under different states of disease or external stimuli in vivo and in vitro. In this study, the results showed that autophagy is positively correlated with the proliferation and differentiation of osteoclasts at the pressure side of the first molars during orthodontic tooth movement, indicating that the effect of autophagy might lead to osteoclastogenesis under this circumstance. Nevertheless, the exact mechanism of autophagy in osteoclasts is still unclear and needs to be further elucidated. It has been demonstrated that the NF-κB pathway plays an essential part in the development, function, and survival of osteoclasts inhibited by SR and is related to the process of autophagy, which suggests that the NF-κB pathway might be a critical link between SR and autophagy [9,10,15]. In this study, SR decreased the expression levels of the proteins central to the NF-κB pathway, including p-IKKα/β, IκBα, and p65, in vivo and in vitro. It might function through the RANK/RANKL/OPG axis, a classical pathway of osteoclastogenesis, that antagonizes the NF-κB pathway, according to previous studies [15]. Bay 11-7082, an inhibitor of the NF-κB pathway, downregulated the autophagy-related proteins except p62 and decreased the size and number of autophagosomes, based on the analysis of monodansylcadaverine staining and transmission electron microscope observation. Similarly, there is evidence that IKK/NF-κB directly enhances autophagy by inducing the expression of related proteins such as ATG5, LC3, and Beclin1 [23,50]. These results demonstrate that SR attenuates osteoclastogenesis by inhibiting autophagy through the NF-κB pathway. There are several limitations to this study. First, we concentrated on only the effect of SR on osteoclasts instead of osteoblasts, which also play an essential part in bone remodeling. Second, SR has some severe side effects, such as thromboembolism, but most of them can be avoided if physicians strictly follow the indications of SR [51]. It was reported that physicians strictly control the indications of SR, such as severe osteoporosis with a high absolute fracture risk, significantly reducing the occurrence of these side effects [52]. Last, the exact mechanisms of how the NF-κB pathway regulates autophagy and how autophagy affects osteoclastogenesis are still unclear and need to be further elucidated. While an increasing number of people are turning to orthodontic treatment, including some osteoporotic patients who have taken SR, little attention has been paid to the effect of SR on orthodontic tooth movement and its underlying mechanism. In this study, it was found that SR could suppress osteoclastogenesis by inhibiting autophagy through the NF-κB pathway, leading to the inhibition of orthodontic tooth movement and root resorption in Sprague–Dawley rats. Therefore, orthodontists should be aware that patients who are taking SR will have a longer course of treatment and need to be informed of their condition in advance. Topical administration of SR might be a more promising and practical mode of administration in orthodontic treatment to prevent orthodontic side effects, such as anchor loss and tooth resorption, and thus avoid the use of inconvenient extraoral anchorage and invasive surgery. 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--- title: Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction authors: - Katarína Grešová - Ondřej Vaculík - Panagiotis Alexiou journal: Biology year: 2023 pmcid: PMC10045089 doi: 10.3390/biology12030369 license: CC BY 4.0 --- # Using Attribution Sequence Alignment to Interpret Deep Learning Models for miRNA Binding Site Prediction ## Abstract ### Simple Summary MicroRNAs are small non-coding RNAs that play a central role in many molecular processes, but the exact rules of their activity are not known. In recent years, deep learning computational methods have revolutionized many fields, including the microRNA field. While making accurate predictions is important in biomedical tasks, it is equally important to understand why models make their predictions. Here, we present a novel interpretation technique for deep learning models that produces human readable visual representation of the knowledge learned by the model. This representation is useful for understanding the model’s decisions and can be used as a proxy for the further interpretation of biological concepts learned by the deep learning model. Importantly, the presented method is not tied to the model or biological domain and can be easily extended. ### Abstract MicroRNAs (miRNAs) are small non-coding RNAs that play a central role in the post-transcriptional regulation of biological processes. miRNAs regulate transcripts through direct binding involving the Argonaute protein family. The exact rules of binding are not known, and several in silico miRNA target prediction methods have been developed to date. Deep learning has recently revolutionized miRNA target prediction. However, the higher predictive power comes with a decreased ability to interpret increasingly complex models. Here, we present a novel interpretation technique, called attribution sequence alignment, for miRNA target site prediction models that can interpret such deep learning models on a two-dimensional representation of miRNA and putative target sequence. Our method produces a human readable visual representation of miRNA:target interactions and can be used as a proxy for the further interpretation of biological concepts learned by the neural network. We demonstrate applications of this method in the clustering of experimental data into binding classes, as well as using the method to narrow down predicted miRNA binding sites on long transcript sequences. Importantly, the presented method works with any neural network model trained on a two-dimensional representation of interactions and can be easily extended to further domains such as protein–protein interactions. ## 1. Introduction MicroRNAs (miRNAs), first discovered in Caenorhabditis elegans in 1993 [1,2], are an abundant class of small (~17–25 nt long) non-coding RNAs that regulate gene expression at the post-transcriptional level [3,4,5,6]. Mature miRNAs are loaded into the Argonaute (AGO) protein, and along with other proteins, form the miRNA-induced silencing complex (miRISC). miRNAs guide the miRISC, through partial base pairing, to target messenger RNAs (mRNAs) [7,8]. Such targeting may lead to translational repression and deadenylation-induced mRNA degradation [9,10]. Several studies have revealed miRNAs involvement in not only normal physiological processes but also pathologies [11,12]. The abnormal expression or function of miRNAs has been closely related to diverse human diseases, such as cancers. miRNAs are thus emerging as novel endogenous bio-targets for diagnostics and therapeutic treatments [13,14]. Understanding miRNA-involved cellular processes, including a clear picture of regulatory networks of intracellular miRNAs, is, therefore, essential and critical for miRNA-targeted biomedicine [15,16]. The 5′ end of the miRNA, and especially the hexamer spanning nucleotides 2–8, were very early identified as important for miRNA target recognition and termed the “seed” region [17]. Target recognition is primarily achieved via base pairing that involves the seed region [18]; however, seed pairing is not always sufficient for functional target interactions, and additional interactions with the miRNA 3′ end may be necessary for specific targeting [19]. Several experimental methods for identifying miRNA:target site pairs interactions have been developed, discovering abundant classes of non-seed interactions [20,21,22]. Experimental validation of functional miRNA:target pairs is a laborious process and computational tools can be utilized to simplify it. The first programs for the computational prediction of miRNA targets started to appear in 2003, shortly after it was suggested that miRNAs are widespread and abundant in cells [4,5,6]. Each mRNA can contain dozens of potential miRNA binding sites [23] and target prediction programs identify these binding sites and combine them into the final prediction on the level of the whole gene. Two main approaches for binding site identification are the “cofold” and the “seed” heuristics [24]. The “cofold” heuristic computes the hybridization energy of miRNA and the binding site sequences [25,26,27]. It also produces a base pairing pattern of two input sequences, providing a way to visualize the miRNA:binding site interaction. However, this computation does not take into account the AGO protein affecting the interaction, resulting in poor predictive power [28]. The “seed” heuristic uses the relaxed seed region to scan the target for potential binding sites. This approach outperforms the “cofold” heuristic [28], but it misses non-seed interactions, amplifying the seed bias. It also lacks the base pairing visualization feature. Advances in experimental identification of miRNA binding sites [20,29] have enabled the rise of computational methods based on machine learning (ML) and especially deep learning (DL). DL methods are currently state-of-the-art in the field and are highly appropriate for uncovering the miRNA binding rules, where clear rules or features are unknown since they work with the raw data and compute the features themselves [28,30]. Despite the high accuracy of DL models, these models have several disadvantages that hinder their usability and interpretability. DL models trained for miRNA target site prediction often work with the fixed input length, giving the prediction score for the whole input sequence, even though it is known that miRNAs are only approximately 17–25nt long, and their target sites potentially even shorter. DL models are also infamous for being unable to directly interpret what they learn from the data. While making accurate predictions is important in biomedical tasks, it is equally important to understand the reason why models make their predictions. Although DL models are not designed to highlight interpretable relationships in data or to guide the formulation of mechanistic hypotheses, they can, nevertheless, be interrogated for these purposes a posteriori [31]. The lack of direct interpretability of DL models may keep them back from being widely used in the context of miRNA target site prediction, compared to less powerful but easily interpretable models, such as the seed measure. In complex models, it is imperative to inspect parameters indirectly by probing the input–output relationships for each predicted example. Attribution scores, also called feature importance scores, relevance scores, or contribution scores, can be used for this purpose. They highlight the parts of a given input that are most influential for the model prediction and thereby help to explain why such a prediction was made. Techniques for obtaining the attribution scores can be divided into two main groups on the basis of whether they are computed using input perturbations or using backpropagation. Perturbation-based approaches [32,33,34] systematically change the input features and observe the difference in the output. For DNA sequence-based models, the induced perturbation can be, for example, a single-nucleotide substitution [33,35,36,37,38] or the insertion of a regulatory motif [39,40]. Backpropagation-based approaches [41,42,43,44,45,46,47] propagate an important signal from an output neuron backward through the layers to the input in one pass. This makes them more efficient than perturbation methods. While DL models are only as good as the data they were trained on, the interpretation technique is constrained by the used representation of data. The field of miRNA targeting is generally not interested in the specific sequence, but rather in the interactions between two sequences, namely the miRNA and target RNA. For the interpretation technique to point to the important interaction, this information has to be encoded in the data. In this paper, we propose a novel interpretation technique for the miRNA target prediction models working with the 2D-binding representation of input sequences. The 2D-binding representation encodes interactions between sequences, allowing the interpretation technique to work in the context of interactions, not sequences. This interpretation provides an understandable visualization of the miRNA:target site interaction in the form of base pairing with the importance scores for each position. It can be further used as a proxy for studying the biological concepts learned by the neural network. We present several applications, such as identifying classes of miRNA binding activities (including seed and non-seed binding) and enhancing the target site predictions by narrowing it to the length of miRNA. All the code and data are available at https://github.com/katarinagresova/DeepExperiment. ## 2.1. Datasets and Models MiRNA:target site interaction datasets introduced in Klimentova et al., 2022 [28] were retrieved from the GitHub repository (https://github.com/ML-Bioinfo-CEITEC/miRBind, date accessed: 9 December 2022). Positive miRNA:target interactions originates from the Helwak et al., 2013 CLASH experiment [29]. Klimentova et al., 2022 standardized the length of miRNA sequences to 20 nt, anchored by the 5′ end of the miRNA. The length of target sequences was standardized to 50 nt by centering and either clipping the sequence or extending it using the hyb reference [48]. These processed miRNA:target pairs were called the positive dataset. As explained in Klimentova et al., 2022, the negative set was constructed by matching real target sequences with random miRNAs from the same experiment excluding the miRNA:target pairs from the positive set. The trained models introduced in Klimentova et al., 2022 [28], namely miRBind and CNN_model_1_20_optimized, were downloaded from the GitHub repository (https://github.com/ML-Bioinfo-CEITEC/miRBind, Date accessed: 9 December 2022). Authors used a modified version of ResNet [49] as a miRBind architecture and a convolutional neural network architecture [50] for the CNN_model_1_20_optimized model. Both models use a two-dimensional representation of miRNA and the putative target site, in which any Watson–Crick binding nucleotide pair is represented by 1, and any non-binding pair by 0, as an input. For the miRNA of length 20 nt and target site of length 50 nt, the result is a 50 × 20 two-dimensional matrix of 1 s and 0 s (Figure 1a). ## 2.2. Attribution Scores Attribution scores were computed using two implementations of the SHAP explanation method [47]—DeepExplainer and GradientExplainer—both available in the shap python package (https://github.com/slundberg/shap, date accessed: 9 December 2022). DeepExplainer implementation builds on a connection with DeepLIFT [45], while GradientExplainer builds on ideas from Integrated Gradients [44] and SmoothGrad [51]. Attribution scores highlight areas within the input that contribute positively or negatively to a model’s decision. The SHAP explanation method is based on principles of the Shapley value. The Shapley value [52] is a widely used method for explaining the outputs of a model and understanding the relationship between the features of the data and the model’s predictions. By assuming that each feature is a “player” in a game where the prediction is the “payout”, the Shapley value provides a fair way to distribute the payout among the features. In this paper, we utilized the SHAP explanation method [47] that computes Shapley values with one innovation: the Shapley value explanation is represented as an additive feature attribution method, a linear model. The SHAP explanation method requires a model, the data sample, and a set of background samples as input parameters. In this study, we selected 100 background samples to be optimal in terms of computational time and variation in importance scores (Figure S1). The variation in importance scores was used to measure the variation in results between models using different random background samples. We computed variation and computation time for numbers of background samples of 10, 50 and then adding 50 background samples up to 500. Each point was averaged over 10 runs. The output of the SHAP method is a matrix with the same shape as the input data sample. In this study, we used samples in the format of a 50 × 20 2D matrix of 1s and 0s (as proposed by Klimentová et al., 2022 [28], Figure 1a); therefore, the output is a 50 × 20 matrix of SHAP values for each pixel in the input sample (Figure 1b). For each class, the input pixel with assigned positive SHAP value increases the model’s probability to classify the input as a given class and the negative value decreases the probability. As for the case of data from Klimentová et al., 2022 with only two classes, attribution scores for the positive and negative class differ only in the sign, pixel, with a high positive value for one class and a highly negative value for the other class. ## 2.3. Attribution Sequence Alignment The attribution scores obtained from the SHAP explanation method produce the map of areas in the input that are positively and negatively contributing to the model’s decision. However, it is hard to see some biological features in this representation. Attribution sequence alignment is based in the principles of dynamic programming for the semi-global sequence alignment computed on top of attribution scores, transforming the attribution scores into the human readable visual representation of miRNA:target interactions. The computation of attribution sequence alignment is based on two steps: [1] forward pass, where the dynamic programming matrix is filled (Algorithm 1), and [2] backward pass, where sequence alignment is computed by finding the highest-scoring path in the dynamic programming matrix. Parameters for the forward pass are the scoring matrix and opening and elongation penalty. The attribution scores for a given input (computed using the method described in Section 2.2.) are used as a scoring matrix. The opening and elongation penalty score is computed for each alignment separately, based on the values in the scoring matrix. The opening penalty is set to the 99th percentile score and elongation penalty to the 90th percentile score. This setting is highly incentivizing mismatches over insertions or deletions and longer bulges over shorter ones. The backward pass is computed the same way as in the original algorithm by Smith and Waterman [53]. Algorithm 1. Algorithm for computing the dynamic programming matrix for modified semi-global sequence alignment. Input:gene and miRNA sequences of length M and N, respectively; scoring matrix of shape MxN; opening and elongation penalty score. Output:Dynamic programming matrix DP.1. Initialization:2. reverse the order of gene and miRNA to match the scoring matrix3. remove negative scores from the score matrix4. swap sign of scores for the mismatch positions in the scoring matrix5. add the first row and column of zeros to the scoring matrix6. initialize the first row and column of the DP matrix with zeros7. Dynamic programming:8. iflast row or column then9. penalty = 010. else ifis opening gap then11. penalty = opening_penalty12. else13. penalty = elonging_penalty14. end if15. fori: 1 to M do16. forj: 1 to N do17. DPi,j = max(DPi,j−1 − penalty, DPi−1,j−1 + score_matrixi,j, Pi−1,j − penalty)18. end for19. end for The outputs of the attribution sequence alignment algorithm are three sequences with the same length: [1] aligned miRNA sequence, [2] aligned binding site sequence, and [3] sequence of attribution scores for each position in the alignment. The first two sequences are obtained from the backward pass of the dynamic programming matrix and are describing the interaction base by base. The third part of the output is obtained from the interpretation matrix and describes the importance of each position for the interaction. For each aligned base pair, the corresponding score is taken from the interpretation matrix, and for the “deletion” or “insertion”, the score is set to zero. These outputs can be used to produce a biologically relevant representation of the interaction between the miRNA and the binding site, as captured by the model. ## 2.4. Importance Scores for miR-7 and miR-278 Binding In vivo experimental mutagenesis data were extracted from Figure 1 from Brennecke et al., 2005 [54]. There are two mRNA:target site pairs with the length of 23 and 22 nt, respectively. We used the first 20 nt of miRNA sequences (starting from the 5′) and the whole target site sequences. Relative reporter activity values for mismatched positions were manually extracted from Figure 1c from Brennecke et al., 2005 and are shown in Supplementary Table S1 These data contain values for positions 1 to 10 and one aggregated value for the 3′ end. Importance scores for miR-7 and miR-278 binding sites were computed using the miRBind model, the Deep SHAP interpretation method with 100 background samples, and attribution sequence alignment. We computed importance scores in 10 runs with different background samples, demonstrating the variability of the output. The importance scores starting from position 11 were averaged into one importance score representing the aggregated value for the 3′ end. In silico mutagenesis (ISM) is a common interpretation technique from the group of perturbation-based approaches [35,36,39,55,56,57]. ISM is an alternate feature attribution approach that involves making systematic mutations to characters in an input sequence and computing the change in the model’s output due to each mutation. It is the computational analog of saturation mutagenesis experiments [58] that are commonly used to estimate the functional importance of each character in a sequence of interest based on its effect size of mutations at each position on some functional read-out, making it a good candidate for obtaining position importance scores for miR-7 and miR-278 binding sites. We conducted two versions of the ISM interpretation, termed here ISM Full and ISM Brennecke. In ISM Full, we systematically mutated each nucleotide in the input miRNA, changing it to three other possible nucleotides, and observed the model’s output. We also computed the model’s prediction for the original miRNA sequence and used it as a base value from which we subtracted the average of the model’s outputs for mutated inputs, resulting in an importance score for a given position. In ISM Brennecke, we performed only the mutations as described in the Figure 1 from Brennecke et al., 2005 and we used changes in the model’s outputs as importance scores. ## 2.5. Narrowing Peaks Artificial data with planted seeds were constructed by inserting a seed sequence into a background gene. A background gene was created by generating a random RNA sequence in which all four bases occurred with equal probability. The first miRNA from the Klimentova et al., 2022 evaluation dataset was selected and the 10 nt seed region starting at the second position was extracted. We calculated the reverse complement of the extracted seed sequence and planted it into specified positions in the gene to create this artificial data. Artificial data with stitched binding sites were constructed from the binding site from the Klimentova et al., 2022 evaluation dataset. We selected the most abundant miRNA sequence and its positive and negative target sequences. The artificial target gene sequence was obtained by combining the positive and negative binding site of a given miRNA. To obtain the model’s output peaks, we used the miRBind model to scan the gene sequence using a 50 nt window with a step size of 1 nt. For each position, we transformed the 50 nt gene window sequence and the miRNA sequence into a 2D-binding matrix and fed it through the miRBind model. The obtained score was added to the overall score for all positions in the current window. After computation, the overall score was normalized in each position by the number of output scores that were added to that position. To obtain peaks using the interpretation of the miRBind model, we scanned the gene sequence in the same manner as in the previous method. For each position, we computed the model’s output score and, if the score was higher than 0.5, we interpreted the model at that position using DeepExplainer, obtaining an attribution matrix with a size of 50 times length of miRNA. Each position in the attribution matrix was scaled by the model’s output and added to the corresponding position in the overall attribution matrix. The overall attribution matrix had a size of the length of the gene times the length of the miRNA. To identify peaks from this matrix, for each position in the gene, we took the maximum value in the corresponding column. To compute the alignment of miRNA with its binding site, we first smoothed the maximum score obtained from the overall attribution matrix and identified the local maxima. The window of size 50 nt around the local maxima was extracted from the gene sequence and the overall attribution matrix. The attribution sequence alignment method was used to compute the alignment and per-nucleotide importance scores in the selected window. ## 2.6. Comparing Models and Interpretation Methods To compare position importance scores computed with our attribution sequence alignment method on attribution scores produced with a different interpretation method on different models, we utilized two models published by Klimentová et al., 2022—miRBind and CNN_model_1_20_optimized—and two interpretation methods available in the shap package—DeepExplainer and GradientExplainer. We computed position importance scores for the following pairs: miRBind model and DeepExplainer interpretation method, miRBind model and GradientExplainer method and CNN model and GradientExplainer interpretation method. The pair with the CNN model and DeepExplainer interpretation method was omitted due to the implementation problem of DeepExplainer method in the shap package. We used 300 true positive samples from the evaluation set published by Klimentová et al., 2022 as our input data. We computed three sets of position importance scores using our attribution sequence alignment method on attribution scores produced with a different interpretation method on different models. To compare the results, we computed the Pearson correlation coefficient on the pairs of position importance scores. We also randomly shuffled a set of position importance scores in each pair and computed the Pearson correlation coefficient on these new pair. The P-value was calculated using the Wilcoxon rank sum test on correlation coefficients from original and shuffled samples. ## 3.1. Using Attribution Scores to Interpret DL Models of miRNA:target Prediction The main aim of the presented method is the interpretation of DL models which work on 2D base pairing representations of miRNA:target site interactions (Figure 1a). Previously, we have shown that such models outperform traditional “seed” or “cofold” approaches [28]. Given as the input to such a trained model on 2D miRNA:target data, we use DeepExplainer [47] to calculate attribution scores for each potential interaction on the 2D matrix (Figure 1b). We use principles of dynamic programming to calculate an optimal path through the binding and attribution matrices, which is in turn used to align the two sequences in a way informed by the attribution scores (Figure 1c). This alignment is interpreting what the trained model has learned, which takes into account several factors such as the interaction between the miRNA, the target site, and the AGO protein. Traditional “cofold” methods lack this information, and although they can produce a similar alignment, their predictive value is lower than that of the DL models [28]. In turn, this attribution sequence alignment is used to cluster putative binding sites into categories based on their predicted mode of binding (Figure 1c). ## 3.2. Attribution Scores Closely Correlate to In Vivo Experimental Data The interpretation method proposed here can be used to produce per-nucleotide importance scores to miRNA sequences within a miRNA:target site interaction. Brennecke et al., 2005 [54] performed an in vivo experiment, in which they systematically introduced single-nucleotide changes in a miRNA target site in order to produce mismatches at different positions of the miRNA:target site duplex. They then observed changes in the repression of the target gene for two miRNA:target site pairs in Drosophila (Figure 2). They reported that mutating specific single nucleotides conferred strong reduction in the ability of the miRNA to regulate its target. For mir-7, positions 2 to 8 were identified as most important, and for miR-278, positions 2–7 from the miRNA 5′ end. We used as the input the miRBind model, which has been trained on Human AGO1 CLASH data, and we implemented three different interpretation methods (a) our attribution sequence alignment, (b) ISM Brennecke and (c) ISM Full (see Methods for details). We computed the importance of each position on the miRNA for the same two miRNA:target pairs as in Brennecke et al., 2005. Importance scores from our attribution sequence alignment were largely consistent with Brennecke et al. ’s in vivo assay results (Figure 2). Notably, we see that the diminished importance of nucleotide 1 and the 3′ end are correctly interpreted using our method, corresponding to the experimental result. The interpretation via our method is only as good as the DL model used as input. Any similarities or discrepancies to the experimental data, represent what the DL model has learned about the AGO:miRNA:target interaction. Using our method, we can better evaluate the consistency of any DL model to this ground truth. To compare the three interpretation methods, we computed the Pearson correlation coefficient between the experimental results and the importance scores calculated with each method based on the same DL model. Table 1 shows that results produced by our method positively correlate with the experimental results, while results computed by any of the in silico mutagenesis (ISM) methods correlate less positively, or even negatively. ## 3.3. Identifying Interaction Classes in CLASH Data In the seminal CLASH paper [29] miRNA:target site interactions were clustered into interaction classes based on a per-nucleotide score derived from “cofold” analysis. Five classes with different binding profiles were produced, using k-means clustering ($k = 5$). Three of these classes (I–III) featured binding between the miRNA seed region and the target but differed in the presence and positioning of additional base-paired nucleotides within the miRNA. In class IV, binding was limited to a region located in the middle and 3′ end of the miRNA, denoting non-seed interactions. Class V showed distributed or less stable base pairing without either strong seed or 3′ binding. We have used the attribution scores produced by our method to reevaluate the rules of Ago1:miRNA:target binding learned by miRBind from the CLASH dataset. We calculated attribution scores for all CLASH interactions, based on the miRBind model, and then used k-means clustering ($k = 5$) to reveal five classes of interactions with distinct base-pairing patterns (Figure 3). Class I corresponded to the classical seed binding, while class II represented more relaxed seed binding. Classes III and IV showed binding in the middle and 3′ end of the miRNA, respectively, while class V showed a distributed base pairing pattern. CLASH interactions were almost uniformly distributed among classes, with 4641 in class I, 4050 in class II, 3403 in class III, 3263 in class IV, and 3156 in class V. ## 3.4. Attribution Scores Narrow down Binding Site Location Prediction Target site prediction models such as miRBind are able to score miRNA:target site interactions of specific short lengths. However, the application of such methods on miRNA:target gene prediction is predicated on the ability to “scan” whole transcripts or other long RNA sequences. Our method can be used to make such “scanning” more precise, by narrowing down the binding site location. As a proof of concept, we produced artificial RNA sequences of various lengths, with two perfect 10 nt miRNA seeds positioned at various distances between them. As a baseline, we used miRBind to “scan” the sequence using a moving window technique (see Methods for details). We also used our method to calculate attribution scores per nucleotide for the same sequences. Figure 4 shows the prediction made using each of the methods, along with the ground truth. The peaks produced by using miRBind scores are indeed covering the seed areas, but they are much wider than the actual binding sites. The peaks are not centered around the seeds and neither are the local maxima corresponding to the seed areas. In contrast, the peaks produced by using the attribution score point directly to, and are more tightly distributed around, the seed area. Furthermore, the attribution score method can be even used to distinguish binding sites placed very closely together, for which miRBind model scores would produce only a single wide peak (Figure 5). We compared these two models on a dataset in which seeds were placed at the exact distances, from 15 nt to 50 nt apart. The attribution score model distinguishes the peaks even when the distance becomes as short as 15 nt (Figure S2). To verify the results on more realistic data, we produced a sequence constructed from positive and negative binding sites of a specific miRNA derived from CLASH data. Again, the miRBind model’s output scores are able to roughly point to the positions of positive binding sites, but these peaks are wide, spanning more than 50 nt. When we computed the attribution score and the attribution sequence alignment, we were able to point to the exact position of miRNA binding. Moreover, we obtained the importance score for each position in the binding site and visualization of the interaction between miRNA and the binding site in the form of a sequence alignment (Figure 6). ## 3.5. Versatility of the Method All previous results were produced using the miRBind trained model and the DeepExplainer interpretation method. However, our method is not tied to a specific model or interpretation method. To demonstrate this versatility, we used a different model (CNN_model_1_20_optimized) and a different interpretation method (GradientExplainer). We computed position importance scores for 300 miRNA:binding site pairs using different combinations of methods as inputs. DeepExplainer could not work with the CNN model, due to an implementation problem in its code. This highlights the importance of having a versatile method that can use different DL models, and interpretation methods. Table 2 shows that position importance scores computed by attribution sequence alignment method using different models and interpretation techniques positively correlate. A visual comparison of position importance scores for one sample is show in Supplementary Figure S3. Corresponding visualizations in the form of sequence alignments are shown in Supplementary Figure S4. ## 4. Discussion Computational models, especially deep learning models, have become the state of the art in the classification of miRNA:target pairs. It is becoming increasingly important to be able to understand the reasoning behind their predictions. The use of a 2D-binding representation to encode interactions between two sequences has been a crucial innovation in miRNA:target prediction. Interpretation techniques can use this 2D-binding representation to produce maps of areas within the input that contribute positively or negatively to a model’s decision. However, it can be challenging to identify important biological features within this type of representation. The difficulty of interpretation of DL models compared to simpler co-fold or seed-based models may hold back their adoption as the state of the art in the miRNA target site prediction field, despite their superior predictive performance. For a DL model to be able to advance biological knowledge, a biologically relevant representation similar to sequence alignment is necessary. This type of representation is familiar to biologists, and easily human-readable, and can be used to condense the DL model’s focus into a small number of parameters. In this paper we introduce a novel interpretation technique called attribution sequence alignment which combines the principles of dynamic programming for semi-global sequence alignment with attribution scores obtained from interpreting a neural network trained on a 2D-binding representation. This method allows us to evaluate the importance of each individual nucleotide on a miRNA binding site, providing a biologically relevant representation that can be visualized as a sequence alignment. Using this method, we can interpret DL models trained on miRNA:target site interaction. Our results correlate with in vivo experimental results and reveal interesting trends, such as the lower importance of 3’ nucleotides compared to the seed area and the low importance of the first nucleotide. However, it should be noted that these scores are specific to the model used and may vary with different models. Attribution sequence alignment scores can be a useful tool for understanding and evaluating the performance of a model, but they should not be considered a validation of the model itself. Further in vivo experimental results from systematically mutating miRNA target sites would be useful to calibrate interpretation methods such as ours more thoroughly. The first step in any miRNA target prediction program is transcriptome-wide scanning for putative miRNA binding sites. These putative miRNA binding sites are further combined into a final prediction for each transcript. Using current miRNA:target site tools for transcriptome scanning are based on the DL giving a single score to a fixed size moving window (50 nt in the case of the miRBind model [28]) resulting in wide peaks. We demonstrate that attribution sequence alignment can be used for narrowing these peaks when scanning for binding sites by computing the miRNA:target site attribution sequence alignment and assigning per-nucleotide importance scores to a long sequence. Our method can provide target prediction programs with more specific and detailed information about each potential binding site, allowing it to leverage more information from the experimental data that has been encoded in the trained DL model. The attribution sequence alignment method can be applied to the field of miRNA binding site prediction, as demonstrated by the miRBind model. However, it is not limited to this specific model, interpretation technique, or field. It could potentially be used for any neural network that has been trained on a 2D-binding representation of sequences, and any interpretation technique that produces per-pixel attribution scores. Additionally, with some modifications, it can easily be extended to other domains where input sequences can be represented by a 2D interaction matrix, such as protein–protein or protein–DNA interactions. Importantly, attribution sequence alignment considers only the scores from the interpretation matrix, without imposing any additional constraints on the alignment. This allows for greater flexibility and adaptability in its use. ## 5. 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